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

Ready to explore machine learning and artificial intelligence in python? This python machine learning and AI mega course contains 4 different series designed to teach you the ins and outs of ML and AI. It talks about fundamental ML algorithms, neural networks, creating AI chat bots and finally developing an AI that can play the game of Flappy Bird.

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⭐ RESOURCES ⭐
IMPORTANT: The text-based guides will have download links for files or datasets needed.

1⃣ Machine Learning for Beginners
💻 Text-Based Guide: https://techwithtim.net/tutorials/machine-learning-python/introduction/
💾 UCI Student Data Set: https://archive.ics.uci.edu/ml/datasets/Student+Performance
💾 UCI Car Evaluation Data Set: http://techwithtim.net/wp-content/uploads/2019/01/Car-Data-Set.zip

2⃣ Neural Networks
💻 Text-Based Guide: https://techwithtim.net/tutorials/python-neural-networks/what-is-a-nn/

3⃣ Simple AI Chat Bot
💻 Text-Based Guide: https://techwithtim.net/tutorials/ai-chatbot/
💾 JSON-File Download: https://techwithtim.net/wp-content/uploads/2019/05/json-file.zip

4⃣ Flappy Bird AI
💻 GitHub/Code: https://github.com/techwithtim/NEAT-Flappy-Bird
💾 Images: https://techwithtim.net/wp-content/uploads/2019/08/imgs.zip

⭐ SOFTWARE DOWNLOADS ⭐
🔗 Anaconda Download: https://www.anaconda.com/download/
🔗 Pycharm Download: https://www.jetbrains.com/pycharm/download/#section=windows

⭐ TIMESTAMPS ⭐
🎥 Course 1: Machine Learning for Beginners 🎥
⌨️ (0:00) Course Introduction
⌨️ (00:02:30) Introduction to Machine Learning & Environment Setup
⌨️ (00:12:24) Linear Regression Part 1 – Data Loading and Analysis
⌨️ (00:26:28) Linear Regression Part 2 – Implementation and Algorithm Explanation
⌨️ (00:42:50) Saving Models and Visualizing Data
⌨️(00:56:05) K-Nearest Neighbors Part 1 – Irregular Data
⌨️ (01:08:16) K-Nearest Neighbors Part 2 – Algorithm Explanation
⌨️ (01:21:33) K-Nearest Neighbors Part 3 – Implementation
⌨️ (01:31:54) Support Vector Machines Part 1 - SkLearn Datasets and Analysis
⌨️ (01:38:34) Support Vector Machines Part 2 – Algorithm Explanation
⌨️ (01:52:21) Support Vector Machines Part 3 – Implementation
⌨️(02:01:57) K-Means Clustering – Algorithm Explanation
⌨️ (02:15:11) K-Means Clustering - Implementation

🎥 Course 2: Neural Networks 🎥
⌨️ (02:27:07) Introduction to Neural Networks
⌨️ (02:53:47) Loading & Looking at Data
⌨️ (03:06:50) Creating a Model
⌨️ (03:24:05) Using and Testing Our Model
⌨️ (03:33:56) Text Classification Part 1 – Data Analysis and Model Architecture
⌨️ (03:55:23) Text Classification Part 2 – Embedding Layers
⌨️ (04:09:43) Text Classification Part 3 – Training the Model
⌨️ (04:19:49) Text Classification Part 4 – Saving and Loading Models

🎥 Course 3: AI Chat Bot 🎥
⌨️ (04:34:35) Part 1
⌨️ (04:50:28) Part 2
⌨️ (05:02:39) Part 3
⌨️ (05:14:32) Part 4
⌨️ (05:30:34) Part 5

🎥 Course 4: Neuroevolutionary Algorithm Plays Flappy Bird 🎥
⌨️ (05:39:16) Creating the Bird
⌨️ (05:51:36) Moving the Bird
⌨️ (06:10:04) Pixel Perfect Collision
⌨️ (06:29:22) Finishing the Graphics
⌨️ (06:41:16) NEAT Introduction and Configuration File
⌨️ (07:01:36) Implementing NEAT and Fitness Functions
⌨️ (07:16:32) Testing and Saving Models

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