Overview

  • Key Learnings

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

  • Capstone Projects

    Complete two end-to-end Capstone Projects in which you will solve real-world problems 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

  • Lectures are released on a daily basis with relevant practise assignment and quiz

  • 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 8 weeks to complete and ends with one-on-one mock interview

Curriculum

  • 1

    Day 1 : Data Science in Industrial Setup - Why, What, How?

  • 2

    Day 2 : Python - Essential Toolkit for a Data Scientist

    • Today's Agenda

    • Exploring Data Types in Python

    • Strings

    • Boolean Variables

    • Today's Summary

    • Lecture Materials & Practise Assignment

    • Practise Quiz

  • 3

    Day 3 : Basic Units of the Python Universe

    • Today's Agenda

    • Introduction to Lists

    • List Indexing & Slicing

    • List Functions & Methods

    • Tuples

    • Sets

    • Dictionary Objects

    • Indentation in Python

    • If elif else statements

    • For Loop

    • While Loop

    • Today's Summary

    • Lecture Materials & Practise Assignment

    • Practise Quiz

  • 4

    Day 4 : Introduction to Methods & Functions

    • Today's Agenda

    • Methods

    • Functions

    • Scope

    • Today's Summary

    • Lecture Materials & Practise Assignment

    • Practise Quiz

  • 5

    Day 5 : Writing Production Grade Code

    • Today's Agenda

    • What is Production Environment?

    • Object Oriented Programming

    • Methods

    • Error & Exception Handling

    • Real World Example

    • Today's Summary

    • Lecture Materials & Practise Assignment

    • Practise Quiz

  • 6

    Day 6 : Facing off with Linear Algebra

    • Today's Agenda

    • Linear Algebra & Its Applications

    • Vectors

    • Matrices

    • Today's Summary

    • Reference files

    • Lecture Materials & Practise Assignment

    • Additional Reading Material

    • Practise Quiz

  • 7

    Day 7 : Calculus

    • Today's Agenda

    • Introduction to Calculus

    • Today's Summary

    • Reference files (PDF)

    • Additional Reading Material

    • Practise Quiz

  • 8

    Day 8 : Probability Theory

    • Today's Agenda

    • Concepts in Probability

    • Random Variables and its Types

    • Probability Mass Function

    • Probability Density Function

    • Today's Summary

    • Additional Reading Material

    • Practise Quiz

  • 9

    Day 9 : Statistics - Measures of Central Tendency & Spread

    • Today's Agenda

    • Measures of Central Tendency

    • Measures of Spread

    • Symmetry and Skewness

    • Reference files(ppt)

    • Mean, Median, Mode, Variance & IQR - Python Implementation

    • Today's Summary

    • Additional Reading Material

    • Practise Quiz

  • 10

    Day 10 : Statistics - Hanging in with Statistical Distributions

    • Today's Agenda

    • Discrete Statistical Distributions

    • Continuous Statistical Distributions

    • Today's Summary

    • Additional Reading Material

    • Practise Quiz

  • 11

    Day 11 : Statistics - Covariance, Correlation & Chi-Squared

    • Today's Agenda

    • Explanatory vs Response Variable

    • Covariance & Correlation

    • Chi - Squared

    • Today's Summary

    • Reference files (PPT & Chi - Squared table)

    • Practise Quiz

  • 12

    Day 12 : Numpy - Operations on Arrays

    • Today's Agenda

    • Why Numpy

    • List vs Array

    • Array Inspection

    • Placeholders

    • Array Indexing and Slicing

    • Array Manipulation

    • Basic Operations

    • Real World problem: Operations on Image

    • Today's Summary

    • Lecture Materials & Practise Assignment

    • Practise Quiz

  • 13

    Day 13 : Pandas - Chug Data, Spit Frames

    • Today's Agenda

    • Pandas package - Introduction

    • Introduction to Series and Data Frame

    • Load csv, xlsx and json format

    • Saving file to location

    • Today's Summary

    • Lecture Materials & Practise Assignment

    • Practise Quiz

  • 14

    Day 14 : Pandas - Operations on Dataframes

    • Today's Agenda

    • Data Frame Inspection

    • Indexing and Slicing

    • Manipulating Columns

    • Merging dataframes

    • Unique and missing values

    • Groupby

    • Today's Summary

    • Lecture Materials & Practise Assignment

    • Practise Quiz

  • 15

    Day 15 : Matplotlib - Data Visualization

    • Today's Agenda

    • Matplotlib & Seaborn : Worth 1000 words

    • Line Plot

    • Bar Plot

    • Box Plot

    • Today's Summary

    • Lecture Materials & Practise Assignment

    • Practise Quiz

  • 16

    Day 16 : Seaborn - Data Visualization

    • Today's Agenda

    • Histogram

    • Understanding Correlation

    • Correlation Heatmap

    • Two-way plots

    • Today's Summary

    • Lecture Materials & Practise Assignment

    • Practise Quiz

  • 17

    Day 17 : Capstone Project - I

    • Defining the Problem Statement

    • Capstone Project

  • 18

    Day 18 : Machine Learning Essentials

    • Machine Learning Overview

    • Supervised Learning

    • Unsupervised Learning

    • Reinforcement Learning

    • Steps for Supervised Machine Learning Modelling

    • Deep Dive in Supervised Machine Learning Modelling

    • Effective approach for training any Machine Learning algorirthm

    • Practise Quiz

  • 19

    Day 19 : Linear Regression

    • Implemention of Linear Regression in Python

    • Lecture Materials & Practise Assignment

    • Practise Quiz

  • 20

    Day 20 : Multiple Linear Regression

    • Definition of Multiple Linear Regression

    • Assumptions of Multivariate Linear Regression

    • Implementing Multiple Linear Regression in Python

    • Concept of Gradient Descent

    • Lecture Materials & Practise Assignment

    • Practise Quiz

  • 21

    Day 21 : Logistic Regression

    • Classification with Logistic Regression

    • Implementing Logistic Regression in Python

    • Lecture Materials & Practise Assignment

    • Practise Quiz

  • 22

    Day 22 : Naive Bayes Classifier

    • Introduction to Naive Bayes Algorithm

    • Implementation of Naive Bayes classifier in Python

    • Lecture Materials & Practise Assignment

    • Practise Quiz

  • 23

    Day 23 : Model Evaluation Metrics

    • Metrics for evaluating classification models

    • Metrics for evaluating regression models

    • Lecture Materials & Practise Assignment

    • Practise Quiz

  • 24

    Day 24 : Bias-Variance Trade-off

    • Training Machine Learning Models

    • Overview of Bias-Variance tradeoff

    • Error - Mathematical Representation

    • Bias - Variance tradeoff

    • Ways to balance the Bias-Variance tradeoff

    • Practise Quiz

  • 25

    Day 25 : Regularization Techniques

    • Introduction to Regularization Techniques

    • Predicting sales using Regularization Techniques

    • Lecture Materials & Practise Assignment

    • Practise Quiz

  • 26

    Day 26 : Support Vector Machines

    • Support Vector Machines

    • Lecture Materials & Practise Assignment

    • Practise Quiz

  • 27

    Day 27 : Kernel Tricks

    • Kernel Tricks

    • Lecture Materials & Practise Assignment

    • Practise Quiz

  • 28

    Day 28 : Neural Network Architecture

    • What is a Neural Network?

    • Implementing Neural Networks in Python

    • Lecture Materials & Practise Assignment

    • Practise Quiz

  • 29

    Day 29 : Hyperparameter Tuning in Neural Networks

    • Hyperparameter Tuning in Neural Networks

    • Lecture Materials & Practise Assignment

    • Practise Quiz

  • 30

    Day 30 : Introduction to Decision Trees

    • Introduction to Decision Trees

    • Predicting humidity using Decision Trees

    • Lecture Materials & Practise Assignment

    • Practise Quiz

  • 31

    Day 31 : Ensembles of Decision Trees

    • Why ensemble of Decision Trees?

    • Different ensembling methods

    • Bagging vs Boosting

    • Advantages and Disadvantages of ensembling

    • Lecture Materials & Practise Assignment

    • Practise Quiz

  • 32

    Day 32 : Random Forest

    • Introduction to Random Forest Algorithm

    • Random Forest Implementation in Python

    • Lecture Materials & Practise Assignment

    • Practise Quiz

  • 33

    Day 33 : Gradient Boosting Machine

    • Introduction to Gradient Boosted Machine

    • Implementing GBM in Python

    • Lecture Materials & Practise Assignment

    • Practise Quiz

  • 34

    Day 34 : Hyperparameter Tuning in Tree Algorithms

    • Hyperparameter Tuning in Tree Algorithms

    • Lecture Materials & Practise Assignment

    • Practise Quiz

  • 35

    Day 35 : Principal Component Analysis

    • Introducing Principal Component Analysis

    • PCA as Dimensionality Reduction Technique

    • PCA for visualization

    • Choosing the number of components

    • Lecture Materials & Practise Assignment

    • Practise Quiz

  • 36

    Day 36 : K-Means Algorithm

    • Introduction to K-Means algorithm

    • Identify similar handwritten digit using K-Means

    • Lecture Materials & Practise Assignment

    • Practise Quiz

  • 37

    Day 37 : Hierarchical Clustering

    • Introduction to Hierarchical Clustering

    • Customer segmentation using Hierarchical Clustering

    • K-Means vs Hierarchical Clustering

    • Lecture Materials & Practise Assignment

    • Practise Quiz

  • 38

    Day 38 : Anomaly Detection

    • Univariate Anomaly Detection

    • Multivariate Anomaly Detection

    • Lecture Materials & Practise Assignment

    • Practise Quiz

  • 39

    Day 39 : Engineering relevant variables

    • Engineering relevant variables

    • Lecture Materials & Practise Assignment

    • Practise Quiz

  • 40

    Day 40 : Recommender Systems - Collaborative Filtering

    • Collaborative Filtering

    • Implementation of Collaborative Filtering in Python

    • Lecture Materials & Practise Assignment

    • Practise Quiz

  • 41

    Day 41 : Recommender Systems - Content-based Filtering

    • Content-based Filtering

    • Implementing Content Based Filtering in Python

    • Lecture Materials & Practise Assignment

    • Practise Quiz

  • 42

    Day 42 : Industry Practices in ML projects

    • Orthogonalization, Metric of Interest, Sizing datasets

    • Structuring a ML Project, Distribution matching

    • Lecture Materials & Practise Assignment

    • Practise Quiz

  • 43

    Day 43 : Sampling Methods and Handling Class Imbalance

    • Sampling Methods

    • Handling Class Imbalance

    • Lecture Materials & Practise Assignment

    • Practise Quiz

  • 44

    Day 44 : Introduction to Deep Learning

    • Why Deep Learning?

    • Practise Quiz

  • 45

    Day 45 : Capstone Project - II

    • Defining the Problem Statement

    • Reference files and data

  • 46

    Day 46 : Capstone Project - II

    • Today's Hint

    • Materials & Practise Notebook

  • 47

    Day 47 : Capstone Project - II

    • Today's Hint

    • Materials & Practise Notebook

  • 48

    Day 48 : Capstone Project - II

    • Today's Hint

    • Materials & Practise Notebook

  • 49

    Day 49 : Capstone Project - II - Solution

    • Capstone Project - Solution

  • 50

    Day 50 : Know all about your job search

    • Job Hunt Overview

    • Platforms to build your job profile

  • 51

    Day 51 : Let companies know you are ready

    • Resume Building

    • Identify right Job and Company for you

    • Handling screening questions

  • 52

    Day 52 : Armour - Case Study

    • Case Study - Data Science

    • Practice Case Study

  • 53

    Day 53 : Armour - Puzzles and Guesstimate

    • How to approach the Problem?

  • 54

    Day 54 : Engage and lead your case

    • Tackle Telephonic Interview

  • 55

    Day 55 : Nail your interviews

    • Tackle face to face Interview

  • 56

    Day 56 : Schedule Mock Interview

    • Schedule Mock Interview