About the Course

Machine Learning is the fundamentals of Artificial Intelligence. Understanding the concepts, theory, process and mechanism in Machine Learning is the key to understand all the areas in Artificial Intelligence.   

Prerequisites

The following Math knowledge is required to take the Artificial Intelligence courses: 

  • Probability & Statistics
  • Statistics
  • Linear Algebra
  • Calculus

Main Contents

Supervised Learning and Unsupervised Learning
Feature
  • Feature selection
Classification Algorithms
  • Linear classification
Regression Algorithms
  • Linear regression
  • Error metrics
  • Generalization
Support Vector Machines (SVM)
  • Introduction to Support Vector Machines
  • Hyperplane, Kernel trick
  • Objective functions
  • SVM applications
Clustering
  • K-Means clustering
  • Finding clusters in a real dataset
Anomaly Detection
  • Anomaly detection and outliers
  • Anomaly detection algorithms
  • Removing outliers to improve the quality of linear regression predictions
Recommender Systems
  • Collaborative filtering
  • Low-rank matrix factorization
Dimensionality Reduction
Regularization
  • L1 regularization
  • L2 regularization
Machine Learning Pipelines 
AutoML
Machine Learning Applications