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Concept Learning in Machine Learning | Gyan Setu

The human race is slowing down, and people want more ease in their lives. For this, we constructed machines capable of receiving directions and performing duties on our behalf. But what if computers can think for themselves and make their judgments. It sounds dreadful!!! This is truly happening in this current period of machines and technology. These miracles are being performed by large corporations such as Google and Facebook.   Have you ever noticed how, when uploading photos to Facebook, the computer analyses faces and statistics and suggests your friend's name for tagging, or how, when searching for flights on Google for a certain place, you start receiving emails for flight-related offers?   Most of us always wonder how machines can learn from data and predict the future based on the available information considering facts and scenarios. Today we live in an era where most of us are working globally on big data technologies with great efficiency and speed. But having a huge amount of data is not the complete solution and optimal use of data until we can find patterns out of it and use those patterns to predict future events and identify our interest-specific solutions. What is the definition of Learning? On the internet, there are various meanings of Learning. "The action or process of obtaining information or ability through studying, practicing, being instructed, or experiencing something," according to one of the most basic definitions. There are numerous categories of learning methods, just as there are various meanings of Learning.   We learn a great deal during our lives as humans. Some are based on our personal experience, while others are based on memorizing. We may split learning techniques into five categories based on this: Rote Learning (Memorizing): Memorizing things without understanding the underlying principles or rationale. Instructions (Passive Learning): Learning from a teacher or expert. Analogy (Experience): We may learn new things by applying what we've learned in the past. Inductive Learning (Experience): Formulating a generalized notion based on prior experience. Deductive Learning: Getting new information from old information.   Inductive Learning is the process of forming a generalized notion after viewing many examples of the concept. For instance, suppose a child is requested to write the solution 2*8=? They can memorize the answer using the rote learning approach or use inductive Learning to construct a notion to compute the outcomes using examples such as 2*1=2, 2*2=4, and so on. In this approach, the child will be able to answer questions of a similar nature utilizing the same notion.   Similarly, we may train our computer to learn from previous data and recognize whether an object is in a given category of interest. What is concept learning, and how does it work? Learning may be characterized as "the problem of exploring through a preset space of candidate hypothesis for the theory that best matches the training instances" in terms of machine learning, according to Tom Michell.   The acquisition of broad concepts from previous experiences accounts for a large portion of human Learning. Humans, for example, distinguish between various cars based on specific traits specified over a vast collection of attributes. This unique collection of characteristics distinguishes the subset of automobiles in the collection of vehicles. A concept is a collection of elements that distinguishes automobiles. Understanding the Concept: The set of instances, represented by X, is the list of elements over which the notion is defined. The target idea, represented by c, is the notion of action to be learned. It's a boolean-valued function that's defined over X and may be expressed as: c: X -> {0, 1}   So, when we have a subset of the training with certain attributes of the target concept c, the learner's issue is to estimate c from the training data.   The letter H stands for the collection of all conceivable hypotheses that a learner could explore while determining the identification of the target idea.   A learner's objective is to create a hypothesis h that can identify all of the objects in X in such a way that: h(x) = c(x) for all x in X   In this sense, there are three things that an algorithm that enables concept learning must have: 1. Details about the training (Past experiences to train our models) 2. Target Conception (Hypothesis to identify data objects) 3. Data objects themselves (For testing the models) The hypothesis of Inductive Learning: The ultimate aim of concept learning, as previously stated, is to find a hypothesis 'h' that is identical to the target notion c over data set X, with the only knowledge about c being its value over X. As a result, our algorithm can ensure that it matches the training data the best. "Any hypothesis that approximates the target value well over a suitably large collection of training instances will likewise approximate the target value well over other unseen cases," to put it another way.   Consider whether a person goes to the movies or not based on four binary characteristics with two potential values (true or false):   1. Is Rich -> true, false 2. Is There Any Free Time -> true or false 3. It's a Holiday -> true or false 4. Has work pending -> true, false   We also have training data, with two data items serving as positive samples and one serving as a negative sample: x1: <true, true, false, false> : +ve x2: <true, false, false, true> : +ve x3: <true, false, false, true> : -ve Notations for Hypotheses: Each data object represents a notion and its associated hypotheses because it can only cover one sample, a hypothesis of <true, true, false, false>. We may add some notations to this hypothesis to make it a more general idea. We have the following notations for this task: ? (Represents a hypothesis that rejects all.) < ? , ? , ? , ? > (Accepts all) <true, false, ? , ? > (Accepts some) The hypothesis will reject all data samples. Conversely, all data samples will be accepted by the hypothesis  <?, ?, ?, ? > The '?' indicates that the values of this specific attribute have no bearing on the outcome.   In this fashion, the overall number of possible hypotheses is: (3 * 3 * 3 * 3) + 1, where 3 represents the fact that each character might be true, false, or '?', and one hypothesis rejects all ().   From the broadest to the most specific ranking of hypotheses:   Many machine learning methods rely on the idea of hypothesis ordering from broad to particular. h1 = < true, true, ?, ? > h2 = < true, ? , ? , ? >   Any occurrence classed as h1 will be categorized as h2 as well. As a result, we might conclude that h2 is more generic than h1. Using this notion, we can find a broad assumption that can be specified for the complete data set X.   The Find-S Algorithm is used to find the most specific hypotheses.   Any occurrence classed as h1 will be categorized as h2 as well. As a result, we might conclude that h2 is more generic than h1. Using this notion, we can find a broad hypothesis that can be specified for the complete data set X.   The idea of more-general-then partial ordering can be used to identify a single hypothesis specified on X. Starting with the most particular hypothesis from H and generalizing it each time it fails to categorize a behavior modification data object as positive is one technique to achieve this.   Step 1: In the Find-S method, the first step is to start with the most specific hypothesis, which is represented by: h <- <?, ?, ?, ?>   Step 2 entails selecting the next training sample and doing step 3 on it.   Step 3: It entails viewing the data sample. If the sample is negative, the hypothesis remains unaltered, and we go to step 2 to select the next training sample; otherwise, we proceed to step 4.   Step 4: If the sample is positive and we discover that our initial hypothesis is too specific and does not apply to the current training sample, we must alter our current hypothesis. This may be accomplished using the pairwise combination (logical With operation) of the present and alternative hypotheses.   We may immediately replace our existing hypothesis with the new one if the next training sample is < true, true, false, false> and the current hypothesis is <?, ?, ?, ?>.   Suppose the next positive training sample is true, true, false, true> and the current hypothesis is true, true, false, false>. In that case, we can use a bilateral conjunctive AND with the present hypothesis and the next training sample to find a new hypothesis by inserting a '?' where the conjunction result is false: <true, true, false, true> ? <true, true, false, false> = <true, true, false, ?>   We may now replace our old hypothesis with a new one: h <-<true, true, false, ?>   Step 5: Repeating steps 2 and 3 until we have enough training data.   Step 6: If no training samples are available, the present hypothesis is the one we were looking for. The final hypothesis can be used to categorize real-world items.   Step 1. Start with h = ? Step 2. Use next input {x, c(x)} Step 3. If c(x) = 0, go to step 2 Step 4. h <- h ? x (Pairwise AND) Step 5. If more examples : Go to step 2 Step 6. Stop   The Find-S algorithm has certain limitations: The Find-S method for idea learning is among the most fundamental machine learning algorithms. However, it has certain limitations and drawbacks. The following are a few of them:   There is no way to tell if the lone final hypothesis (discovered by Find-S) is compatible with the data or if there are other hypotheses. Because the discovers algorithm excludes negative data samples, inconsistent sets of training images might mislead it. Thus an algorithm that can identify inconsistency in training data would be preferable. An excellent concept The learning algorithm will be able to reverse the hypothesis selection process so that the final hypothesis can be improved over the years. Unfortunately, Find-S does not have such a feature.   Data Science, Artificial Intelligence and Machine Learning, and Programming in various languages are taught in many coaching centers in Delhi.  Some of them are mentioned below: SIIT- Computer Institut in India Gyan Setu, Delhi AnalytixLabs Noida Institute JEETECH Academy IICS (INDIAN INSTITUTE OF COMPUTER SCIENCE)   Many constraints may be overcome using the Candidate Elimination Algorithm, one of the most significant idea learning algorithms. Frequently asked questions? 1.What are hypothesis and concept learning? Learning a generic category's description using positive and negative training examples is known as concept learning. Finding the hypothesis that best matches the training examples requires searching through a preset space of candidate hypotheses.   2. What does machine learning's target notion mean? In machine learning, a target function is a solution to a problem that an AI algorithm finds by analyzing its training data. Once an algorithm locates its target function, it may use it to forecast outcomes (predictive analysis).   3. What is learning supervised and unsupervised learning? Unsupervised learning algorithm does not employ labelled input and output data. Supervised learning does. When using supervised learning, the algorithm iteratively predicts the data and modifies for the proper response to "learn" from the training dataset.   4. What distinguishes classification from clustering? Although there are some similarities between the two procedures, clustering discovers commonalities between items, which it groups according to those in common and which separate them from others, whereas classification employs predetermined classes to which objects are given.   5. What does machine learning regression mean? Regression is a method for determining how independent traits or variables relate to a dependent feature or result. It is a technique for machine learning predictive modeling, where an algorithm is used to forecast continuous outcomes.  

Advance Excel Course Syllabus and Structure you should consider

Many occupations now call for excellent Excel abilities. So you naturally want to know what these Advanced Excel Skills are. According to the teachers at Gyan Setu, the following nine categories make up the foundation of advanced Excel abilities. In addition, they have experience teaching more than 1 million students in various physical and online training programs.   Excel has become intelligent thanks to advanced formulas. Excel would only be a tool for data storage without them. However, by employing formulae, you can crunch data, analyze it, and find solutions to even the most difficult queries. A skilled user of it would easily construct and combine formulae like SUMIFS, SUMPRODUCT, INDEX, MATCH, and LOOKUP, even if everyone can use a simple SUM or IF formula.   Advanced Excel users not only know the formulae but also how to audit them, debug them, and which formula to employ when (and they also know few alternatives for any given formula problem).   Power Query, Data, Tables, and Formatting Advanced Excel users understand how to collect, organize, and compellingly display their data. To create amazing Excel workbooks, it's important to have a solid grasp of capabilities like Power Query (Get & Transform Data), Tables, cell styles, and formatting choices. Conditional Formatting Excel has a strong feature called conditional formatting that is frequently underused. By using conditional formatting, you may instruct Excel to highlight sections of your data that satisfy any particular criterion, emphasizing the top 10 clients, underperforming workers, etc. Anyone can create basic conditional formatting rules, but a skilled Excel user is capable of much more. They may highlight data that satisfy practically any criterion by combining mathematics and conditional formatting. Complex Charting If all of your analysis is contained in a sizable spreadsheet, it is of little Use. Excel experts are aware that charts help us communicate clearly and impressively display results. The knowledge necessary for advanced charting includes, Being able to choose the appropriate type of chart for every occasion Being able to integrate many charts into one Utilize tools like conditional formatting charts and in-cell charts. Being able to create dynamic and interactive charts Employ sparkplugs Pivot Reporting & Pivot Tables We can quickly and easily evaluate vast volumes of data with the help of pivot tables and reporting. Advanced Excel users are well familiar with the different capabilities of pivot tables and are skilled at using them. Relationships, multiple-table pivots, grouping, slicers, measures (Power Pivot), and summary by various metrics are a few of the more advanced pivot table capabilities. Advanced Excel Syllabus Introduction to Excel A description of the interface, the menu system, and the fundamentals of spreadsheets Various methods of selecting Short Cut Keys   Personalising Excel Changing Excel's Default Options Using AutoCorrect and Customizing It Customizing the Ribbon   Understanding and Using Basic Functions Using Functions – Sum, Average, Max, Min, Count, Counta Absolute, Mixed, and Relative Referencing   Text Functions Upper, Lower, Proper Left, Mid, Right Trim, Len, Exact Concatenate Find, Substitute   Arithmetic Functions SumIf, SumIfs CountIf, CountIfs AverageIf, AverageIfs   Proofing and Formatting Formatting Cells with Number formats, Font formats, Alignment, Borders, etc Basic conditional formatting   Protecting Excel- Excel Security  File Level Protection Workbook, Worksheet Protection   Printing Workbooks Setting Up Print Area Customizing Headers & Footers Designing the structure of a template Print Titles –Repeat Rows / Columns   Advance Paste Special Techniques Paste Formulas, Paste Formats Transpose Tables Paste Validations   Time and Date Functions Today, Now Date, Date if, DateAdd Day, Month, Year Month, Weekday   New in Excel 2013 / 2016 & 365 New Charts – Tree map & Waterfall Combo Charts – Secondary Axis Sunburst, Box, and whisker Charts Using Power Map and Power View Adding Slicers Tool in Pivot & Tables Sparklines -Line, Column & Win/ Loss Forecast Sheet Smart Lookup and manage Store New Controls in Pivot Table – Field, Items, and Sets Using 3-D Map Auto complete a data range and list Various Time Lines in Pivot Table Quick Analysis Tool   Filtering and Sorting Filtering on Text, Numbers & Colors Sorting Options Advanced Filters on 15-20 different criteria(s)   Printing Workbooks Setting Up Print Area Print Titles –Repeat Rows / Columns Designing the structure of a template Customizing Headers & Footers   Advance Excel   What-If Analysis Goal Seek Data Tables (PMT Function) Solver Tool Scenario Analysis   Data Validation Number, Date & Time Validation Dynamic Dropdown List Creation using Data Validation – Dependency List Custom validations based on a formula for a cell Text and List Validation   Logical Analysis If Function Complex if and or functions Nested If How to Fix Errors – iferror   Lookup Functions Vlookup / HLookup Vlookup with Helper Columns Creating Smooth User Interface Using Lookup Index and Match Reverse Lookup using Choose Function Nested VLookup Worksheet linking using Indirect   Arrays Functions What are the Array Formulas, Use of the Array Formulas? Array with if, len, and mid functions formulas. Basic Examples of Arrays (Using ctrl+shift+enter). Advanced Use of formulas with Array. Array with Lookup functions. Here are the Uses of Microsoft Excel in Daily Life  Pivot Tables Creating Simple Pivot Tables Classic Pivot table Basic and Advanced Value Field Setting Calculated Field & Calculated Items Grouping based on numbers and Dates   Excel Dashboard Planning a Dashboard Adding Dynamic Contents to Dashboard Adding Tables and Charts to Dashboard   Slicers and Charts Using SLICERS, Filter data with Slicers Various Charts i.e. Bar Charts / Pie Charts / Line Charts Manage Primary and Secondary Axis   VBA Macro   Introduction to VBA What Is VBA? Procedure and functions in VBA Recording a Macro What Can You Do with VBA?   Variables in VBA What are Variables? Using Non-Declared Variables Using Const variables Variable Data Types   Inputbox and Message Box Functions Customizing Msgboxes and Inputbox Reading Cell Values into Messages Various Button Groups in VBA   If and select statements Simple If Statements Defining select case statements The Elseif Statements   Looping in VBA Introduction to Loops and its Types Exiting from a Loop Advanced Loop Examples The Basic Dos and For Loop   Worksheet / Workbook Operations Merge Worksheets using Macro Split worksheets using VBA filters Worksheet copiers Merge multiple excel files into one-sheet   Mail Functions – VBA Using Outlook Namespace Outlook Configurations, MAPI Send automated mail Develop Your Excel Skills You must be well-versed in all of the aforementioned topics and more in order to utilize Excel at an advanced level. Our training packages are helpful in this situation. Over 10,000 individuals have been instructed by Gyan Setu tutors to become proficient Excel users. You may also take this class to learn how to use Excel at a high level. Frequently Asked Questions? 1. What distinguishes Excel from Advanced Excel? Picture of advanced excel The user focuses more on DSUM, DCOUNT, Pivot Table, Pivot Chart, Formulas, Functions, and Macros in Advanced Excel than in Basic Excel. Other crucial ideas to investigate when working with Advanced Excel include: If declarations. Total Products   2. Is Excel Advanced difficult? Excel might be difficult to grasp if you're a newbie and have no prior expertise with data or spreadsheets. But mastering the fundamentals is simple and only takes a short while, especially if you get some assistance from online classes.   3. What are the fundamental Excel skills? Basic Excel User Skills Count or Sum cells according to one or more criteria. To summarise the data, construct a pivot table. Incorporate both absolute and relative references in your formula. Make a drop-down menu of choices in a cell to simplify data entering. Sort a list of numbers or text without affecting the data.   4. The value of Excel certification A Microsoft Excel certification will help you stand out on the job market and prove to hire managers that you have the skills required for the position. In some cases, applying for the post can even require you to have an Excel certification.   5. How quickly can I learn Excel? Learning Excel doesn't take weeks, months, or years to learn it. In reality, you can pick up most of Excel's essential features in a single day. That is, provided you take a quality Excel course from a qualified instructor.  

How to Learn Data Science From Scratch (Complete Guide)

The field of data science is relatively new to the commercial sector. However, the growth in data gathering and processing technologies over the last decade has created a once-in-a-generation opportunity to use the public's collective intelligence to display patterns, investigate correlations between factors, and forecast future market behavior and events.   As the globe moved into the era of big data, so did the demand for storage. Until 2010, it was the key problem and concern for the business industries. The major focus was on developing a framework and data storage solutions. Now that Hadoop and other systems have successfully handled the storage challenge, the attention has switched to data processing. The special recipe here is data science. Data Science can make all of the ideas you can see in Hollywood sci-fi movies a reality. Artificial Intelligence's future is Data Science. As a result, it's critical to comprehend what Data Science is and how it might benefit your company.   As a business professional, using the information to drive decision-making may set you apart. But where do you begin? How do you break into the field of data science, develop your abilities, and create change in your business if you don't have a background in it? Here's a primer on data science and six steps to getting started. WHAT IS DATA SCIENCE, AND HOW DOES IT WORK? Data science studies obtaining, processing, visualizing, analyzing data, and conveying the results.   Data scientists frequently utilize coding and machine-learning techniques in languages like R or Python to answer issues.   In the corporate world, data science abilities may help you acquire insights into your consumers while protecting their privacy, anticipate market trends, estimate financial movement, and use machine learning to expedite production operations.   Understanding data science and being data literate will assist you in making data-driven decisions and answering your company's most important business concerns. Here are six stages to learning data science from the bottom up if you're unsure where to begin.   Predictive causal analytics, prescriptive analytics (predictive + decision science), and machine learning are all utilized in Data Science to make judgments and predictions.   Predictive causal analytics: If you want a model that can forecast the chances of a specific event occurring in the future, predictive causal analytics is the way to go. For example, if you're lending money on credit, the likelihood of clients making future credit payments on schedule is a problem. Here, you may create a model that uses predictive analytics to forecast whether payments are on time or not based on the customer's payment history. Pattern discovery using machine learning: If you don't have the parameters on which to generate predictions, you'll need to locate underlying patterns inside the dataset to create meaningful predictions. Because there are no specified labels for grouping, this is the unsupervised model. Clustering is the most used pattern detection algorithm. Assume you work for a telephone business and need to build a network by erecting towers over an area. Then you may utilize the clustering approach to determine which tower sites will provide the best signal strength to all users. Prescriptive analytics: You'll need prescriptive analytics if you want a model with the intelligence to make its judgments and the capacity to alter it using dynamic parameters. It's all about giving guidance in this relatively young industry. Put another way, it not only forecasts but also offers a set of specified behaviors and results. The finest example is Google's self-driving automobile, which I previously described. Vehicles can collect data that can be used to teach self-driving automobiles. You can use algorithms to add intelligence to this Data. Your automobile will be able to make judgments such as when to turn, which course to take, and whether to slow down or accelerate up as a result. Machine learning for forecasting – If you have financial transaction data and need to develop a model to forecast future trends, machine learning algorithms are your best choice. This is part of the supervised learning paradigm. Since you already have the information on which to train your robots, it's termed supervised learning. A fraud detection model, for example, can be trained using a database of fraudulent purchases. How to Learn Data Science From Scratch Embrace the Obstacle The first step in learning data science is overcoming any mental hurdles preventing you from taking on the task, learning the subject, and developing data science abilities.   Data science isn't frightening, and it shouldn't be. On the contrary, data science combined with your business knowledge and intuition may help you and your organization succeed.   Even though data science seems to have an image of just being code-based and complicated, the principles are simple to grasp if you have the willingness and motivation to study and put in the effort.   Some people believe that they won't be able to compete unless they've been trained as just a data scientist and have years of coding experience. But that isn't the case. It's never too late to start. Begin with the Fundamentals After that, brush up on the principles of data science. Reading blog posts and articles, watching videos, conversing with people in the field, or completing a basic data subject like Harvard Online's Data Science Principles are good ways to get started. The objective is to build a solid foundation in data principles and best practices so that you may progress to more difficult issues as time goes on.   You may move on to the tools and frameworks needed to employ data science in your company once you have a good grip on core data science concepts like the data ecosystems and cycle of life, data integrity, information management and security, and data wrangling. Become acquainted with the tools and frameworks available. When employing data science at work, there are a variety of data science frameworks and tools to consider. One is the framework for data-driven decision-making.   Six phases are presented in this approach for leveraging data to influence business decisions: Recognize the business issue: What are you hoping to learn or accomplish? Data to wrangle: Data should be cleaned, validated, and organized. Make visual representations: Present your information in a way that highlights important patterns and linkages. Construct hypotheses: Make forecasts based on current events. Analyze the situation: Do statistical tests to see if your theories are right. Results should be communicated: Present your results with the original business challenge.   Gaining knowledge of applications and technologies that can assist you during the process is also beneficial. Excel and Power BI, for example, are both Microsoft statistical methods that enable you to organize, view, and analyze data. Other programs, such as Google Analytics and Tableau, may be used for further in-depth research and dashboard creation to show and track changes in your statistics.   Leveraging data frameworks allows you to take a raw dataset, comprehend the narrative it tells, and apply it to relevant business problems. Apply What You've Learned to Real-Life Situations Real-world examples may be a valuable resource while studying data science. You may picture what you'd do in their situation, analyze the effect of their decisions, and put that information into reality by looking at how other industry professionals utilize the data science to address challenges.   It's up to you to make it a reality. 'Why do I care about this?' or 'Why do I want to glance at a summary statistic?' are good questions to ask. or 'How will this be useful in making a certain decision?' The breadth of the variety allows us to put ourselves in the shoes of a decision-maker and learn how actual judgments are made by exposing ourselves to instances from diverse sectors.   Locate a Community A network of like-minded experts who share your objective of mastering data science may be a motivating and supporting force. Internet forums, social media, loyalty groups inside your organization or geographical region, or a cohort of students in an online class are all options.   Having a community enables you to solicit criticism and advice, collaborate on new ideas, and encourage one another as you work towards your objectives. Make a list of big questions to ask of your data. Finally, keep asking big questions about your data to broaden and enhance your knowledge. Each question is a fresh opportunity to learn more and build abilities. For example, you may need to learn new programming languages, analysis approaches, regression, or visualization tools to address a specific business question. Here are some questions to consider while working with data:   What exactly do I want to comprehend? What information do I require to make a specific business decision? What is the story that this Data is telling? What has to be changed in the data to get the intended result? What does it suggest for the future if the data continues to trend in this direction?   Determine what you need to understand and the best approach to address that issue utilizing accessible data to make the information work for you. Developing your data science abilities is a never-ending process, with each new encounter providing new opportunities to learn more.   Tutoring classes that offer Data Learning from scratch: Cambridge Infotech |Software Training Gyansetu Crystal Training Solutions TryCatch Classes 360DigiTMG - Data Science Course Training Centre   WHY CONSIDER DATA SCIENCE? Learning data science may be a great investment in your career and organization, regardless of your rank.   In today's corporate environment, we all have a responsibility. "Data science is a collaborative effort involving all of us.   You can communicate and drive significant, data-backed choices within your business if you have data basics, resources, structures, real-world examples, a huge community, difficult questions, and confidence.   THE FUTURE OF DATA SCIENCE Every day, 2.5 quadrillion bytes of data are created. Data science is essential for every company that generates big amounts of data. The gasoline that drives the global economy's engine is digital data. Data science and technologies such as artificial intelligence, deep learning, machine learning, and others are among the hottest new technology.   Statistics paved the way for data science. Since the early 1800s, simple statistical methods have been used to gather, analyze, and manage data. When computers were more widely used, the digital age began with the creation of massive volumes of data. Statistical procedures and models were automated to manage such vast data.   Following the digital age came the Internet era, which resulted in an enormous amount of data, resulting in Big Data. The need to handle and manage enormous data necessitated knowledge, which led to data science. Businesses use data science to process, acquire, analyze, visualize, and turn data into information to make business choices.   With text and a human outline vector, an AI map network billboard is created. The intelligent mind of a robot or cyborg. A brain that resembles a human mind As more individuals get linked to their mobile devices, massive amounts of data are generated daily. IoT, AI big data analytics, cryptocurrency, and quantum computing are just a few technologies that will see significant growth in the future. Growth of Disruptive Technologies Aviation, e-commerce, mining, automobile, telecom, and other industries will be the biggest contributors to this Data. In addition, the acquired data will be more affluent and diverse as the quality of device technology improves. Data-driven insights will alter organizations, allowing them to attract new consumers, explore new growth opportunities, increase revenue, and much more. The forefront of Artificial Intelligence and Machine Learning Artificial intelligence, machine learning, and machine learning would be at the cutting edge of technology. There will be a great need for competent personnel who can handle these technologies. Data scientists must create and train machine learning modules as part of their crucial tasks.   Pre-trained AI models successfully deliver the ML experience while reducing training time and effort. Moreover, these simulations can even provide crucial information right away.   AI and machine learning are already commonplace in daily life, and their capabilities are improving with each new day. Artificial intelligence, machine learning, and deep machine - learning have begun to gain skills and improve their performance without human interaction.   Frequently asked questions ? 1. Can I independently learn data science? You can become a data scientist even if you have no formal training or work experience. What matters most is the ability to learn new things and be motivated to find solutions. And it would be much better if you could find a mentor or community to support and guide your learning.   2. Is it simple to start from scratch learning data science? Data Science continues to be this enigmatic area that captures people's attention but is still seen by many as exceedingly difficult or even impossible to master from the start.   3. How can I begin studying data science from scratch? How to Learn Data Science in 6 Easy Steps Accept the Challenge Start with the Basics Become Familiar with Tools and Frameworks Learn From Real-World Examples, and  Embrace the Challenge Ask Big Questions   4. Is working as a data scientist stressful? To put it plainly, data analysis is a challenging undertaking. The enormous amount of work, deadline restrictions, and job demand from several sources and levels of management, among other things, make a data scientist's job difficult.   5. Can someone without a background in math study data science? Mathematical knowledge is necessary for data science careers because machine learning algorithms, data analysis, and insight discovery depend on it. Although there are other requirements for your degree and employment in data science, math is frequently one of the most crucial.  

Future Scope of Python in India

Developers have access to a dizzying array of programming languages, making it difficult to concentrate on just one. The future scope of Python in India is one of the most important concerns for programmers. IT behemoths like Google use this fluid programming language, IBM, Netflix, and many more to create accurate and unambiguous programs. It also holds domains worldwide and provides aspiring and seasoned developers with lucrative work prospects. Continue reading to learn more about Python's potential employment opportunities in India and how Python from GyanSetu training might benefit you. What Different Python Career Possibilities Are There in India? Python is becoming increasingly popular in India since it supports various programs and enables developers to create interactive apps. However, to create a great career in this subject, it is advisable to learn about the prevalent job descriptions in the sector.   Let's look at the best job paths you may take to become a proficient Python developer. Python Programmer's Roles and Responsibilities But first, let's look at the roles and responsibilities of a Python Programmer. Let's talk about some of the main duties and jobs performed by professionals in this industry. Collaborating with development teams to determine the needs of the application. Utilizing the Python programming language to create scalable programs. Check for bugs and debug software. Making advantage of server-side logic, creating back-end components, and integrating user-facing components. Evaluating and ranking client feature requests Work together with front-end programmers. Upgrading the functionality of current databases. Create internet traffic monitoring software.   Data Scientist  The subject of data science is rapidly expanding and needs highly qualified personnel. According to Glassdoor, the third-most coveted occupation in America is in data science. Additionally, the demand for data scientists is rising steadily in India. Additionally, these experts process, model, and analyze data before interpreting the outcomes to develop workable plans for businesses and other organizations. Explore our Data Science course here Software Engineer A DevOps engineer adds methods, tools, and approaches to balance demands across the software development process, from coding and installation to maintenance and upgrades. The compensation of DevOps Engineers reflects the career of such individuals in this very promising field, which offers development.   Senior Software Engineer Senior software engineers must have five or more years of expertise and be fluent in any current language, such as Python. Software engineering is a highly strong career choice based on every metric, including pay, the number of vacant positions, and overall job satisfaction. Software developers also have a fantastic opportunity to advance their careers and increase their income. The number of factors that affect a software engineer's overall salary is quite large.   Program Engineer A software engineer is another term for a coder. These experts create, develop, test, and review computer software using software engineering concepts. Software experts have been in high demand in recent years. Additionally, according to data from the U.S. Bureau of Labor Statistics, jobs in software development are expected to grow by 22% between 2020 and 2030.   Python Programmer The person in charge of writing, developing, delivering, and troubleshooting development projects, usually on the server-side, is a Python Developer. This is because they support organizations' technology infrastructure as well. As a result, the duties of a Python developer might encompass a wide range of activities. Additionally, the growing need for Python engineers across several sectors makes being a Python developer a wise career decision.   Networking And Artificial Intelligence Analyst The opportunities for Python programmers in networking and AI are vast. You can explore career options in this industry and work as a network engineer or an AI analyst by studying the more complex principles from Python programming classes.   Python Development Trends of 2022   Data Science:  Data Science is a rapidly expanding field that heavily relies on Python. Statistics show that data science will be popular in the next ten years. Python is among the simplest when it comes to using tools to solve data science challenges. Python has indeed changed the game in the data sciences. Overall, Python is among the top options for different data scientists worldwide.   Machine Learning:  Python is an extremely versatile language, so developers working on machine learning projects choose to use it. As a result, machine learning is made fairly accessible with the aid of Python. Python's several specialized libraries are one of its finest features. These libraries are brimming with logical and mathematical processes when it comes to machine learning. Check Machine Learning with Python Training   Game Developer:  Nowadays, game developers use Python because of its adaptability and unique characteristics. They create computer games in Python. Many notable games, including The Sims, World of Tanks, Battlefield, and many more, were created using Python programming. This industry is extremely popular and expanding quickly. It is among the greatest fields to launch your career in.   Artificial Intelligence:  Often known as AI, is growing in popularity daily based on the present situation. Every company is competing to grow in AI and is developing projects in AI. A programming language that can make creating AI projects easier is required for this to happen. As a result of its unique capabilities, Python is the programming language that these experts utilize the most. Scientists are using AI technology to tackle problems in real-time, which helps them work more effectively and faster. Python is hence a recommended choice for developing AI.   Web development:  Python is used by web developers for various purposes in different sectors. Python is a computer language used to build websites, and since Google, Facebook, and Microsoft all use it, your website will be able to compete with theirs. Additionally, Python web development can carry out several tasks, from creating a website to administering cloud infrastructures.   Cloud Computing:  Python is heavily dependent on the C programming language and is highly helpful in writing embedded C code for embedded applications. Python-based high-level programs that are calculated may be run on tiny devices. In addition, the Raspberry Pi is a well-known embedded program that uses Python for cloud computing initiatives. It may also be used as a computer or a basic integrated board to carry out sophisticated computations.   Business Applications:  ERP, e-commerce, and other areas are covered by business applications. Businesses want apps that are readable, extendable, and scalable. And Python has the power to offer everything. To create a business application, there are systems like Tryton accessible.   Why Is Programming in Python So Popular? Python's wonderful characteristic is what makes it so popular. Python's distinctive characteristics give programmers, web designers, software engineers, etc., several benefits. So let's peek at some of Python's most well-liked features. Python is a multi-paradigm programming language with features including imperative, procedural, object-oriented, functional, reflective, etc. Possess a comprehensive collection of built-in libraries and tools to improve the language's capabilities. A sizable community supports Python. Python was created to have more readable code than other languages. In comparison to other programming languages, it has fewer lines of code. As a result, it is simple to comprehend. Competencies of a Python Programmer It can seem impossible to become an expert in Python. But it's a process that demands acquiring sophisticated ideas and honing a skill set. The following are the key competencies you must build to become a good Python developer:   Proficiency with Core Python. A solid grasp of foundational ideas is beneficial for anything from comprehending data types to handling exceptions.   Knowledge of the Python framework and libraries One of its main benefits is that Python has the most libraries available. However, you must be an expert on them to work as a Python developer. You also need to be familiar with the Python environment and frameworks.   Along with these two fundamental skills, a good python programmer needs the skills mentioned below: Relational object mapping software. How to go into data science. AI and machine learning. Profound learning Multi-process architecture expertise Abilities in analysis. Future for Python in India India, one of the world's fastest-growing economies, is embracing big data, machine learning, and computer science rapidly. According to a recent survey, India's analytical business is expanding at an astounding pace of 35.1%. In India, Python is a popular technology due to its adaptability. Additionally, because of its straightforward syntax, it has gained significant use in the analytics sector. As a result, python coders may find career possibilities across numerous industrial sectors, from healthcare to retail. Python's acceptance will grow more widely in India as AI and machine learning progress there. Future for Python Programming Python is a programming language used extensively for system and application development. Python is being used by major businesses and search engine juggernauts to simplify their tasks. For example, python programming is operated by Google, Yahoo, Quora, and Facebook to address their challenging programming issues. As a result, you should get ready as soon as possible and develop your thoughts in Python.    You may enroll in the Gyan Setu Python programming classes, which will assist you in developing your Python foundation. Additionally, this studying Python Programming at Gyan Setu enhances your knowledge of some topics, including data structures, object-oriented Python, and working with XML, files, and modules, among others. Frequently asked questions 1. Does Python have scope in India? Naturally, Python has a wide range of applications. Since Python is the fastest-growing programming language in these nations, pay for Python developers is high in India, the US, and the UK.   2. Is Python good for placement in India? Yes, that will be beneficial. It will be fine if you decide to use Python as your primary language. Data structures and several well-known algorithms come up in interview questions, and programming them in Python is much simpler than doing so in other languages like Java or C++. Additionally, since you have to write less code, you will have more time to interact with the interviewer and explain the solution. Additionally, it is widely used by many businesses nowadays, including those that sell products and even provide services. Therefore, Python appears to be the best option to stand out in the placements due to the rapid growth of AI. Move forward with it!   3. Is Python developer a promising future profession? Python is not only one of the most widely used programming languages in the world, but it also has some of the most promising job prospects. The need for Python programmers is growing yearly. This high-level programming language is well-liked for a reason.   4. Can I obtain a job with Python? Python has revolutionized the sector with its numerous applications, sophisticated libraries, and high productivity. Python coders are in high demand, and the jobs pay well. And because Python is simple, many people opt to pursue a rewarding career in the language.  

Job Profiles & Responsibilities of Data Scientist at Microsoft, Google, Amazon

Data scientists are in huge demand for the skill they bring to the table. Businesses, medium or large scale expect to grow bigger with the insights provided by them. Analyzing the business from every angle progressively becomes possible with the help of a data scientist. So What is a Data Scientist Job about? Data Scientist Responsibilities They have to work hand in hand with stakeholders for understanding their objectives and how data can help in achieving them. The Data Scientist Job requirements include the following 1. Gathering data and prompting the proper questions for further data cleaning and processing. 2. Conducting data investigation and exploratory analysis after storing and integrating data. 3. Implementing techniques including machine learning, artificial intelligence and statistical modeling, after selecting potential algorithms and models. 4. Showcasing the final outcome after improvising and measuring the results, doing necessary adjustments if required and repeating the procedure. Different Job Profiles in Data Science These are some common Career paths, which includes the data scientist role 1. Data Architect: responsible for creating, designing and managing the data architecture of an organization. 2. Data Analyst: maneuvering massive data sets for recognizing trends and making significant conclusions for insights and business-related decisions. 3. Business Intelligence Expert: gathering patterns from the data. 4. Data Scientist: performs data modeling for creating predictive modeling and algorithms, also deals with customized analysis. 5. Data Engineer: they deal in organizing, cleaning and aggregating data from diverse sources, further shifting them to data warehouse. Data Scientist at Microsoft Microsoft has a sub-department named applied sciences and data that is classified under engineering. Teams are divided based on the major titles which include machine learning engineer, data scientist and applied scientist. These are some general functions: 1. Writing code to be implemented by data scientists for machine learning algorithms. Also, for forwarding the models towards production. 2. Dealing with experimentation, product features, metrics, customers (direct or indirect), technical issues. Data scientist jobs at Microsoft are team-based, for instance, one team is dedicated to machine learning while the other deals with analytics. Data Scientist required skills at Microsoft General requirements are Bachelor/ Master Degree in a quantitative field. For a mid-level role, a 2-year experience is preferred. 1. Prior experience in reinforcement Learning, casual inference, DNN, time series, network Analysis, NLP or in other relative fields. 2. Substantial experience in Azure or AWS, the cloud-based architecture. 3. Proficiency in R, Python, SQL, NumPy, Spark, SciPy or C# or similar numerical programming language. Data Scientist at Amazon Like any other global firm, Amazon has departments set up for everything. Data scientists join a specific team, but regardless of that, they all have some similarities. It includes a background in Statistics, programming, analytics, mathematics, computer science, and scripting languages like Java, Python etc. They also possess a thorough understanding of artificial intelligence and machine learning algorithms. Some specializations available include: 1. Amazon Web services: the data scientist assists AWS customers by creating ML models and tending their business requirements. 2. Alexa: here the data scientist is expected to be proficient in natural language processing plus information retrieving. This is needed for training the AI to comprehend the commands in several languages. 3. Demand forecasting: the data scientist allotted here has to develop algorithms that comprise learning from huge data like product attributes, prices, similar products, promotions for predicting the demand of millions of products on Amazon. They are expected to collaborate with information architects, marketers, data engineers, designers, software developers. Levels of Data Scientist at Amazon 1. Entry-level: This position is often held by those who are still studying or are there for internships. They need proficiency in one language PHP, Java or Python plus some working knowledge of SQL. They should be adept at dealing with analytical problems via a quantitative approach. 2. Senior-level: Apart from management roles, this level requires degrees in Statistics, engineering, computer science, economics, mathematics. For a specialized role, expertise in computer vision and natural language processing can be expected along with work experience in analytics. Data Scientist at Google The data scientist has to be either product-oriented or analysis-oriented. Product Analyst They usually have domain and other specialized knowledge. They work on: 1. Consumer's different choices and sentiments 2. Product Popularity and failure reasons 3. Target market statistics 4. Datasets (external and internal) Quantitative Analyst They generally have degrees in mathematics, quantitative study, and Statistics. They work on: 1. Product research 2. Forecasting customer lifetime value 3. A/B experiments 4. Modifying search algorithms 5. Estimating future internet reach in different countries 6. Statistical modeling Data Scientist Responsibilities at Google 1. Inspecting and improvising the products. Collaborating with engineers and analysts. Working on big data sets 2. Conducting requirements specifications, ongoing deliverables, data gathering, processing, presentations and analysis 3. Implementing optimization and forecasting, R&D Analysis. Doing cost-benefit recommendation, communicating inter-functionally 4. Conducting presentation findings with experimental analysis, displaying quantitative information related to stakeholders. 5. Understanding metrics and data structures, recommending necessary product development changes. 6. Prototyping Analysis pipelines and building iteratively for insights. Data Scientist Salaries These are the mean salaries in different firms for data scientists. 1. The average Data Scientist Microsoft salary is around 25 lakhs + Stocks annually 2. The average Data Scientist Amazon salary is around 23 lakhs + Stocks yearly 3. The average Data Scientist Google salary is around 24 lakhs + Stocks for a year   Want One-On-One session with Instructor to clear doubts and help you clear Interviews? Contact Us on +91-9999201478 or fill the Enquiry Form Data Science Instructor Profile Check Machine Learning Course Content

Machine Learning Project Ideas for 2022

Machine learning is expected to take over large-scale production in almost every field as it’s constantly evolving. There are hundreds of machine learning projects suggestions which when implemented can save a ton of time via automation. A practical machine learning project can open the doors to newer horizons and improve productivity. In the recent past, massive breakthroughs have resulted in the realm of technology and with these machine learning project ideas, it’s going to make businesses smoother and operations optimal. Online Fake Logo Detection This idea is great as it helps in 1. Assisting customers with product verification before making a purchase, thus preventing them from being swindled. 2. The design is user-friendly allows normal people to utilize it. 3. Piracy and Logo copycats can cause confusion among customers, this system will give the firms control over forgeries. However, incorrect input can yield wrong output. Plagiarism Checker Copied content is a common problem, which makes this project worth it. A detector can be built this way. 1. Loading plagiarized data corpus. Exploring data distribution and existing features. Further, preprocessing and cleaning the data. 2. Defining and extracting features for similarity comparison of answer and source text. Analyzing correlation, selecting features and creating .csv files. 3. Uploading test feature to S3, defining training script plus binary classification model. Train & deploy model via SageMaker then evaluate. Uber Pickup Analysis This project can help in identifying patterns as to which hour is the busiest, or the maximum trips/pickups. This can be done as follows 1. Import the dataset and libraries. Categorize into hours and days. The number of pickups should likely increase on weekdays. 2. The hourly data should show fewer pickups from midnight to 6 am, from then increases, making 6 in the evening a peak hour. 3. The data would show Saturday with the least pickups, Sunday, substantially more for leisure, and Monday for the most work-related pickups. Stock Price Prediction This project will allow determining the near future value that is held by the stock. Estimation can be done using ml algorithms and long short-term memory (LSTM) 1. Import libraries and start by visualizing the stock data, Print DataFrame shape to detect null values. 2. Select features & set target variables. Make test & training sets, process data for LSTM then build & train the model for predictions. 3. Make comparisons between the true adjusted values and the predicted. Sentiment Analyzer This idea is useful for businesses as many users express their views regarding a product, service or a company/organization. Analyzing a sentiment reveals if a user is satisfied with the product or not, thus rendering what’s hot and what’s not in terms of demand. It can be done as follows 1. Choose a classifier model, import data. 2. Train the analysis classifier by tagging tweets if needed, then test the classifier. Customer Segmentation It is an evergreen project that not only maximizes clarity for businesses but also benefits customers. Customer segmentation has numerous types ranging from demographics to psychology, techno-graphics, behavioral, geographical etc. With these steps, customer segmentation can be done. 1. Design, categorize a business case. Prepare data after collecting. Then segment via k-means clustering. 2. Tune the model’s hyperparameters and visualize the results. Recommendation System The recommendation system is time-saving and efficient for the customers. Correlated items and other varieties are easily accessible. A system of movie recommendations can be built in this manner. 1. Collect data needed for building the model. Reverse map titles and indices. 2. Test the content-based recommending system. Churn Prediction The churn rate is the pace at which entities are opting out of an organization over a period of time. The churn prediction allows identifying the issues of customers, their pain points, and those who are at the highest risk. This prediction requires a workflow, it can be done as follows 1. Define the issues and the objective, gather sources for data such as CRM systems or customer feedback. 2. Prepare data and explore. Further, preprocess it for modeling and tests. Deploy the model and monitor if required. YouTube Video Classification Plenty of videos exist on YouTube and without proper classification, they would not be found in searches. The categorization helps to index the videos into relevance. The classification system can be built by 1. Collecting data and setting up, then defining the hyperparameters and preparing the data. 2. Using a network pre-trained for extracting relevant features. Feed data into the sequence model. Text Summarizer This is also an evergreen project. Trying to access the gist from a large piece of article can be time-consuming which brings the need for a text summarizer for quick results. The summarizer can be created in these ways 1. Start with data preparation and then process it, basic clean it. Then do article tokenization into sentences. 2. Further, locate their respective weighted frequency, then do threshold calculation, and generate a summary. Image Regeneration for old and damaged reels Doing damaged image repair manually can be a cumbersome task, one that requires skill. But with deep learning, these defects in imagery and reels can be easily corrected via inpainting algorithms. It can help in 1. Colorizing the black and white pics, the areas where pigment has eroded. Different anomalies include tears, holes, scuffs. 2. Pixel values can be altered and old photos can be transformed into a newer edit. Techniques used for restoration 1. SC-FEGAN: it’s useful in face restoration, filling the void with the most probable pixels. 2. EdgeConnect: it utilizes adversarial edge learning to continue over minor imperfections. 3. Pluralistic image completion: yields various outcomes when dealing with hugs gaps. Music Generator Music is a creative human pursuit, however, it can also be generated using LSTM neural networks. This can be done as follows 1. Collect data (royalty-free midi music), use python toolkit music21, Keras for extracting midi files data. 2. Implement LSTM, set the sequence length to 100. Set further 500 note sequences for extending music duration. The repeat feature of this recurrent neural network will generate music. Want One-On-One session with Instructor to clear doubts and help you clear Interviews? Contact Us on +91-9999201478 or fill the Enquiry Form Data Science Instructor Profile Check Machine Learning Course Content

15 Machine Learning Interview Questions (with Answers) for Data Scientists

Data science is a progressive field that deals with handling large chunks of data that normal software fails to do. Although machine learning is a vast field in itself, machine learning interview questions are a common occurrence in a job interview of a data scientist. Some very basic data scientist interview questions deal with various aspects of it, including Statistics and programming. Over here, we will focus on the machine learning part of data science. Machine Learning Interview Questions 1. Differentiate between supervised learning and unsupervised learning These are some notable differences between the two. Supervised Learning Unsupervised Learning Trained on labeled dataset Trained on unlabeled dataset Algorithms used: regression and classification Algorithms used: clustering, association and density estimation Suited for predictions Suited for analysis Maps input to the known output labels Finds hidden patterns and discovers the output   2. Define logistic regression with example Also known as the Logit model, it’s used for predicting a binary outcome from predictor variables having a linear combination. For instance, predicting a politician's victory or defeat in an election is binary. The predictor variables would be time spent in the camp and total money used for the camp. 3. How do classification machine learning techniques and regression differ? These are the key differences Classification Regression Target variables can have discrete values Target variables can have continuous values, usually real numbers Evaluated by measuring accuracy Evaluated by measuring root mean square error   4. What is meant by collaborative filtering? The kind of filtering done by recommender systems for fetching information or patterns, by integrating data sources, agents, and viewpoints is called collaborative filtering. For example, predicting a user’s rating based on his recommendations/ratings for other movies. This technique is very commonly used in referring to sites like Bookmyshow, IMDb, Amazon, Snapdeal, Flipkart, Netflix, YouTube, etc. 5. What are the numerous steps in an analytics project? These are the steps taken in an analytics project:- 1. Comprehending the business problems. 2. Transforming the variables, outlier detection for Data preparation for modeling, checking missing values. 3. Analyzing the outcome, using tweaked approaches after running the model, this is done for achieving a good outcome. 4. Validation of the model via a few data sets. Further, implementing the model and analyzing its performance over a specific duration. 6. Explain in brief a few types of ensemble learning There are several types of ensemble learning, below are some of the more common types. Boosting An iterative technique that helps in weight adjustment of a particular observation based on previous classification. In case, the classification is incorrect, then observation weight is increased. This helps in building reliable predictive models, as it reduces the bias error, but there’s also a possibility of overfitting into the training data. Bagging It attempts to implement learners on a particular sample bunch, further taking a mean of the productions. One can implement other learners on varying bunches in generalized bagging, this prevents some of the variance errors. 7. Describe box-cox transformation In a regression analysis, the dependent variable might not be able to satisfy ordinary least square regression assumptions. The residuals could be following the distribution (skewed) or curve, in case the prediction increases. In such scenarios, the transformation of response variable becomes a necessity in order for data to meet specific assumptions. The box-cox transformation relates to Statistical techniques for transforming dependent, non-normal variables to a conventional shape. When the available data is unconventional, then many statistical techniques assume normality. Numerous tests can be run when box-cox transformation is applied, it’s a method for transforming unconventional, dependent variables into a more conventional shape. This transformation gets its name from its developers, who were Statisticians. Sir David Roxbee Cox and George Box collaborated on a paper in 1964 developing this technique. 8. What’s Random Forest, and how does it function? Also, explain it's working. It’s a versatile method for machine learning that can do classification and regression both. It gets used in outlier values, dimensionality reduction, treating missing values. It’s a kind of ensemble learning method, wherein clusters of weak models integrate to build a powerful model. Numerous decision trees are created instead of a single tree in a random forest. For classifying new attribute-based objects, every tree provides a classification, and the one that has maximum votes (total trees in the forest) gets selected by the forest, as for regression average output of varying trees gets considered. Working of Random Forest This technique's main principle is that various weak learners combine to make a strong learner. The steps include:- 1. Randomly pick k records from the dataset. 2. Build a decision tree on these k records. 3. Repeat the above 2 steps for each decision tree you want to build (repeats for #trees to make) 4. Predictions are based on the majority rule. In regression problem, it predicts a value for output whereas, in classification problem, it predicts the class. 9. If you were to train a model using 10 GB of data set and had only 4 GB RAM, then how would you approach this problem? To start, it’s best to ask about the type of ML model that requires training. For SVM (partial fit will suit best) Follow these steps 1. Start by division of a large data set into smaller size sets. 2. Implement SVM's partial fit method, it will need the full data set's subset. 3. Repeat the second step for different subsets. For neural networks (NumPy array plus batch size will do) Follow these measures 1. In NumPy array, load the full data, NumPy array has a tendency to make mapping of the full data set. It doesn’t load into the memory, the full data set. 2. For attaining required data, pass index into the NumPy array. 3. Make use of this data for passing to neural networks. Maintain a smaller batch size. 10. In an analysis, how do missing values get treated? Once the variables having missing values get identified, the extent of the values that are absent also gets discovered. In case any patterns are picked, it becomes necessary for the analyst to pay attention as these could bring about a couple of significant and valuable business-related insights. And, if no patterns are discovered, then median or mean values can take place of the missing values, or it can be ignored. A default value can be allotted as maximum, minimum, or mean value. In case, the variable is categorical, the default value is assigned to the missing value. If data distribution is incoming, then a mean value is assigned for normal distribution. Also, if a variable’s 80% values seem missing, then it’s reasonable to drop the variable than treat the missing values. 11. How to treat outlier values? For detection of outlier values, some graphical analysis or univariate method can be used. If the outliers are large, then either the 1st percentile value or the 99th percentile can replace the values. Also if the outliers are fewer, then the individual assessment can be done. It should be noted that all outlier values are not necessarily extreme values. For treating outlier values, the values can either be modified and brought within range or they can be discarded. 12. Which cross-validation technique can be used on a time-series dataset? Rather than Implementing the K-Fold technique, one should know that time-series have an inherent chronological order, and is not some randomly distributed data. As far as time-series data is concerned, one can implement the forward-chaining technique, where one has to model previous data, then consider data that is forward-facing. fold 1: training[1], test[2] fold 1: training[1 2], test[3] fold 1: training[1 2 3], test[4] fold 1: training[1 2 3 4], test[5] 13. How often an algorithm requires updating? If these requirements call, then it’s suitable for an algorithm to be updated:- 1. Model evolution is a must as data runs through infrastructure. 2. Data source (underlying) is not constant. 3. A non-stationary case shows up. 4. Results don’t have good precision and accuracy as the algorithm doesn’t perform well. 14. List some drawbacks of linear model These are a few drawbacks of the linear model 1. For binary and count outcomes, it is not usable. 2. Error linearity assumptions occur too often. 3. Over-fitting problems that cannot be solved. 15. Describe SVM algorithm SVM (Support Vector Machine) is an algorithm (supervised machine learning) implemented for classification and regression. If one’s training data set has n features, then SVM does their plotting in a space that is n-dimensional, where every feature's value is a specific coordinate's value. SVM implements hyperplanes for the segregation of distinct classes. Want One-On-One session with Instructor to clear doubts and help you clear Interviews? Contact Us on +91-9999201478 or fill the Enquiry Form Data Science Instructor Profile Check Machine Learning Course Content

Top NLP (Natural Language Processing) Interview Question Answers

An Introduction to Natural language processing is fairly a good start for students who wish to bridge the gap between what’s human-like and what’s mechanical. Natural language processing is widely utilized in artificial intelligence and also implemented in machine learning. Its use is expected to go up in the coming years, along with rising job opportunities. Students preparing for natural language processing (NLP) should have a decent understanding of the type of questions that get asked in the interview.  1. Discuss real-life apps based on Natural Language Processing (NLP). Chatbot: Businesses and companies have realized the importance of chatbots, as they assist in maintaining good communication with customers, any queries that a chatbot fails to resolve gets forwarded. They help keep the business moving as they are used 24/7. This feature makes use of natural language processing. Google Translate: Spoken words or written text can be converted into another language, proper pronunciation is also available of words, Google Translate makes use of advanced NLP which makes all of this possible. 2. What is meant by NLTK? Natural language toolkit is a Python library that processes human languages, different techniques including tokenization, stemming, parsing, lemmatization are used for grasping the languages. Also used for classification of text, and assessing documents. Some libraries include DefaultTagger, wordnet, patterns, treebank, etc. 3. Explain parts of speech tagging (POS tagging). POS is also known as parts of speech tagging is Implemented for assigning tags onto words like verbs, nouns, or adjectives. It allows the software to understand the text, then recognize word differences using algorithms. The purpose is to make the machine comprehend the sentences correctly.  Example:- import nltk from nltk.corpus import stopwords from nltk.tokenize import word_tokenize, sent_tokenize stop_words = set (stopwords.Words('english')) txt = "A, B, C are longtime classmates."   ## Tokenized via sent_tokenize tokenized_text = sent_tokenize (txt)   ## Using word_tokenizer to identify a string’s words and punctuation then removing stop words for n in tokenized_text: wordsList = nltk.word_tokenize(i) wordsList = [w for w in wordsList if not w in stop_words]   ## Using POS tagger tagged_words = nltk.pos_tag(wordsList) print (tagged_words)   Output:- [(‘A’, 'NNP'), ('B', 'NNP’), ('C', 'NNP’), ('longtime', 'JJ’), ('classmates', 'NNS')]   4. Define pragmatic analysis In a given data of human language, different meaning exists, in order to understand more, pragmatic analysis is used for discovering different facets of the data or document. Actual meaning of words or sentences can be understood by the systems, and for this purpose pragmatic analysis is deployed. 5. Elaborate on Natural language processing components These are the major NLP components:- 1. Lexical/morphological analysis: word structure is made comprehensible via analysis through parsing. 2. Syntactic analysis: specific text meaning is assessed 3. Entity extraction: information like the place, institution, individual gets retrieved via sentence dissection. Entities present in a sentence get identified. 4. Pragmatic analysis: helps in finding real meaning and relevancy behind the sentences. 6. List the steps in NLP problem-solving The steps in NLP problem-solving include:- 1. Web scraping or collecting the texts from the dataset. 2. For text cleaning, making use of lemmatization and stemming. 3. Use feature engineering 4. Use word2vec for embedding 5. Using machine learning techniques or with neural networks, start training the models. 6. Assess the performance. 7. Do the required model modifications and deploy. 7. Elaborate stemming with examples When a root word is gained by detaining the prefix or suffix involved, then that process is known as stemming. For instance, the word 'playing' can be minimized to ‘play’ by removing the rest. Different algorithms are deployed for implementation of stemming, for example, PorterStemmer which can be imported from NLTK as follows:- from nltk.stem import PorterStemmer pst = PorterStemmer() pst.stem(“running”), pst.stem(“cookies”), pst.stem(“flying”)   Output:- (‘run’, 'cooki', 'fly' )   8. Define and implement named entity recognition For retrieving information and identifying entities present in the data for instance location, time, figures, things, objects, individuals, etc. NER (named entity recognition) is used in AI, NLP, machine learning, implemented for making the software understand what the text means. Chatbots are a real-life example that makes use of NER. Implementing NER with spacy package:- import spacy nlp = spacy.load('en_core_web_sm') Text = "The head office of Tesla is in California" document = nlp(text)  for ent in document.ents: print(ent.text, ent.start_char, ent.end_char, ent.label_)   Output:- Office 9 15 Place Tesla 19 25 ORG California 32 41 GPE   9. Explain checking word similarity with spacy package Spacy library allows the implementation of word similarity techniques for detecting similar words. The evaluation is done with a number between 0 & 1 (where 0 tends towards less similar and 1 tends toward highly similar). import spacy nlp = spacy.load('en_core_web_md') print ("Enter the words:") input_words = input() tokens = nlp(input_words) for i in tokens: print(i.text, i.has_vector, i.vector_norm, i.is_oov) token_1, token_2 = tokens[0], tokens[1] print("Similarity between words:", token_1.similarity(token_2))   Output:- hot  True 5.6898586  False cold True6.5396233 False Similarity: 0.597265 This implies that the similarity between the two words cold and hot is  59%. 10. Describe recall and precision. Also, explain TF-IDF. Precision and recall Precision, F1 and Recall, accuracy are NLP model testing metrics. The ratio of predictions with required output provides for a model's accuracy. Precision: The ratio of positive instances and total predicted instances. Recall: The ratio between real positive instances and total (real + unreal) positive instances. TF-IDF Term frequency-inverse document frequency is used for retrieval of information via numerical Statistics. It helps in identifying keywords present in any document. The real usage of it revolves around getting information from important documents using Statistical data. It’s also useful in filtering out the stop words and text summarizing plus classification in the documents. With TF one can calculate the ratio of term frequency in a document wrt total terms, whereas IDF implies the significance of the term. TF IDF calculation formula: TF = frequency of term 'W' in a document / total terms in document IDF = log( total documents / total documents with the term ‘W’) If TF*IDF appears higher then term frequency is likely less. Google implements TF-IDF for deciding search results index, which helps in optimization or ranking the relevant quality content higher. 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Top SQL Interview Questions with Answers for a Data Analyst Interview

Data analysts perform a variety of roles including providing reports using statistical methods and analyzing data, implementing systems for data collection, and developing databases, identifying trends, and interpreting complex data set patterns. SQL is the industry-standard language used by data analysts for providing data insights. In a job interview, SQL being a major component of data analysis features highly in the interrogation. These are some of SQL Query Interview Questions for Data Analyst that are frequently asked. Data Analyst interview questions and answers for freshers Consider the following tables Employee table employee_id full_name manager_id date_of_joining city 121 Shanaya Gupta 321 1/31/2014 Bangalore 321 Snehil Aggarwal 986 1/30/2015 Delhi   Salary table employee_id project salary variable 121 P1 8000 500 321 P2 10000 1000 421 P1 12000 0   1. Write a query fetching the available projects from salary table. Upon looking at the employee salary table, it is observable that every employee has a project value correlated to it. Duplicate values also exist, so a unique clause will be used in this case to get distinct values. SELECT DISTINCT(project) FROM Salary;   2. Write a query fetching full name and employee ID of workers operating under manager having ID 986 Take a look at the employee details table, here we can fetch employee details working under the manager with ID 986 using a WHERE clause. SELECT employee_id, full_name FROM Employee WHERE manager_id=986;   3. Write a query to track the employee ID who has a salary ranging between 9000 and 15000 In this case, we will use a WHERE clause, with BETWEEN operator SELECT employee_id, salary FROM Salary WHERE salary BETWEEN 9000 and 15000;   4. Write a query for employees that reside in Delhi or work with manager having ID 321 Over here, one of the conditions needs to be satisfied. Either worker operating under Manager with ID 321 or workers residing in Delhi. In this scenario, we will require using OR operator. SELECT employee_id, city, manager_id FROM Employee WHERE manager_id='321' OR city='Delhi';   5. Write a query displaying each employee's net salary added with value of variable Now we will require using the + operator. SELECT employee_id, salary+variable AS Net Salary FROM Salary;   6. Write a query fetching employee IDs available in both tables We will make use of subquery SELECT employee_id FROM Employee WHERE employee_id IN (SELECT employee_id FROM Salary);   7. Write a query fetching the employee’s first name (string before space) from the employee table via full_name column First, we will require fetching space character’s location from full_name field, then further extracting the first name out of it. We will use LOCATE in MySQL, then CHARINDEX in SQL server. MID or SUBSTRING method will be utilized for string before space Via MID (MySQL) SELECT MID(full_name, 1, LOCATE(' ', full_name)) FROM Employee;   Via SUBSTRING (SQL server) SELECT SUBSTRING(full_name, 1, CHARINDEX(' ', full_name)) FROM Employee;   8. Write a query fetching the workers who have their hands-on projects except for P1 In this case, NOT operator can be used for fetching rows that do not satisfy the stated condition. SELECT employee_id FROM Salary WHERE NOT project = 'P1';   Also, using not equal to operator SELECT employee_id FROM Salary WHERE project <> 'P1';   9. Write a query fetching name of employees who have salary equating 5000 or more than that, also equating 10000 or less than that Over here, BETWEEN will be used in WHERE for returning employee ID of workers whose remuneration satisfies the stated condition, further using it as subquery for getting the employee full name via the table (employee). SELECT full_name FROM Employee WHERE employee_id IN (SELECT employee_id FROM Salary  WHERE salary BETWEEN 5000 AND 10000);   10. Write a query fetching details of the employees who started working in 2020 from employee details table. For this, we will use BETWEEN for time period ranging 01/01/2020 to 31/12/2020 SELECT * FROM Employee WHERE date_of_joining BETWEEN '2020/01/01' AND '2020/12/31';   Now the year can be extracted from date_of_joining using YEAR function in MySQL SELECT * FROM Employee WHERE YEAR(date_of_joining) = '2020';   11. Write a query fetching salary data and employee names. Display the details even if an employee's salary record isn’t there. Here, the interviewer is trying to gauge your knowledge related to SQL JOINS. Left JOIN will be used here, with Employee table being on the left side of Salary table. SELECT E.full_name, S.salary FROM Employee E LEFT JOIN Salary S ON E.employee_id = S.employee_id;   Advanced SQL, DBMS interview questions These SQL interview questions for 6 years of experience can help you in your job application. 12. Write a query for removing duplicates in a table without utilizing the temporary table Inner join along with delete will be used here. Equality of matching data will be assessed further, the rows with higher employee ID will be discarded. DELETE E1 FROM Employee E1 INNER JOIN Employee E2 WHERE E1.employee_id > E2.employee_id AND E1.full_name = E2.full_name AND E1.manager_id = E2.manager_id AND E1.date_of_joining = E2.date_of_joining AND E1.city = E2.city;   13. Write a query fetching just the even rows in the Salary table If there’s an auto-increment field, for instance, employee_id, then the below-mentioned query can be used. SELECT * FROM Salary WHERE MOD(employee_id,2) = 0;   If the above-stated field is absent (auto-increment field), then these queries can be used. Verifying the remainder is 0 when divided with 2, and by using ROW_NUMBER (in SQL server) SELECT E.employee_id, E.project, E.salary FROM (       SELECT *, ROW_NUMBER()       OVER(ORDER BY employee_id) AS RowNumber       FROM Salary      ) E WHERE E.RowNumber % 2 = 0;   Using variable (user-defined) in MySQL SELECT * FROM (            SELECT *, @rowNumber := @rowNumber+1 RowNo      FROM Salary      JOIN(SELECT @rowNumber := 0) r      ) t WHERE RowNo % 2 = 0;   14. Write a query fetching duplicate data from Employee table without referring to employee_id (primary key) In this case, on all the fields, we will use GROUP BY, further HAVING clause will be used for returning duplicate data that has more than one count. SELECT full_name, manager_id, date_of_joining, city, COUNT(*) FROM Employee GROUP BY full_name, manager_id, date_of_joining, city HAVING COUNT(*) > 1;   15. Write a query creating the same structured empty table as any other Over here, false WHERE condition will be used. CREATE TABLE NewTable SELECT * FROM Salary WHERE 1=0;   The above mentioned are some of the most common SQL data analyst interview questions to prepare for entry-level, intermediate and advanced level jobs. Check the SQL Training Program

How Power BI is Better than Excel?

Analysis of business data is essential to make it big as far as commerce is concerned. Be it a small enterprise or a multinational company looking to widen its reach. Several businesses are waking up and realizing the significance of data analysis. Two of the most common used tools are Power BI and excel, choosing the right one to work with can be a bit cumbersome.   What is Power BI tool? Power BI is a product from Microsoft, which focuses on processing data associated with a business. To be more specific, it is a toolset that caters to the deeper demographics of a business, and its functional operations. It is often compared to Excel, as both are very similar in what they do. There are quite some differences as visualization in Power BI is far more appealing for instance, reports also are more concise. Power BI Advantages Power BI has a couple of advantages over Excel: 1. Exclusive data visualization tool 2. Designed keeping business intelligence as the focus 3. Handles large chunks of data easily 4. Can be used on mobile devices 5. Connects to several data sources 6. Quicker processing 7. Customizable dashboards 8. Better interactivity 9. Appealing visualization 10. In-depth comparison of data files and reports 11. User friendly 12. Actionable insights can be achieved thanks to incredible data visualization. 13. Facilitates the exploring of data via natural language query. Excel and its most common uses Microsoft Excel is ideal in many ways 1. Faster calculations: making formulas in data and doing calculations is quick work with Excel. 2. Versatility: users don’t have to switch to another app due to its versatility. 3. Table creation: complex tables can be created for advanced calculations. Why Power BI is highly preferred? One of the reasons it’s the go-to tool is that Power BI dashboard can be accessed on a mobile device and can be shared among co-workers. Although a dashboard contains a single page, Power BI report allow for more than one page. Data interrogation is possible with dashboards. Power BI uses a combo of dashboards and reports for specific usage. Monitoring a business gets easier as various metrics are available to analyze and look for answers. Integration of cloud and on-premises data gives a compact view regardless of data location. Apart from its appealing looks, the tiles appear dynamic and change alongside the circulating data to facilitate updates. Prebuilt reports are also available for SaaS solutions. Secure environment, quick deployment, and hybrid configuration are a big plus of Power BI. Start Learning Power BI Packed with versatile tools There are a bunch of Power BI tools that allow better interactivity 1. Data gateway: installed via admin, it acts as a bridge between on-premise data sources such as Live Query and Power BI service. 2. Service: an online software service, where admin sharing occurs via cloud. Dashboard, data models, and reports also get hosted. 3. Desktop: Primary tool for publishing and authoring. Used by developers for creating reports and models. 4. Report server: hosts several types of reports including mobile, Power BI, paginated, and KPIs. Gets updated every fourth month, as IT professionals manage it. 5. Mobile apps: made for windows, Android and iOS. On the report server, users can view the dashboard and reports. Power BI filters and Data sorting The filters in Power BI allow for refined results to appear based on value selection. Some commonly used filters are: 1. Report level 2. Visual level 3. Automatic 4. Page-level 5. Drill-through 6. Cross drill What’s better is that users get both basic and advanced modes of utilizing the filters to get the desired results. Check Business Analytics Course Content More factors that make Power BI the first choice 1. Q/A and custom pack 2. Quick spotting of Data trends 3. Available on the go access 4. Scheduling Data refresh 5. Intuitive and better UX features 6. Storing, analyzing, accessing huge amounts of data without hassles 7. Data integration into a centralized dashboard 8. Forecasting via inbuilt predictive models 9. Security features (row-level) 10. Various cloud services integration 11. Access control Apart from the listed plus points, one can also use Power BI API which allows pushing data into a set, rows to a table can be further added. The data then shows up in dashboard tiles as a visual in the reports. Advanced Excel Crash Course Conclusion Power BI is the right choice compared to excel when the target is 1. Maneuvering large data for insights 2. Creating complex, graphically interactive visualizations 3. Making tabular format reports 4. Collaborative teamwork 5. Dealing in business intelligence and profound data analysis

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