- Andrej Karpathy blog
- Brandon Roher
- Andrew Trask
- Jay Alammar
- Sebastian Ruder
- Distill
- StatQuest with Josh Starmer
- sentdex
- Lex Fridman
- 3Blue1Brown
- Alexander Amini
- The Coding Train
- Coimbatore School of AI Join here🔗⬇️
- TensorFlow User Group Coimbatore
- 🖥️ HOW TO GET STARTED WITH MACHINE LEARNING!
- How to Build a Meaningful Career in Data Science
- My Self-Created Artificial Intelligence Masters Degree
- PyImageSearch
- 5 Beginner Friendly Steps to Learn Machine Learning and Data Science with Python
- Luis Serrano: A Friendly Introduction to Machine Learning
- StatQuest: A Gentle Introduction to Machine Learning
- Teachable Machine Train a computer to recognize your own images, sounds, & poses. A fast, easy way to create machine learning models for your sites, apps, and more – no expertise or coding required.
- Machine Learning by Andrew Ng, Stanford IMDB 10/10 LOL :P
- Datacamp : Data Engineer with Python
- Intro to Machine Learning Topics Covered Naive Bayes, SVM, Decision Trees, Regressions, Outliers, Clustering, Feature Scaling, Text Learning, Feature Selection, PCA, Validation, Evaluation Metrics
- Intro to TensorFlow for Deep Learning The Best Course for Learning TensorFlow
- End-to-End Machine Learning
- NVIDIA DEEP LEARNING INSTITUTE
- Introduction to Machine Learning for Coders!
- Practical Deep Learning for Coders, v3
- FastAI
- Bias and Variance
- Cross Validation
- Machine Learning Fundamentals: The Confusion Matrix
- Sensitivity and Specivicity
- ROC and AUC, Clearly Explained!
- StatQuest: R-squared explained
- StatQuest: P Values, clearly explained
- Regularization Part 1: Ridge Regression
- Regularization Part 2: Lasso Regression
- Maximum Likelihood
- Covariance and Correlation Part 1: Covariance
- Statistics Fundamentals: The Mean, Variance and Standard Deviation
- Statistics Fundamentals: Population Parameters
- Glossary: Statistics
- Glossary: Machine Learning
- Mathematics for Machine Learning In this post I have compiled great e-resources (MOOC, YouTube Lectures, Books) for learning Mathematics for Machine Learning.
- I highly Recommend you to go through the following resources by 3Blue1Brown
- Gilbert Strang: Linear Algebra vs Calculus
▶️ - Basics of Integral Calculus in Tamil
▶️ - New fast.ai course: Computational Linear Algebra
- Linear Algebra Book
- A Visual Intro to NumPy and Data Representation
- CS231n : Python Numpy Tutorial
- NumPy resources : part of the End-to-End Machine Learning library
- 100 numpy exercises (with solutions)
- 101 NumPy Exercises for Data Analysis (Python)
- Numpy Tutorial – Introduction to ndarray
- Sci-Py Lectures : NumPy: creating and manipulating numerical data
- Python NumPy Tutorial for Beginners
▶️ Learn the basics of the NumPy library in this tutorial for beginners. It provides background information on how NumPy works and how it compares to Python's Built-in lists. This video goes through how to write code with NumPy. It starts with the basics of creating arrays and then gets into more advanced stuff. The video covers creating arrays, indexing, math, statistics, reshaping, and more. - Python NumPy Tutorial – Learn NumPy Arrays With Examples
- Python Numpy Array Tutorial
- NumPy Tutorial: Data analysis with Python
- Deep Learning Prerequisites: The Numpy Stack in Python
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- A Gentle Visual Intro to Data Analysis in Python Using Pandas
- Data analysis in Python with pandas by Data School
▶️ - Best practices with pandas by Data School
▶️ - 10 minutes to pandas
- Python Pandas Tutorial: A Complete Introduction for Beginners
- CodeBasics: Machine Learning Tutorial Python
▶️ - StatQuest: Machine Learning
▶️ - sentdex: Machine Learning with Python
▶️ - Simplilearn: Machine Learning Tutorial Videos
▶️ - Machine Learning Tutorial in Python
▶️ - deeplizard: Machine Learning & Deep Learning Fundamentals
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Note: Below you can find the best lectures for popular Machine Learning Algorithms
- Linear Regression: A friendly introduction by Luis Serrano
▶️ - Statistics 101: Linear Regression, The Very Basics
▶️ - All Types of Regression📙
- Linear Regression vs Logistic Regression | Data Science Training | Edureka
▶️ - Logistic Regression and the Perceptron Algorithm: A friendly introduction by Luis Serrano
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- StatQuest: Decision Trees
▶️ - StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data
▶️ - Decision Tree Introduction with example📙
- Decision Tree📙
- Python | Decision Tree Regression using sklearn📙
- ML | Logistic Regression v/s Decision Tree Classification📙
- StatQuest: Random Forests Part 1 - Building, Using and Evaluating
▶️ - StatQuest: Random Forests Part 2: Missing data and clustering
▶️ - Random Forests for Complete Beginners📙
- Support Vector Machines (SVMs): A friendly introduction by Luis Serrano
▶️ - Support Vector Machines, Clearly Explained!!! by StatQuest
▶️ - Support Vector Machines Part 2: The Polynomial Kernel by StatQuest
▶️ - Support Vector Machines Part 3: The Radial (RBF) Kernel by StatQuest
▶️ - How Support Vector Machines work / How to open a black box
▶️ - Support Vector Machines - The Math of Intelligence (Week 1)
▶️ - Demystifying Support Vector Machines📙
- Support Vector Machine (SVM) - Fun and Easy Machine Learning
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- Bayes theorem, and making probability intuitive
▶️ - A friendly introduction to Bayes Theorem and Hidden Markov Models
▶️ - The Bayesian Trap
▶️ - Naive Bayes classifier: A friendly approach
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- StatQuest: K-means clustering
▶️ - Machine Learning Tutorial Python - 13: K Means Clustering
▶️ - K Means Clustering Algorithm - K Means Example in Python - Machine Learning Algorithms - Edureka
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- StatQuest: PCA main ideas in only 5 minutes!!!
▶️ - StatQuest: Principal Component Analysis (PCA), Step-by-Step
▶️ - Principal Component Analysis (PCA) by Luis Serrano
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- Boosting Machine Learning Tutorial | Adaptive Boosting, Gradient Boosting, XGBoost | Edureka
▶️ - XGBoost Part1: XGBoost Trees for Regression
▶️ - XGBoost Part 2: XGBoost Trees For Classification
▶️ - AdaBoost, Clearly Explained
▶️ - Gradient Boost Part 1: Regression Main Ideas
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- Linear Regression using Gradient Descent📙
- An overview of gradient descent optimization algorithms📙
- Gradient Descent, Step-by-Step
▶️ - Stochastic Gradient Descent, Clearly Explained!!!
▶️ - How Optimization Works A short series on the fundamentals of optimization for machine learning
- Linear Regression using Gradient Descent
- Polynomial Regression
- Gradient Descent in Linear Regression - Math📙
- Neural Network Backpropagation Basics For Dummies
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- 3.4: Linear Regression with Gradient Descent - Intelligence and Learning
▶️ - 3.5: Mathematics of Gradient Descent - Intelligence and Learning
▶️ - 3.5a: Calculus: Power Rule - Intelligence and Learning
▶️ - 3.5b: Calculus: Chain Rule - Intelligence and Learning
▶️ - 3.5c: Calculus: Partial Derivative - Intelligence and Learning
▶️
- https://datascience.stackexchange.com/questions/24534/does-gradient-descent-always-converge-to-an-optimum
- https://datascience.stackexchange.com/questions/18802/does-mlp-always-find-local-minimum
- https://www.coursera.org/learn/deep-neural-network/lecture/RFANA/the-problem-of-local-optima
- An introduction to machine learning with scikit-learn📙
- Python Machine Learning: Scikit-Learn Tutorial
- DEEP BLUEBERRY BOOK This is a tiny and very focused collection of links about deep learning. If you've always wanted to learn deep learning stuff but don't know where to start, you might have stumbled upon the right place!
- 6.S191: Introduction to Deep Learning (2019)
- MIT 6.S191 Introduction to Deep Learning (2020)
- MIT Deep Learning Basics: Introduction and Overview
- MIT Deep Learning by Lex Fridman
- Deep Learning in Tamil
- The Deep Learning Textbook from Ian Goodfellow, Yoshua Bengio, and Aaron Courville
- Neural Networks And Deep Learning by Michael Nielsen
- Grokking Deep Learning by Andrew Trask
- Machine Learning for Beginners: An Introduction to Neural Networks A simple explanation of how they work and how to implement one from scratch in Python.
- A Visual and Interactive Guide to the Basics of Neural Networks
- A Visual And Interactive Look at Basic Neural Network Math
- Neural Network Architectures
- Neural Networks Demystified by Welch Labs
- CS131 Computer Vision: Foundations and Applications Fall 2019
- CS231A: Computer Vision, From 3D Reconstruction to Recognition Winter 2018
- CS231n Convolutional Neural Networks for Visual Recognition
- CS231n: Convolutional Neural Networks for Visual Recognition Spring 2019
- CS231n: Convolutional Neural Networks for Visual Recognition
- A friendly introduction to Convolutional Neural Networks and Image Recognition
- A Comprehensive Guide to Convolutional Neural Networks — the ELI5 way
- Convolutional Neural Networks for Beginners
- Tensorflow Convolutional Neural Network (CNN)
- Convolutional Networks Book
- CNNs, Part 1: An Introduction to Convolutional Neural Networks
- CS231n Winter 2016 BY Andrej Karpathy 15 Videos
- Intuitive understanding of 1D, 2D, and 3D Convolutions in Convolutional Neural Networks
- CNN ExplainerAn interactive visualization system designed to help non-experts learn about Convolutional Neural Networks (CNNs)
- Face editing with Generative Adversarial Networks
- Variational Autoencoders
- Generative Adversarial Networks (GANs) in 50 lines of code (PyTorch)
- Generative Models
- Anyone Can Learn To Code an LSTM-RNN in Python (Part 1: RNN) Baby steps to your neural network's first memories.
- The Unreasonable Effectiveness of Recurrent Neural Networks
- An Introduction to Recurrent Neural Networks for Beginners A simple walkthrough of what RNNs are, how they work, and how to build one from scratch in Python.
- A Visual Guide to Using BERT for the First Time
- The Illustrated Word2vec
- The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning)
- The Illustrated Transformer
- The Unreasonable Effectiveness of Recurrent Neural Networks by Andrej Karpathy
- Attention and Augmented Recurrent Neural Networks by Distill
- Visualizing memorization in RNNs by Distill Inspecting gradient magnitudes in context can be a powerful tool to see when recurrent units use short-term or long-term contextual understanding.
- Deep Learning for NLP: ANNs, RNNs and LSTMs explained!
- LSTM implementation explained
- A Gentle Introduction to LSTM Autoencoders
- Keras LSTM tutorial – How to easily build a powerful deep learning language model
- The Annotated GPT-2
- The Annotated Transformer
- The Illustrated GPT-2 (Visualizing Transformer Language Models)
- A Visual Guide to Using BERT for the First Time
- BERT Explained: State of the art language model for NLP
- BERT – State of the Art Language Model for NLP
- BioBERT, a language representation model for biomedical domain, especially designed for biomedical text mining tasks such as biomedical named entity recognition, relation extraction, question answering, etc
- Deep Reinforcement Learning Course 🕹️ A Free course in Deep Reinforcement Learning from beginner to expert.
- Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
- Unity Machine Learning Agents Toolkit
- 🖥️ WRITING MY FIRST MACHINE LEARNING GAME! (1/4)
- Deep Reinforcement Learning: Pong from Pixels by Andrej Karpathy
- A Beginner's Guide to Deep Reinforcement Learning
- An Introduction to Unity ML-Agents
- Deep Reinforcement Learning Algorithms with PyTorch
- LECTURES: Introduction to Reinforcement Learning - David Silver
- BOOK: Reinforcement Learning - An Introduction by Sutton and Barto
- BOOK: Deep Reinforcement Learning Hands On by Maxim Lapan
- Udacity : Deep Learning with PyTorch
- Deep Learning (PyTorch) : Code
- Udacity : Secure AI
- TORCHSCRIPT
- PyTorchZeroToAll (in English) Sung Kim a Series of 14 Videos
- Introduction to TensorFlow 2.0: Easier for beginners, and more powerful for experts (TF World '19)
- TensorFlow Lite: Solution for running ML on-device (TF World '19)
- Machine Learning in JavaScript (TensorFlow Dev Summit 2018)
- TensorFlow.js Quick Start
- Keras vs. tf.keras: What’s the difference in TensorFlow 2.0?
- How To Run TensorFlow Lite on Raspberry Pi for Object Detection
- How computers learn to recognize objects instantly | Joseph Redmon
- Intro to TensorFlow for Deep Learning
- TensorFlow in Practice Specialization : Coursera
- TensorFlow: Data and Deployment Specialization : Coursera
- Transfer Learning with Keras and Deep Learning
- A Comprehensive Hands-on Guide to Transfer Learning with Real-World Applications in Deep Learning
- TensorFlow Core Tutorials
- Machine Learning in 5 Minutes: How to deploy a ML model (SurveyMonkey Engineer explains)
- Deploy Machine Learning Models with Django
- MlFlow - An open source platform for the machine learning lifecycle
- TensorFlow: Data and Deployment Specialization
- 3Blue1Brown
- StatQuest with Josh Starmer
- Sentdex
- Luis Serrano
- Brandon Rohrer
- deeplizard
- Tech With Tim
- Microsoft Research
- Corey Schafer
- Data School
- Two Minute Papers
- Welch Labs
- Simplilearn
- Great Learning
- DeepLearning.TV
- TensorFlow
- Deeplearning.ai
- Code Bullet
- edureka!
- Lex Fridman
- The Artificial Intelligence Channe
- freeCodeCamp.org
- CloudxLab
- Alexander Amini
- Jeff Heaton
- Abhishek Thakur
- The Coding Train
- 🖥️ HOW TO GET STARTED WITH MACHINE LEARNING!
- My Self-Created Artificial Intelligence Masters Degree
- https://end-to-end-machine-learning.teachable.com/courses/667372/lectures/11900568
- ML Fundamentals by StatQuest
- Machine Learning with Python by sentdex
- 5 Beginner Friendly Steps to Learn Machine Learning and Data Science with Python - Daniel Bourke
- Data School
- Neural Networks and Deep Learning
- https://www.machinelearningisfun.com/
- https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471
- https://medium.com/greyatom
- https://greyatom.com/glabs
- John Searle: "Consciousness in Artificial Intelligence" | Talks at Google
- ML Terms
- https://github.com/nature-of-code/NOC-S17-2-Intelligence-Learning/tree/master/week3-classification-regression
- https://github.com/nature-of-code/NOC-S17-2-Intelligence-Learning
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