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⌨️ Introduction to Neural Networks
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Classroom Contents
Python Machine Learning & AI Mega Course - Learn 4 Different Areas of ML & AI
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- 1 ⌨️ Course Introduction
- 2 ⌨️ Introduction to Machine Learning & Environment Setup
- 3 ⌨️ Linear Regression Part 1 – Data Loading and Analysis
- 4 ⌨️ Linear Regression Part 2 – Implementation and Algorithm Explanation
- 5 ⌨️ Saving Models and Visualizing Data
- 6 ⌨️ K-Nearest Neighbors Part 1 – Irregular Data
- 7 ⌨️ K-Nearest Neighbors Part 2 – Algorithm Explanation
- 8 ⌨️ K-Nearest Neighbors Part 3 – Implementation
- 9 ⌨️ Support Vector Machines Part 1 - SkLearn Datasets and Analysis
- 10 ⌨️ Support Vector Machines Part 2 – Algorithm Explanation
- 11 ⌨️ Support Vector Machines Part 3 – Implementation
- 12 ⌨️ K-Means Clustering – Algorithm Explanation
- 13 ⌨️ K-Means Clustering - Implementation
- 14 ⌨️ Introduction to Neural Networks
- 15 ⌨️ Loading & Looking at Data
- 16 ⌨️ Creating a Model
- 17 ⌨️ Using and Testing Our Model
- 18 ⌨️ Text Classification Part 1 – Data Analysis and Model Architecture
- 19 ⌨️ Text Classification Part 2 – Embedding Layers
- 20 ⌨️ Text Classification Part 3 – Training the Model
- 21 ⌨️ Text Classification Part 4 – Saving and Loading Models
- 22 ⌨️ Part 1
- 23 ⌨️ Part 2
- 24 ⌨️ Part 3
- 25 ⌨️ Part 4
- 26 ⌨️ Part 5
- 27 ⌨️ Creating the Bird
- 28 ⌨️ Moving the Bird
- 29 ⌨️ Pixel Perfect Collision
- 30 ⌨️ Finishing the Graphics
- 31 ⌨️ NEAT Introduction and Configuration File
- 32 ⌨️ Implementing NEAT and Fitness Functions
- 33 ⌨️ Testing and Saving Models
