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Full-Stack Machine Learning Program

Start your Data Science journey by learning the core data science concepts, along with applying them in exclusive real-time cases, from industry experts and experienced faculties.
Course Overview

 

Learn essential data science techniques using Python to analyze the data and help make your organization take important decisions. Explore popular classification, regression, clustering, etc. algorithms using many hands-on exercises. By the end of this module, you will be able to transform your theoretical knowledge into practical by looking at real-life business scenarios.
 
What you will learn:
  • Installing Anaconda
  • Learning Coding Practices
  • Python Variables & Data Types
  • Python Operators
  • Python Conditional Statements & Loops
  • Python Strings
  • Python Data Structures
  • Python UDFs & Lambda Functions
  • NumPy
  • Pandas Series
  • Pandas
  • DataFrames
  • Data Visualization
  • Regular Expressions
  • Exploratory Data Analysis with Python
  • What is Data Science, ML, AI
  • Why Data Science, ML, AI
  • Business Case Studies
  • How to prepare for a career switch
  • Freshers: How to study for interviews
  • Experienced: How to study for interviews
  • Why do we need statistics for Data Science?
  • Measures of Central Tendencies
  • of Dispersion
  • Central Limit Theorem
  • How do I apply these concepts?
  • Commonly used distributions – Binomial, Poisson, Geometric and Normal
  • Probability Theory
  • Why do we need probability in Data Science?
  • Naive Bayesian
  • Hypothesis Testing
  • Can I apply them in real life? Yes
  • Linear Regression
  • Understanding how Linear Regression (OLS) works
  • Easy-to-Understand Maths for Linear Regression
  • Data Encoding – Why Encoding? How to Encode? What Encoding?
  • Assumption of Normality
  • Assumption of Autocorrelation
  • Assumption of Heteroskedasticity
  • Interpreting A-Z of Linear Regression Output
  • Feature Engineering
  • Understanding Performance Metrics
  • Hands-on: Basic Case Study
  • Hands-on: Advanced Case Study demonstrating real-life challenges
  • Logistic Regression
  • Understanding how Logistic Regression (MLE) works
  • Easy-to-Understand Maths for Logistic Regression
  • Interpreting A-Z of Logistic Regression Output
  • Understanding Performance Metrics
  • Hands-on: Basic Case Study
  • Hands-on: Advanced Case Study demonstrating real-life challenges
  • Types of Learning
  • Supervised Learning
  • Unsupervised Learning
  • Semi-Supervised Learning
  • How do I know which algorithm to apply?
  • Understanding Bias & Variance
  • Understanding Underfitting & Overfitting
  • How do I overcome Bias & Variance in real projects?
  • Understanding Loss Functions for different problems
  • Understanding & fine-tuning Hyperparameters
  • Understanding Cross-Validation
  • Optimization Techniques
  • Local Optimization vs Global Optimization
  • Stochastic Gradient Descent
  • Batch & Mini-Batch Gradient Descent
  • Momentum, AdaGrad, RMSProp
  • ADAM Optimization
  • Hands-on: Revisiting Linear Regression with Optimization Algorithms
  • Hands-on: Revisiting Logistic Regression with Optimization Algorithms
  • Why do I need Optimization Techniques?
  • Decision Tree for Supervised Learning Problems
  • Understanding how Decision Tree works
  • Easy-to-Understand Maths for Decision Tree
  • Understanding Gini Index and Information Entropy
  • Hands-on: Case Study using Decision Tree Algorithm
  • Hands-on: Overcoming Challenges in Decision Trees
  • Practical Key Takeaways
  • K-Nearest Neighbour
  • Understanding how KNN works
  • Understanding the distance metrics
  • Easy-to-Understand Maths for KNN
  • Determining when can we not use KNN appropriately
  • Support Vector Machines
  • Understanding how SVM works
  • Easy-to-Understand Maths for SVM
  • Understanding when & how SVM outperforms other algorithms
  • Understanding SVM Kernels
  • Hands-on: Case Study with SVM & SVM Kernels
  • Understanding when do you not need a kernel
  • Understanding which kernel to use
  • K-Means Clustering
  • Revisiting Distance Metrics
  • Understanding how K-Means Clustering works
  • Easy-to-Understand Maths for K-Means Clustering
  • Hands-on: Clustering using K-Means Algorithm
  • Practical Applications
  • Hierarchical Clustering
  • Understanding how Hierarchical Clustering works
  • Easy-to-Understand Maths for Hierarchical Clustering
  • Hands-on: Clustering using Hierarchical Algorithm
  • Practical Applications
  • Apriori Algorithm
  • Understanding how Apriori Algorithm works
  • Easy-to-Understand Maths for Apriori Algorithms
  • Understanding Metrics for Association Rules
  • Hands-on: Market Basket Analysis using Apriori Algorithm
  • Practical Applications
  • Random Forest
  • Understanding how Random Forest works
  • Why Random Forest?
  • Random Forest Hyperparameters
  • Fine-tuning Hyperparameters using Grid-Search
  • Hands-on: Case Study
  • Boosting Algorithms
  • Understanding how Boosting Algorithms works
  • Easy-to-Understand Maths behind Boosting Algorithms
  • Deep dive into Adaboost Algorithm
  • Deep dive in Gradient Boosting Method
  • Hands-on: Case Study on Adaboost & gradient Boosting Method
  • Heterogeneous Ensembles
  • Stack Generalization
  • Artificial Neural Networks
  • How ANN Works
  • Easy-to-Understand Maths behind ANN
  • ANN Hyperparameters
  • ANN from scratch
  • Hands-on: Case Study on ANN
  • Convolutional Neural Networks
  • Convolution network vs plain neural network
  • Convolution
  • Locally Connected Layer
  • Transitional Invariance
  • Convolutions (Discrete 1D)
  • Spatial dimension – convolutions
  • Convolution- backward
  • Recurrent Neural Networks
  • What are RNNs?
  • Forward pass in RNN
  • Backward pass in RNN
  • Vanishing and exploding gradient problem
  • Different types of RNN architecture
  • Bi-directional RNNs
  • Advantages and disadvantages of RNN
  • Application area of RNN
  • Natural Language Processing
  • NLP Foundation
  • Cleaning Text Manually
  • Cleaning Text using NLTK
  • Preparing Text with Scikit-Learn
  • Preparing Text with Keras
  • Bag of Words
  • Basic Neural Model for Sentiment Analysis
  • The n-gram CNN Model for Sentiment Analysis
  • Word Embeddings
  • Text Classification using CNN
  • Language Modeling
  • Develop a Neural Language Model for Text generation
  • Image Captioning using Pre-Trained Models
  • Computer Vision
  • Introduction to Computer Vision
  • Working with Images
  • Manipulating Images with PIL
  • Manipulating Images with Keras
  • Scaling Images with Keras
  • Image Augmentation
  • Manipulating & Ordering Image Channels
  • How Convolutional Neural works
  • Using Filter, Padding, Stride, Pooling
  • ImageNet Large Scale Visual Recognition
  • Milestone Model Architectures Implementations
  • Managing Model Complexity using 1X1 Convolutions
  • Using Pre-Trained Models, Transfer Learning
  • Image Classification
  • Object Detection
  • Face Recognition
  • Understanding Git
  • ML Model Persistence
  • ML Model Deployments using Python frameworks
  • ML Model Deployments using Cloud Platform
  • Understanding Big Data Ecosystem
  • Learning PySpark
  • Building ML Models using PySpark
Schedule

Weekdays (Mon – Fri)
Mar 21 – Dec 21, 2022

Training Timing:
07:00 AM – 09:00 AM (IST)

Faculty Member
Dipayan Sarkar

Senior Data Scientist, Ex-Maersk

Adjunct Faculty, Great Learning, Chennai and BML Munjal University, Gurgaon

With over 20 years of experience, Dipayan has a wealth of knowledge under his belt when it comes to analytics in real-world situations. Prior to being an independent consultant and a mentor in the Data Science, Machine Learning (ML), and AI space with various organizations, universities, and educational institutions, he served in the capacity of a senior data scientist with various Fortune 500 companies. He also holds a Masters in Economics and is currently pursuing doctoral program in artificial intelligence from Rennes School of Business, France

Over the last decade, he has focused his analytics expertise in the fields of Data Science, ML, and AI as he has found how passion, technology, and analytics can be combined to create products that are essential in today’s business environment. Dipayan takes a keen interest in the mathematics behind the ML techniques.

While consulting for corporations on their Data Science and Analytics requirements, he is also a current content developer, mentor, and trainer in the Data Science, ML, and AI space with premier institutions in India & Singapore.

Course Certificate

Start your journey today!

₹ 206500