Prepared to examine machine learning and artificial intelligence in python? This python machine learning and AI mega program has 4 diverse collection developed to train you the ins and outs of ML and AI. It talks about fundamental ML algorithms, neural networks, building AI chat bots and last but not least building an AI that can play the video game of Flappy Chook.
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⭐ Methods ⭐
Significant: The text-centered guides will have down load one-way links for data files or datasets desired.
1⃣ Machine Learning for Beginners
💻 Textual content-Dependent Guide: https://techwithtim.internet/tutorials/machine-finding out-python/introduction/
💾 UCI Scholar Knowledge Established: https://archive.ics.uci.edu/ml/datasets/College student+Performance
💾 UCI Car or truck Evaluation Facts Established: http://techwithtim.net/wp-content/uploads/2019/01/Motor vehicle-Facts-Established.zip
2⃣ Neural Networks
💻 Text-Primarily based Information: https://techwithtim.net/tutorials/python-neural-networks/what-is-a-nn/
3⃣ Very simple AI Chat Bot
💻 Text-Centered Guidebook: https://techwithtim.net/tutorials/ai-chatbot/
💾 JSON-File Download: https://techwithtim.net/wp-information/uploads/2019/05/json-file.zip
4⃣ Flappy Bird AI
💻 GitHub/Code: https://github.com/techwithtim/NEAT-Flappy-Bird
💾 Photographs: https://techwithtim.internet/wp-information/uploads/2019/08/imgs.zip
⭐ Program DOWNLOADS ⭐
🔗 Anaconda Obtain: https://www.anaconda.com/down load/
🔗 Pycharm Down load: https://www.jetbrains.com/pycharm/obtain/#area=windows
⭐ TIMESTAMPS ⭐
🎥 Program 1: Machine Learning for Inexperienced persons 🎥
⌨️ (:00) Course Introduction
⌨️ (00:02:30) Introduction to Machine Learning & Environment Setup
⌨️ (00:12:24) Linear Regression Section 1 – Details Loading and Examination
⌨️ (00:26:28) Linear Regression Part 2 – Implementation and Algorithm Explanation
⌨️ (00:42:50) Conserving Styles and Visualizing Facts
⌨️(00:56:05) K-Nearest Neighbors Aspect 1 – Irregular Data
⌨️ (01:08:16) K-Nearest Neighbors Section 2 – Algorithm Rationalization
⌨️ (01:21:33) K-Closest Neighbors Part 3 – Implementation
⌨️ (01:31:54) Guidance Vector Equipment Component 1 – SkLearn Datasets and Investigation
⌨️ (01:38:34) Assist Vector Machines Aspect 2 – Algorithm Rationalization
⌨️ (01:52:21) Guidance Vector Machines Part 3 – Implementation
⌨️(02:01:57) K-Signifies Clustering – Algorithm Rationalization
⌨️ (02:15:11) K-Usually means Clustering – Implementation
🎥 Training course 2: Neural Networks 🎥
⌨️ (02:27:07) Introduction to Neural Networks
⌨️ (02:53:47) Loading & Searching at Info
⌨️ (03:06:50) Making a Design
⌨️ (03:24:05) Using and Tests Our Design
⌨️ (03:33:56) Text Classification Part 1 – Facts Assessment and Design Architecture
⌨️ (03:55:23) Text Classification Component 2 – Embedding Layers
⌨️ (04:09:43) Text Classification Aspect 3 – Training the Product
⌨️ (04:19:49) Textual content Classification Section 4 – Preserving and Loading Versions
🎥 System 3: AI Chat Bot 🎥
⌨️ (04:34:35) Portion 1
⌨️ (04:50:28) Aspect 2
⌨️ (05:02:39) Element 3
⌨️ (05:14:32) Element 4
⌨️ (05:30:34) Portion 5
🎥 Course 4: Neuroevolutionary Algorithm Performs Flappy Chook 🎥
⌨️ (05:39:16) Producing the Fowl
⌨️ (05:51:36) Relocating the Bird
⌨️ (06:10:04) Pixel Great Collision
⌨️ (06:29:22) Finishing the Graphics
⌨️ (06:41:16) NEAT Introduction and Configuration File
⌨️ (07:01:36) Implementing NEAT and Health and fitness Capabilities
⌨️ (07:16:32) Testing and Conserving Models
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💻 Enroll in The Fundamentals of Programming w/ Python
https://tech-with-tim.teachable.com/p…
