Tech With Tim created a Python web scraping tutorial that shows you how to build an awesome python project, a coronavirus web scraper and python voice assistant.

He starts by scraping covid-19 data from a website then build a voice assistant that can answer coronavirus related questions.

Code Download: https://github.com/techwithtim/Cornavirus-Voice-Assistant

Are you ready to explore machine learning and artificial intelligence in python?

This free Python machine learning and AI mega course contains 4 different series designed to teach you the ins and outs of ML and AI.

It covers about fundamental ML algorithms, neural networks, creating AI chat bots and finally developing an AI that can play the game of Flappy Bird.

RESOURCES

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

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 

Neural Networks

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

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

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

TIMESTAMPS

Course 1: Machine Learning for Beginners
⌨️ (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