Artificial Intelligence and Machine Learning Fundamentals Online Course

Artificial Intelligence and Machine Learning Fundamentals Online Course

Machine learning and neural networks are rapidly becoming the foundation of intelligent application development. This course is designed to introduce you to the core principles of AI and machine learning, starting with the basics and progressing to more advanced concepts.

You'll begin by learning Python and exploring AI search algorithms. Foundational topics such as regression and classification will be covered in depth, with clear, practical examples implemented in Python to solidify your understanding.

As the course progresses, you'll dive into more complex AI techniques, working with real-world datasets to build decision trees and perform clustering. You'll also be introduced to neural networks—powerful models that leverage modern computing capabilities to solve complex problems.

By the end of the course, you'll have the confidence and foundational knowledge needed to start building your own AI-powered applications.

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

Regression

• 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

Classification

• 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

Clustering

• 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