Course curriculum

  • 1
    Welcome to the course!
    • Welcome Message FREE PREVIEW
    • Why should one study Machine Learning?
    • Before we begin... Let us commit our learning goal
    • What topics will be covered in this course?
    • Books for developing multidimensional Machine Learning Skills.
    • My Reading List
  • 2
    Introduction to Machine Learning
    • Algorithmic vs Machine Learning Approach
    • When to use Machine Learning based approach
    • Types of Machine Learning
    • Data Analysis and Data Visualization (Data Types)
    • Undersatnding data through descriptive statistics
    • Data Visualization
    • Bias and Variance Tradeoff
  • 3
    Regression Modelling
    • Linear Regression : Introduction 7 Key Points to remember
    • Linear Regression-II : Statistical Background
    • Estimating Parameters for Simple Linear Regression through Ordinary Least Square (OLS) method
    • Evaluating Simple Linear Regression Model
    • SLR using Gradient Descent Algorithm
    • Multiple Linear Regression-I
    • Multiple Linear Regression-OLS Example
    • Multiple Linear Regression-Gradient Descent Algorithm
    • Linear Regression :Assumptions
    • Data Preprocessing: Feature Scaling
    • Data Pre-Processing: Handling Categorical Data
  • 4
    Classification
    • Classification Introduction
    • Conditional Mean and Sigmoid Function
    • Estimating Parameters: Logistic Regression
    • Logistic Regresssion Gradient Descent Algorithm
    • Logistic Regression: Evaluating Model
    • Linear Discriminant Analysis
    • Multivariate LDA and Quadratic Discriminant Analysis
    • K-Nearest Neighbour and Naive Baye's Classifiers.
  • 5
    Linear Model Selection and Regularization
    • Model Selection : Introduction
    • Ridge and Lasso Regression
    • Principal Component Analysis or Dimension Reduction
  • 6
    First Lab Session
    • Exploratory Data Analysis -I
    • Exploratory Data Analysis Part-II Querying a Data Frame
    • Exploratory Data Analysis Part-III (Data Visualization)
  • 7
    Regression Lab
    • Simple Linear Regression : Lab Activity
    • Simple Linear Regression using Gradient Descent and Scikit-Learn
    • Implementing Multiple Linear Regression using Matrix operation, OLS and Gradient Descent
    • Data Pre-Processing Lab
    • Logistic Regression Lab
    • Linear Discriminant Analysis Lab
    • PCA Lab 1
    • PCA Lab 2