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Artificial Intelligence and Machine Learning Fundamentals Online Course

Artificial Intelligence and Machine Learning Fundamentals Training Course

Machine learning and neural networks are fast becoming pillars on which you can build intelligent applications. The course will begin by introducing you to Python and discussing using AI search algorithms. You will learn math-heavy topics, such as regression and classification, illustrated by Python examples.

You will then progress on to advanced AI techniques and concepts, and work on real-life data sets to form decision trees and clusters. You will be introduced to neural networks, which is a powerful tool benefiting from Moore's law applied on 21st-century computing power. By the end of this course, you will feel confident and look forward to building your own AI applications with your newly-acquired skills!

Course Curriculum

Principles of Artificial Intelligence

  • Course Overview
  • Installation and Setup
  • Lesson Overview
  • Introduction to AI and Machine Learning
  • How Does AI Solve Real World Problems?
  • Fields and Applications of Artificial Intelligence
  • AI Tools and Learning Models
  • The Role of Python in Artificial Intelligence
  • A Brief Introduction to the NumPy Library
  • Python for Game AI
  • Breadth First Search and Depth First Search
  • Lesson Summary

AI with Search Techniques and Games

  • Lesson Overview
  • Heuristics
  • Tic-Tac-Toe
  • Pathfinding with the A* Algorithm
  • Introducing the A* Algorithm
  • Game AI with the Minmax Algorithm
  • Game AI with Alpha-Beta Pruning
  • Lesson Summary


  • Lesson Overview
  • Linear Regression with One Variable
  • Fitting a Model on Data with scikit-learn
  • Linear Regression with Multiple Variables
  • Preparing Data for Protection
  • Polynomial and Support Vector Regression
  • Lesson Summary


  • The Fundamentals of Classification Part 1
  • The Fundamentals of Classification Part 2
  • The k-nearest neighbor Classifier
  • Classification with Support Vector Machines
  • Lesson Summary

Using Trees for Predictive Analysis

  • Lesson Overview
  • Introduction to Decision Trees
  • Entropy
  • Gini Impurity
  • Precision and Recall
  • Random Forest Classifier
  • Random Forest Classification Using scikit-learn
  • Lesson Summary


  • Lesson Overview
  • Introduction to Clustering
  • The k-means Algorithm
  • Mean Shift Algorithm
  • Lesson Summary

Deep Learning with Neural Networks

  • Lesson Overview
  • TensorFlow for Python
  • Introduction to Neural Networks
  • Forward and Backward Propagation
  • Training the TensorFlow Model
  • Deep Learning
  • Lesson Summary

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