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Machine Learning with Python Training Course in Gurgaon

According to the 2020 Stack Overflow survey, nearly 52% of developers use python, and a further 30% want to do so. It is one of the official languages of Google. Python and Machine learning are termed as the bread & butter of the future analytics industry. There is so much curiosity about them that Python and Machine learning searches even outstrip searches for Donald Trump and Sunny Leone on Google. 

Instructor from Microsoft  |  Instructor-Led Training  |  Free Course Repeat  |  Placement Assistance  |  Job Focused Projects  |  Interview Preparation Sessions 

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Curriculum

This Course Content designed by Microsoft Expert, Machine learning is training PCs to gain information without being unequivocally customized. Python is a famous programming language for Machine learning since it has countless strong libraries and systems

    This course presents standards, calculations, and utilizations of Machine learning according to the perspective of demonstrating and expectation. It incorporates definitions of learning issues and ideas of portrayal, over-fitting, and speculation. These ideas are practiced in administered learning and support learning, with applications to pictures and worldly arrangements.

    Key terms of this module

    • Statistical learning vs. Machine learning
    • Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
    • Different Phases of Predictive Modelling (Data Pre-processing, Sampling, Model Building, Validation) 
    • Concept of Overfitting and Underfitting (Bias-Variance Tradeoff) & Performance Metrics
    • Types of Cross-validation(Train & Test, Bootstrapping, K-Fold validation etc)
    • Iteration and Model Evaluation 

    The core of any effective Machine learning model. Exploratory information examination is a basic move toward fostering an incredible model. As we partition our information into train and test bunches utilizing an 80/20 split, distributing more information to preparing and less to test with machine learning with Python courses.

    Key terms of this module

    • Python Numpy (Data Manipulation)
    • Python Pandas(Data Extraction & Cleansing)
    • Python Matplot (Data Visualization)
    • Python Scikit-Learn (Data Modelling)
    • EDA – Quantitative Technique

    The information preprocessing stage is pivotal for deciding the right information for the Machine learning calculations. As we saw beforehand, without applying the legitimate methods, you can have a more regrettable model outcome.


    Key terms of this module

    • Data Exploration Techniques
    • Sea-born | Matplotlib
    • Correlation Analysis
    • Data Wrangling
    • Outliers Values in a DataSet
    • Data Manipulation
    • Missing & Categorical Data
    • Splitting the Data into Training Set & Test Set
    • Feature Scaling
    • Concept of Over fitting and Under fitting (Bias-Variance Trade off) & Performance Metrics
    • Types of Cross validation(Train & Test, Bootstrapping, K-Fold validation etc)

    Machine learning, a part of computerized reasoning (artificial intelligence), has become perhaps the main improvement in information science. This ability centers around building calculations intended to find designs in large informational collections, working on their exactness over the long run.


    Key terms of this module

    • Basic Data Structure & Data Types in Python language.
    • Working with data frames and Data handling packages.
    • Importing Data from various file sources like csv, txt, Excel, HDFS and other files types.
    • Reading and Analysis of data and various operations for data analysis.
    • Exporting files into different formats.
    • Data Visualization and concept of tidy data.
    • Handling Missing Information.

    Capstone tasks or capstone courses it is that timeframe where the understudy should give 1 or 2 semesters in their last year where he/she invests energy doing explores, gathering data, and organizing into an exploration paper (can be involved) or an undertaking thought by picking one specific field of revenue.


    Key terms of this module

    • Calls Data Capstone Project
    • Finance Project : Perform EDA of stock prices. We will focus on BANK Stocks(JPMorgan, Bank Of America, Goldman Sachs, Morgan Stanley, Wells Fargo) & see how they progressed throughout the financial crisis all the way to early 2016.

    Examining in Measurable Derivation The utilization of randomization in testing considers the examination of results utilizing the strategies for factual inference. Statistical surmising depends on the laws of likelihood and permits experts to gather decisions about a given populace given results seen through irregular testing


    Key terms of this module

    • Fundamental of descriptive Statistics and Hypothesis testing (t-test, z-test).
    • Probability Distribution and analysis of Variance.
    • Correlation and Regression.
    • Linear Modeling.
    • Advance Analytics.
    • Poisson and logistic Regression

    Dimensionality decrease is an interaction used to diminish the dimensionality of a dataset, taking many highlights and addressing them as fewer elements. For instance, dimensionality decrease could be utilized to diminish a dataset of twenty elements to only a few highlights.


    Key terms of this module

    • Feature Selection
    • Principal Component Analysis(PCA)
    • Linear Discriminant Analysis (LDA)
    • Kernel PCA
    • Feature Reduction 

    These datasets are intended to prepare or "direct" calculations into grouping information or foreseeing results precisely. The model can gauge its precision and learn after some time by utilizing market data sources and results. Administered learning can be isolated into two issues while information mining: arrangement and relapse.


    Key terms of this module

    • Simple Linear Regression
    • Multiple Linear Regression
    • Perceptron Algorithm
    • Regularization
    • Recursive Partitioning (Decision Trees)
    • Ensemble Models (Random Forest , Bagging & Boosting (ADA, GBM)
    • Ensemble Learning Methods
    • Working of Ada Boost
    • AdaBoost Algorithm & Flowchart
    • Gradient Boosting
    • XGBoost
    • Polynomial Regression
    • Support Vector Regression (SVR)
    • Decision Tree Regression
    • Evaluating Regression Models Performance
    • Logistic Regression
    • K-Nearest Neighbours(K-NN)
    • Support Vector Machine(SVM)
    • Kernel SVM
    • Naive Bayes
    • Decision Tree Classification
    • Random Forest Classification
    • Evaluating Classification Models Performance

    These datasets are intended to prepare or "direct" calculations into grouping information or foreseeing results precisely. The model can gauge its precision and learn after some time by utilizing market data sources and results. Administered learning can be isolated into two sorts of issues while information mining: arrangement and relapse


    Key terms of this module

    • K-Means Clustering
    • Challenges of Unsupervised Learning and beyond K-Means
    • Hierarchical Clustering

    A recommender framework, or a proposal framework (here and there supplanting 'framework' with an equivalent word like stage or motor), is a subclass of data-sifting framework that gives ideas to things generally relevant to a specific client.


    Key terms of this module

    • Purpose of Recommender Systems
    • Collaborative Filtering

    Related product recommendations are another fundamental use instance of proposal frameworks. If a client makes a buy, it's a good idea to offer them something that goes with that. If the client were to purchase some pants, you could offer them shoes or a shirt that works out positively for them.


    Key terms of this module

    • Market Basket Analysis
    • Collaborative Filtering
    • Content-Based Recommendation Engine
    • Popularity Based Recommendation Engine
    • Anomaly Detection and Time Series Analysis

    Reinforcement learning (RL) is an area of Machine learning worried about how wise specialists should move in a climate to expand the idea of aggregate award. Reinforcement learning is one of three fundamental Machine learning standards, close by managed learning and solo learning.


    Key terms of this module

    • Upper Confidence Bound (UCB)
    • Thompson Sampling

    Text mining (additionally alluded to as text examination) is a man-made reasoning (computer-based intelligence) innovation that utilizes regular language handling ( NLP ) to change the free (unstructured) text in records and data sets into standardized, organized information appropriate for investigation or to drive Machine learning (ML) calculations.


    Key terms of this module

    • Spacy Basics
    • Tokenization
    • Stemming
    • Lemmatization
    • Stop-Words
    • Vocabulary-and-Matching
    • NLP-Basics Assessment
    • TF-IDF

    Word2vec is a method for normal language handling (NLP) distributed in 2013. The word2vec calculation utilizes a brain network model to gain word relationships from a huge text corpus. Such a model can identify interchangeable words or propose extra words for a halfway sentence when prepared.


    Key terms of this module

    • Understanding Word Vectors
    • Training the Word2Vec Model
    • Exploring Pre-trained Models

    Machine learning is essential to learn NLP because the strategies like Sack-of-words(BoW), Word2Vec, and TF-IDF all go under the Machine learning umbrella, and learning NLP is a must.


    Key terms of this module

    • POS-Basics
    • Visualizing POS
    • NER-Named-Entity-Recognition
    • Visualizing NER
    • Sentence Segmentation

Course Description

Machine learning is training PCs to gain information without being unequivocally customized. Python is a famous programming language for Machine learning since it has countless strong libraries and systems that make it simple to execute Machine learning

    Machine Learning with Python Training course will help you understand both basic & advanced level concepts like writing python scripts, sequence & file operations in python, Machine Learning, Data Analytics, Web application development & widely used packages like NumPy, Matplot, Scikit, Pandas & many more. Professionals who don’t have good coding skill need not worry, Python is the most user-friendly and easy to learn language, is used as a powerful tool in handling advanced analytics applications

    After the completion of Machine Learning Course, you will be able to:

    • Develop and Implement various Machine Learning Algorithms in daily practices & Live Environment.
    • Build Real time Machine Learning Applications
    • Implement Data Analytics models on various Data Sets
    • Data Mining across various file formats using Machine Learning models
    • Building Recommendation systems and Classifiers
    • Perform various type of Analysis (Prediction & Regression)
    • Implement plotting & graphs using various Machine Learning Libraries
    • Import data from HDFS & Implement various Machine Learning Models
    • Building different Neural networks using NumPy and TensorFlow

    Gyansetu Machine Learning program is delivered by faculty having a strong educational M.Tech (CS) from IIT-Hyderabad, B.Tech (CS) from NIT-Surat (Gold Medalist) & currently working with the world's top IT company Microsoft.

    We at Gyansetu understand that teaching any course is not difficult but to make someone job-ready is an essential task. That's why we have prepared capstone projects which will drive your learning through real-time industry scenarios and help you clearing interviews.

    All the advanced level topics will be covered at Gyansetu in a classroom/online Instructor-led mode with recordings.

    Knowledge of basic statistics & any programming language is beneficial. However, Gyansetu offers a complimentary instructor-led course on statistics & python before you start the Machine Learning Training in Gurgaon.

    Gyansetu is providing a complimentary placement service to all students. Gyansetu Placement Team consistently works on industry collaboration and associations which help our students to find their dream job right after the completion of training.

    • Our placement team will add Python and Machine Learning skills & projects in your CV and update your profile on Job search engines like Naukri, Indeed, Monster, etc. This will increase your profile visibility in top recruiter search and ultimately increase interview calls by 5x.
    • Our faculty offers extended support to students by clearing doubts faced during the interview and preparing them for the upcoming interviews.
    • Gyansetu’s Students are currently working in Companies like Sapient, Capgemini, TCS, Sopra, HCL, Birlasoft, Wipro, Accenture, Zomato, Ola Cabs, Oyo Rooms, etc.
    • Gyansetu trainer’s are well known in Industry; who are highly qualified and currently working in top MNCs.
    • We provide interaction with faculty before the course starts.
    • Our experts help students in learning Technology from basics, even if you are not good in basic programming skills, don’t worry! We will help you.
    • Faculties will help you in preparing project reports & presentations.
    • Students will be provided Mentoring sessions by Experts.

    Upon finishing the Machine learning course training, participants are required to take an online examination facilitated by the academy. To obtain certification, they must score 60% or higher.
    The skills and knowledge you gain through working on projects, simulations, and case studies will set you apart from the competition, giving you a Machine Learning with Python Training in Gurgaon course certificate to differentiate yourself. 
     

    • This thorough Artificial Intelligence and ML course convey a high-commitment growth opportunity utilizing industry ability in man-made intelligence and ML.
    • Gain from Top Teachers - Worldwide artificial intelligence and ML specialists lead preparing with industry-explicit cases and applications.
    • Masterclasses conveyed by recognized & top-notch teachers only at Gurgaon
       

    A few essential requirements for learning AI include A fundamental comprehension of Python programming basics. Extra programming abilities in R, C++, and Octave. Capacity to understand a few high-level numerical ideas, including direct polynomial math, math, and chart hypothesis. 
    Some basic criteria to get us started.

    • Precision
    • Robustness
    • Learning productivity and transformation
    • Execution

    To become a machine learning specialist, you first need major areas of strength out of four learning regions: Coding, Math, ML hypothesis, and how to construct your own ML project from beginning to end.

    • Coding abilities: Building ML models includes significantly more than simply knowing ML ideas — it requires coding to do the information on the board, boundary tuning, and parsing results expected to test and advance your model.
    • Math and details: ML is a number-related weighty discipline, so if you intend to change ML models or construct new ones without any preparation, knowledge of the fundamental numerical ideas is pivotal to the interaction.
    • ML hypothesis: Knowing the rudiments of the ML hypothesis will give you an establishment to expand on and assist you with investigating when something turns out badly.
    • Fabricate your own ventures: Getting hands-on experience with ML is the most effective way to scrutinize your insight. Feel free to plunge right on time with a straightforward collab or instructional exercise to get some training.
       

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Machine Learning with Python Certification

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Machine Learning with Python Training Course in Gurgaon Features

Frequently Asked Questions

    We have seen getting a relevant interview call is not a big challenge in your case. Our placement team consistently works on industry collaboration and associations which help our students to find their dream job right after the completion of training. We help you prepare your CV by adding relevant projects and skills once 80% of the Machine Learning course is completed. Our placement team will update your profile on Job Portals, this increases relevant interview calls by 5x.

    Interview selection depends on your knowledge and learning. As per the past trend, the initial 5 interviews is a learning experience of

    • What type of technical questions are asked in interviews?
    • What are their expectations?
    • How should you prepare?

    Our faculty team will constantly support you during interviews. Usually, students get job after appearing in 6-7 interviews.

    We have seen getting a technical interview call is a challenge at times. Most of the time you receive sales job calls/ backend job calls/ BPO job calls. No Worries!! Our Placement team will prepare your CV in such a way that you will have a good number of technical interview calls. We will provide you interview preparation sessions and make you job-ready. Our placement team consistently works on industry collaboration and associations which help our students to find their dream job right after the completion of training. Our placement team will update your profile on Job Portals, this increases relevant interview call by 3x

    Interview selection depends on your knowledge and learning. As per the past trend, the initial 8 interviews is a learning experience of

    • What type of technical questions are asked in interviews?
    • What are their expectations?
    • How should you prepare?

    Our faculty team will constantly support you during interviews. Usually, students get job after appearing in 6-7 interviews.

    We have seen getting a technical interview call is hardly possible. Gyansetu provides internship opportunities to non-working students so they have some industry exposure before they appear in interviews. Internship experience adds a lot of value to your CV and our placement team will prepare your CV in such a way that you will have a good number of interview calls. We will provide you interview preparation sessions and make your job-ready. Our placement team consistently works on industry collaboration and associations which help our students to find their dream job right after the completion of training and we will update your profile on Job Portals, this increases relevant interview call by 3x

    Interview selection depends on your knowledge and learning. As per the past trend, initial 8 interviews is a learning experience of

    • What type of technical questions are asked in interviews?
    • What are their expectations?
    • How should you prepare?

    Our faculty team will constantly support you during interviews. Usually, students get job after appearing in 6-7 interviews.

     

    Yes, a one-to-one faculty discussion and demo session will be provided before admission. We understand the importance of trust between you and the trainer. We will be happy if you clear all your queries before you start classes with us.

    We understand the importance of every session. Sessions recording will be shared with you during Machine Learning Training and in case of any query, faculty will give you extra time to answer your queries.

    Yes, we understand that self-learning is most crucial and for the same we provide students with PPTs, PDFs, class recordings, lab sessions, etc, so that a student can get a good handle of these topics.

    We provide an option to retake the Machine Learning course within 3 months from the completion of your training, so that you get more time to learn the concepts and do the best in your interviews.

    We believe in the concept that having less students is the best way to pay attention to each student individually and for the same our batch size varies between 5-10 people.

    Yes, we have batches available on weekends. We understand many students are in jobs and it's difficult to take time for training on weekdays. Batch timings need to be checked with our counsellors.

    Yes, we have batches available on weekdays but in limited time slots. Since most of our trainers are working, so either the batches are available in morning hours or in the evening hours. You need to contact our counsellors to know more on this.

    Total duration of the Machine Learning Training course is 160 hours (80 Hours of live-instructor-led training and 80 hours of self-paced learning).

    You don’t need to pay anyone for software installation, our faculties will provide you all the required softwares and will assist you in the complete installation process.

    Our faculties will help you in resolving your queries during and after the course.

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