AlphaZero from Scratch – Machine Learning Tutorial
By freeCodeCamp.org
Published: Feb 28, 2023“
In this machine learning course, you will learn how to build AlphaZero from scratch. AlphaZero is a game-playing algorithm that uses artificial intelligence and machine learning techniques to learn how to play board games at a superhuman level.
Trained Models + Code for each Chapter: https://github.com/foersterrobert/AlphaZeroFromScratch
AlphaZero-Paper: https://arxiv.org/pdf/1712.01815.pdfRobert Förster created this course.
Website: https://robertfoerster.com/Contents
- (0:00:00) Introduction
- (0:01:35) Overview – Part 1
- (0:05:43) MCTS-Explained
- (0:27:03) AlphaMCTS-Explained
- (0:39:05) Overview – Part 2
- (0:45:14) Chapter 1: TicTacToe
- (1:00:32) Chapter 2: MCTS
- (1:34:54) Chapter 3: Model
- (2:03:09) Chapter 4: AlphaMCTS
- (2:16:39) Chapter 5: AlphaSelfPlay
- (2:35:13) Chapter 6: AlphaTrain
- (2:47:15) Chapter 7: AlphaTweaks
- (3:08:18) Chapter 8: ConnectFour
- (3:21:48) Chapter 9: AlphaParallel
- (3:55:59) Chapter 10: Eval
Learn to code for free and get a developer job: https://www.freecodecamp.org
Read hundreds of articles on programming: https://freecodecamp.org/news
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Tag: Machine Learning
[#Script #Coding] Self-Driving Car with JavaScript Course – Neural Networks and Machine Learning
Self-Driving Car with JavaScript Course – Neural Networks and Machine Learning
By freeCodeCamp.org
Published: May 12, 2022“
Learn how to create a neural network using JavaScript with no libraries. In this course you will learn to make a self-driving car simulation by implementing every component step-by-step. You will learn how to implement the car driving mechanics, how to define the environment, how to simulate some sensors, how to detect collisions, and how to make the car control itself using a neural network.
The course covers how artificial neural networks work, by comparing them with the real neural networks in our brains. You will learn how to implement a neural network and how to visualize it so we can see it in action.
Dr. Radu Mariescu-Istodor created this course. Check out his channel: https://www.youtube.com/channel/UC3XG…
Code: https://github.com/gniziemazity/Self-…
Course Contents
- (0:00:00) Intro
- (0:03:44) Car driving mechanics
- (0:32:26) Defining the road
- (0:50:50) Artificial sensors
- (1:10:07) Collision detection
- (1:23:20) Simulating traffic
- (1:34:57) Neural network
- (2:03:10) Parallelization
- (2:18:31) Genetic algorithm
- (2:29:40) Ending
Links
- Radu’s website (with enhanced version of code): https://radufromfinland.com
- Radu’s workplace (consider applying): https://karelia.fi/en/front-page
- Segment intersection (Math and JavaScript code): https://youtu.be/fHOLQJo0FjQ
- Visualizing a neural network in JavaScript: https://youtu.be/lok3RVBwSqE
- Visualizer code: https://radufromfinland.com/projects/…
- Drawing random color cars in JavaScript: https://youtu.be/SL6PfDpSjao
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[#Script #Coding] Machine Learning for Everybody – Full Course
Machine Learning for Everybody – Full Course
By freeCodeCamp.org
Published: Sep 26, 2022“
Learn Machine Learning in a way that is accessible to absolute beginners. You will learn the basics of Machine Learning and how to use TensorFlow to implement many different concepts.
Kylie Ying developed this course. Check out her channel: https://www.youtube.com/c/YCubed
Code and Resources
- Supervised learning (classification/MAGIC): https://colab.research.google.com/dri…
- Supervised learning (regression/bikes): https://colab.research.google.com/dri…
- Unsupervised learning (seeds): https://colab.research.google.com/dri…
- Datasets (add a note that for the bikes dataset, they may have to open the downloaded csv file and remove special characters)
- MAGIC dataset: https://archive.ics.uci.edu/ml/datase…
- Bikes dataset: https://archive.ics.uci.edu/ml/datase…
- Seeds/wheat dataset: https://archive.ics.uci.edu/ml/datase…
Google provided a grant to make this course possible.
Contents
- (0:00:00) Intro
- (0:00:58) Data/Colab Intro
- (0:08:45) Intro to Machine Learning
- (0:12:26) Features
- (0:17:23) Classification/Regression
- (0:19:57) Training Model
- (0:30:57) Preparing Data
- (0:44:43) K-Nearest Neighbors
- (0:52:42) KNN Implementation
- (1:08:43) Naive Bayes
- (1:17:30) Naive Bayes Implementation
- (1:19:22) Logistic Regression
- (1:27:56) Log Regression Implementation
- (1:29:13) Support Vector Machine
- (1:37:54) SVM Implementation
- (1:39:44) Neural Networks
- (1:47:57) Tensorflow
- (1:49:50) Classification NN using Tensorflow
- (2:10:12) Linear Regression
- (2:34:54) Lin Regression Implementation
- (2:57:44) Lin Regression using a Neuron
- (3:00:15) Regression NN using Tensorflow
- (3:13:13) K-Means Clustering
- (3:23:46) Principal Component Analysis
- (3:33:54) K-Means and PCA Implementations
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[#Script #Coding] Python TensorFlow for Machine Learning – Neural Network Text Classification Tutorial
Python TensorFlow for Machine Learning – Neural Network Text Classification Tutorial
By freeCodeCamp.org
Published: Jun 15, 2022“
This course will give you an introduction to machine learning concepts and neural network implementation using Python and TensorFlow. Kylie Ying explains basic concepts, such as classification, regression, training/validation/test datasets, loss functions, neural networks, and model training. She then demonstrates how to implement a feedforward neural network to predict whether someone has diabetes, as well as two different neural net architectures to classify wine reviews.
Course created by Kylie Ying.
YouTube: https://youtube.com/ycubed
Twitter: https://twitter.com/kylieyying
Instagram: https://instagram.com/kylieyying/This course was made possible by a grant from Google’s TensorFlow team.
Resources
- Datasets: https://drive.google.com/drive/folder…
- Feedforward NN colab notebook: https://colab.research.google.com/dri…
- Wine review colab notebook: https://colab.research.google.com/dri…
Course Contents
- (0:00:00) Introduction
- (0:00:34) Colab intro (importing wine dataset)
- (0:07:48) What is machine learning?
- (0:14:00) Features (inputs)
- (0:20:22) Outputs (predictions)
- (0:25:05) Anatomy of a dataset
- (0:30:22) Assessing performance
- (0:35:01) Neural nets
- (0:48:50) Tensorflow
- (0:50:45) Colab (feedforward network using diabetes dataset)
- (1:21:15) Recurrent neural networks
- (1:26:20) Colab (text classification networks using wine dataset)
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[#Script #Coding] R Shiny for Data Science Tutorial – Build Interactive Data-Driven Web Apps
R Shiny for Data Science Tutorial – Build Interactive Data-Driven Web Apps
By freeCodeCamp.org
Published: Sep 22, 2021“
Learn how to build interactive data-driven web apps in R using the Shiny package.
Course developed by Chanin Nantasenamat (aka Data Professor). Check out his YouTube channel for more bioinformatics and data science tutorials: https://www.youtube.com/dataprofessor
Code
- Apps 1-5: https://github.com/dataprofessor/rshiny_freecodecamp
- Deploy Shiny App: https://github.com/dataprofessor/iris…
- Medium blog posts for more data science tutorials https://data-professor.medium.com/
- For updates connect via Newsletter: http://newsletter.dataprofessor.org/
- Twitter: https://twitter.com/thedataprof/
Course Contents
- (0:00:00) Introduction
- (0:01:13) Introduction to Shiny
- (0:08:24) App 1 – Print User Input
- (0:21:12) App 2 – Display Histogram
- (0:32:07) App 3 – Machine Learning (Weather Dataset)
- (0:47:51) App 4 – Machine Learning (Iris Dataset)
- (1:05:03) App 5 – BMI Calculator
- (1:19:18) Deploy Shiny Apps to Heroku
Learn to code for free and get a developer job: https://www.freecodecamp.org
Read hundreds of articles on programming: https://freecodecamp.org/news
Subscribe for new videos on technology every day: https://youtube.com/subscription_cent…”
[#Script #Coding] Machine Learning Course for Beginners
Machine Learning Course for Beginners
By freeCodeCamp.org
Published: Aug 30, 2021“
Learn the theory and practical application of machine learning concepts in this comprehensive course for beginners.
- Course website with learning resources: https://antern.co/pages/ml001.html
- Code: https://github.com/ayush714/ML001-Project-Sources-Code-and-Learning-Materials
Course developed by Ayush Singh. Check out his channel: https://www.youtube.com/c/neweraa
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