Watch Intro Video

What you will learn

Overview

  • Key Learnings

    Gain holistic understanding of state-of-the-art machine learning algorithms and their implementation in Python.

  • Capstone Projects

    Complete an end-to-end Capstone Project in which you will solve a real-world problem and build machine learning models.

  • Job Guidance

    Become job ready with dedicated lectures on resume development, case studies, puzzles and one-on-one Mock Interviews.

Methodology

  • All classes will be one-to-one instructor-led live web sessions

  • Lecture materials and Jupyter notebooks will be shared with lectures on same day

  • Post completion of the course, you will receive a certificate of completion

  • This course will take 4 weeks to complete and end with one-on-one mock interview

Curriculum

  • 1

    Change in Era - Human Decision to Machine Learning

    • L01 - Course Overview

    • L01 - Reference File

    • L02 - Types of Learning

    • L02 - Reference File

    • L03 - How to train your machine?

    • L03 - Reference File
  • 2

    Linear Methods of Regression

    • L04 - Linear Regression

    • L04 - Reference File

    • L05 - Multiple Linear Regression

    • L05 - Reference File
  • 3

    Linear Methods of Classification

    • L06 - Logistic Regression

    • L06 - Reference File

    • L07 - Naive Bayes Classifier

    • L07 - Reference File
  • 4

    Make Your Machine Smarter

    • L08 - Model Evaluation Metrics

    • L08 - Reference File

    • L09 - Bias-Variance Trade-off

    • L09 - Reference File

    • L10 - Regularization Techniques

    • L10 - Reference File
  • 5

    Non-Linear Algorithms

    • L11 - Support Vector Machines

    • L11 - Reference File

    • L12 - Kernel Tricks

    • L12 - Reference File

    • L13 - Neural Network Architecture

    • L13 - Reference File

    • L14 - Hyperparameter Tuning in Neural Networks

    • L14 - Reference File
  • 6

    Tree Based Algorithms

    • L15 - Introduction to Decision Trees

    • L15 - Reference File

    • L16 - Ensembles of Decision Trees

    • L16 - Reference File

    • L17 - Random Forest

    • L17 - Reference File

    • L18 - Gradient Boosting Machine

    • L18 - Reference File

    • L19 - Hyperparameter Tuning in Tree Algorithms

    • L19 - Reference File
  • 7

    Discover the Underlying Data Patterns

    • L20 - Principal Component Analysis

    • L20 - Reference File

    • L21 - K-Means Algorithm

    • L21 - Reference File

    • L22 - Hierarchical Clustering

    • L22 - Reference File

    • L23 - Anomaly Detection

    • L23 - Reference File
  • 8

    Feature Engineering Techniques

    • L24 - Engineering Relevant Variables

    • L24 - Reference Files
  • 9

    Recommender Systems

    • L25 - Collaborative Filtering

    • L25 - Reference Files

    • L26 - Content-Based Filtering

    • L26 - Reference Files
  • 10

    Capstone Project - Solution

    • Loan Default Prediction - Kaggle benchmark score solution
  • 11

    Get Job Ready

    • Writing Capstone Project in your Resume

    • Nailing your Data Science Interviews

    • One-on-One Mock Interview
  • 12

    Final Assessment

    • Quiz