Course curriculum

  • 1

    Introduction to the Course

    • Introduction to the Course
    • Focus of the Course
    • Python Practical of the Course
  • 2

    Why Machine Learning

    • Machine Learning Applications-Part 1
    • Machine Learning Applications-Part 2
    • Why Machine Learning is Trending Now
  • 3

    Process of Learning from Data

    • Supervised Learning
    • UnSupervised Learning and Reinforcement Learning
  • 4

    Machine Learning Methods

    • Features
    • Features Practice with Python
    • Regression
    • Regression Practice with Python
    • Classsification
    • Classification Practice with Python
    • Clustering
    • Clustering Practice with Python
  • 5

    Data Preparation and Preprocessing

    • Handling Image Data
    • Handling Video and Audio Data
    • Handling Text Data
    • One Hot Encoding
    • Data Standardization
  • 6

    Machine Learning Models and Optimization

    • Machine Learning Model 1
    • Machine Learning Model 2
    • Machine Learning Model 3
    • Training Process, Error, Cost and Loss
    • Optimization
  • 7

    Building Machine Learning Model from Scratch

    • Linear Regression from Scratch- Part 1
    • Linear Regression from Scratch- Part 2
    • Minimun-to-mean Distance Classifier from Scratch- Part 1
    • Minimun-to-mean Distance Classifier from Scratch- Part 2
    • K-means Clustering from Scratch- Part 1
    • K-means Clustering from Scratch- Part 2
  • 8

    Overfitting, Underfitting and Generalization

    • Overfitting Introduction
    • Overfitting example on Python
    • Regularization
    • Generalization
    • Data Snooping and the Test Set
    • Cross-validation
  • 9

    Machine Learning Model Performance Metrics

    • The Accuracy
    • The Confusion Matrix
  • 10

    Dimensionality Reduction

    • The Curse of Dimensionality
    • The Principal Component Analysis (PCA)
  • 11

    Deep Learning Overview

    • Introduction to Deep Neural Networks (DNN)
    • Introduction to Convolutional Neural Networks (CNN)
    • Introduction to Recurrent Neural Networks (CNN)
  • 12

    Hands-on Machine Learning Project Using Scikit-Learn

    • Principal Component Analysis (PCA) with Python
    • Pipeline in Scikit-Learn for Machine Learning Project
    • Cross-validation with Python
    • Face Recognition Project with Python
  • 13

    OPTIONAL Section- Mathematics Wrap-up

    • Mathematical Wrap-up on Machine Learning