Python Machine Learning & AI Mega Course - Learn 4 Different Areas of ML & AI

Embark on a comprehensive 7.5-hour journey through machine learning and artificial intelligence in Python. Explore four distinct areas: fundamental ML algorithms, neural networks, AI chatbots, and game-playing AI. Begin with machine learning basics, covering linear regression, K-nearest neighbors, support vector machines, and K-means clustering. Dive into neural networks, learning about model creation, text classification, and model management. Develop an AI chatbot from scratch, understanding its core components. Finally, create an AI capable of playing Flappy Bird using neuroevolutionary algorithms. Gain hands-on experience with practical implementations, data analysis, and visualization techniques throughout the course.

Syllabus

⌨️ Course Introduction
⌨️ Introduction to Machine Learning & Environment Setup
⌨️ Linear Regression Part 1 – Data Loading and Analysis
⌨️ Linear Regression Part 2 – Implementation and Algorithm Explanation
⌨️ Saving Models and Visualizing Data
⌨️ K-Nearest Neighbors Part 1 – Irregular Data
⌨️ K-Nearest Neighbors Part 2 – Algorithm Explanation
⌨️ K-Nearest Neighbors Part 3 – Implementation
⌨️ Support Vector Machines Part 1 - SkLearn Datasets and Analysis
⌨️ Support Vector Machines Part 2 – Algorithm Explanation
⌨️ Support Vector Machines Part 3 – Implementation
⌨️ K-Means Clustering – Algorithm Explanation
⌨️ K-Means Clustering - Implementation
⌨️ Introduction to Neural Networks
⌨️ Loading & Looking at Data
⌨️ Creating a Model
⌨️ Using and Testing Our Model
⌨️ Text Classification Part 1 – Data Analysis and Model Architecture
⌨️ Text Classification Part 2 – Embedding Layers
⌨️ Text Classification Part 3 – Training the Model
⌨️ Text Classification Part 4 – Saving and Loading Models
⌨️ Part 1
⌨️ Part 2
⌨️ Part 3
⌨️ Part 4
⌨️ Part 5
⌨️ Creating the Bird
⌨️ Moving the Bird
⌨️ Pixel Perfect Collision
⌨️ Finishing the Graphics
⌨️ NEAT Introduction and Configuration File
⌨️ Implementing NEAT and Fitness Functions
⌨️ Testing and Saving Models