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Data Science, Machine Learning Specialization

 

Instructor: Shalki Aggarwal (Working with Microsoft, M.Tech: IIIT-Hyderabad, B.Tech: NIT Surat)

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Mode of Teaching: Hybrid model (Online Classes are delivered through a Learning Platform & Offline classes are available in institute in Gurgaon)

Learner Outcome: 45% started new career after completing this course | 80% got a salary hike

Skills you will gain: Data Science, Machine Learning, Python Programming, Data Analysis, Data Visualization, Statistical Inference, Supervised Learning, Unsupervised Learning, Dimensionality Reduction

Features: 100% Instructor Led, Classes will be Recorded, Repeat your course for free, Job Assistance available, Job Focussed Projects

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Curriculum

A specialized 5 months program comprises of ten-courses, developed and taught by industry experts

    1 Week

    • 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 Under fitting (Bias-Variance Trade off) & Performance Metrics
    • Types of Cross validation(Train & Test, Bootstrapping, K-Fold validation etc)
    • Iteration and Model Evaluation

     

    Assignments will be given every week

    4 Weeks

    • Installation of Python
    • Python Datatypes & Python Loops
    • Python Strings
    • Python Lists
    • Python Tuples
    • Python Dictionary
    • Python Date & Time
    • Python Operators
    • Python Functions
    • Python I/O Functions

     

    + Weekly Assignments

    2 Weeks

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

     

    + Weekly Assignments

    2 Weeks

    • 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

     

    + Weekly Assignments

    4 Weeks

    • 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

     

    + Weekly Assignments

    2 Weeks

    • K-Means Clustering
    • Challenges of Unsupervised Learning and beyond K-Means
    • K-Means++ Clustering
    • Hierarchical Clustering - Single, Complete, Average and Ward Linkages
    • DBSCAN Clustering
    • GMM Clustering (Gaussian Mixture Model Clustering)
    • Cluster Validation - External and Internal indices for cluster validation

     

    + Weekly Assignments

    1 Week

    • Feature Selection
    • Principal Component Analysis(PCA)
    • Linear Discriminant Analysis (LDA)
    • Random Projection
    • Independent Component Analysis (ICA)
    • Kernel PCA
    • Feature Reduction

     

    + Weekly Assignments

    2 Weeks

    Project-1: Online Retail Analysis

    Online retail is a transactional data set which contains all the transactions for a UK-based and registered non-store online retail. The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.

    Project-2: Building ML Model for Auto Insurance Industry

    The aim of the project is to build a Machine Learning Model to predict whether an owner will initiate an auto insurance claim in the next year.

    1 Week

    • What is Natural Language Processing?
    • NLTK Toolkit Basics
    • Tokenization
    • Stemming
    • Lemmatization
    • Stop-Words
    • Vocabulary-and-Matching
    • NLP-Basics Assessment
    • TF-IDF
    • Word2Vec Model
    • Glove Model
    • POS-Basics
    • Visualizing POS
    • NER-Named-Entity-Recognition
    • Visualizing NER
    • Sentence Segmentation

    1 Week

    • Introduction to Neural Networks
    • What is a Neuron?
    • Neural Network Activation Functions
      • RELU
      • TANH
      • Sigmoid
      • Hyperbolic
    • How do Neural Networks work?
    • How do Neural Network Learn?
    • Cost Functions
    • Gradient Descent
    • Stochastic Gradient Descent
    • Backpropagation
    • Building an ANN

Course Description

    Deep Science training will build your expertise in 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. The training will build your skills in real time scenarios related to analytics such as Prediction Analysis, Linear Regression, Image Processing, Audio & Video Analytics, Text Data Processing, IOT and various Machine Learning & Deep Learning Algorithms. 

    After the completion of Data Science Course, you will be able to:

    • Develop and Implement various Machine Learning Algorithms in daily practices & Live Environment.
    • Build Real time Machine Learning and Deep Learning Applications
    • Implement Data Analytics models on various Data Sets
    • Data Mining across various file formats using Machine Learning and Deep Learning models
    • Building Recommendation systems and Classifiers
    • Perform various type of Analysis (Prediction & Regression)
    • Implement plotting & graphs using various Machine Learning and Deep Learning Libraries (Tensor Flow & Keras)
    • Implement Data Analytics models (CNN & RNN) on various Data Sets
    • Building Image & Video Classifiers, Speech Analytics using Deep Learning models
    • Perform various type of Analysis (Time Series, Image, Video, Audio, Face Detection & Recognition)
    • Perform Big Data Analytics using Deep Learning & other frameworks
    • Building different Neural networks using TensorFlow, Keras, PyTorch & other Deep Learning  Libraries.

    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.

    No prerequisites. This course is for beginners.

    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.

Certification

You can share your Course Certificates in the Certifications section of your LinkedIn profile, on printed resumes, CVs, or other documents.

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Projects

    Online retail is a transactional data set which contains all the transactions for a UK-based and registered non-store online retail. The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.

    Goal: We aim to segment the Customers based on RFM so that the company can target it’s customers efficiently.

    Recency, Frequency, Monetary Value (RFM): 

    Recency, frequency, monetary value is a marketing analysis tool used to identify a company's or an organization's best customers by using certain measures. The RFM model is based on three quantitative factors:

    • R (Recency): Number of days since last purchase
    • F (Frequency): Number of transactions
    • M (Monetary): Total amount of transactions (revenue contributed)

    Aim of the Project:

    The aim of the project is to build a Machine Learning Model to predict whether an owner will initiate an auto insurance claim in the next year.

    Background:

    The auto insurance industry is witnessing a paradigm shift. Since auto insurance company consists of homogenous goods thereby making it difficult to differentiate product A from product B, also companies are fighting a price war (for insurance price). On top of that, the distribution channel is shifting more from traditional insurance brokers to online purchases, which means that the ability for companies to interact through human touchpoints is limited, and customers should be quoted at a reasonable price. A good price quote is one that makes the customer purchase the policy and helps the company to increase the profits.

    Also, the insurance premium is calculated based on more than 50+ parameters, which means that traditional business analytics-based algorithms are now limited in their ability to differentiate among customers based on subtle parameters.

    Use Cases:

    1. Conquering Market Share:- Capture market share by lowering the prices of the premium for the customers, who are least likely to claim.
    2. Risk Management:- Charge the right premium from the customer, who is likely to claim insurance in the coming year.
    3. Smooth Processing:- Reduce the complexity of pricing models. Most of the transactions are happening online with larger customer attributes (thanks to the internet and social media). Harness the power of huge data to build complex ML models.
    4. Increased Profits:- As per industry estimate 1% reduction in the claim can boost profit by 10%. So, through the ML model, we can identify and deny the insurance to the driver who will make a claim. Thus, ensuring reduced claim outgo and increased profit.

Data Science, Machine Learning Specialization Features

FAQs

    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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