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- Machine Learning Study Path
- Top ML repos
- Hands on ML
- ML for Software Engineers
- PredictionIO, a machine learning server for developers and ML engineers. Built on Apache Spark, HBase and Spray
- Machine learning and deep learning tutorials, articles and other resources
- Curriculum – Machine Learning Sabbatical
- Curriculum & Log - ML
- Interactive Machine Learning, Deep Learning and Statistics websites
- Slides of how PyTorch helped the speaker learn ML
- Slides of Collaborative Recommender System for Music
- Tensorflow 2.0 by Josh Gordon at the Google X / X-Team event
- Algorithmia's Machine Learning Roadmap
- Machine Learning Terminology
- Understand Machine Learning Implementation
- Machine Learning on Retail
- Machine Learning on Marketing
- Understand How to answer Why
- Abductive Learning: Towards Bridging Machine Learning and Logical Reasoning: Slides | Video | GitHub
- Stacking Ensemble Machine Learning With Python
- ML Flashcards: website | github
- Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning by Chris Albon
- A number of useful ML related repositories by Chris Albon
- ML Blogs by faculty.ai
- ML Blog by Neptune.ai
- ML topics expanded by Chris Albon - topics covered: Vectors, Matrices, And Arrays • ML Basics • Preprocessing Structured Data • Preprocessing Images • Preprocessing Text • Preprocessing Dates And Times • Feature Engineering • Feature Selection • Model Evaluation • Model Selection • Linear Regression • Logistic Regression • Trees And Forests • Nearest Neighbors • Support Vector Machines • Naive Bayes • Clustering
- Claoudml - Free Data Science & Machine Learning Resources
- How to 🤷♂️🤷♂️🤷♂️ #Practical #Lessons for #Scaling Machine Learning Solutions in the Real World
- Design Thinking and Machine Learning
- Machine Learning Quick Reference best Practice
- Most popular machine learning prediction
- Exploring ML in 100 Days
- A-Z Machine Learning Resources
- Machine Learning Map
- Machine Learning Mindmap: 1 | 2
- Machine Learning from scratch!
- Top Machine Learning Algorithm for Preditions
- Pytorch London meetup talk slides
- Learn, play, and create with artificial intelligence: great online portal to perform ML and AI training and experiments
- [Allegro Trains: Auto-Magical Experiment Manager, Version Control and ML-Ops for AI](https://allegro.ai/](https://allegro.ai/trains-open-source/) | Github
- 👉 Scalars Vectors Matrices and Tensors 👈
- Introduction to Matrices and Matrix Arithmetic for Machine Learning
- ML Quick reference - best practices: 1 | 2
- Grid search & Tuning Hyperparameters
- 👉 Rules of Machine Learning 👈
- ✍ Machine Learning - Lecture Notes ✍ Credits - Sebastian Raschka
- End-to-end ML
- Sources of Error in Machine Learning - Towards AI - Medium
- 🔥Become a self-taught #machinelearning #engineer🔥
- Machine Learning: An Analytical Invitation to Actuaries
- Machine Learning Yearning
- Machine Learning vs Statistics vs Statistical Learning in One Picture +
- 3 Types of Regression in One Picture +
- 10 Must-read Machine Learning Articles (March 2020)
- Restart from basics, here's the learning path (ML)
- Feedback Recurrent AutoEncoder
- A Gentle Introduction to LSTM Autoencoders
- New State of the Art AI Optimizer: Rectified Adam (RAdam)
- Its becoming clearer than ever before that there are huge advantages of training very large models.
- Survival Analysis in R
- How to Reduce Overfitting Using Weight Constraints in Keras
- How to Configure XGBoost for Imbalanced Classification
- How to Fix k-Fold Cross-Validation for Imbalanced Classification
- Approaching (almost) Any Machine Learning Problem | by Abhishek Thakur | Kaggle Days Dubai | Kaggle: original post | Video | Slides - highly recommended
- Prediction Intervals for Machine Learning
- Correlation explained in a way everyone can understand!
- Correlation Coefficients in One Picture
- How to Use Quantile Transforms for Machine Learning
- Gath-Geva fuzzy clustering (python)
- Some Notable Recent ML Papers and Future Trends
- How to Selectively Scale Numerical Input Variables for Machine Learning
- 2020 ML Roadmap by Daniel Bourke
- 📌50 Days of Machine Learning📌
- Curve Fitting With Python
- Do we need to learn Optimization to build Machine Learning Models ?
- Machine Learning Engineering book
- Machine Learning from Scratch
- 10 Fun Machine Learning Project Ideas for Newbies
- Title: From Machine Learning to Machine Reasoning (Drew Hudson is a Ph.D. student at Stanford University) : Papers: Learning by Abstraction: The Neural State Machine | Compositional Attention Networks for Machine Reasoning | GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering | Visual Reasoning
- Algorithms
- See Machine Learning in Courses
- See ML on Code/Programm/Source Code
- See Cloud/DevOps/Infra > Performance - to find various ML performance benchmarking suites
- See Post model-creation analysis, ML interpretation/explainability
- Stack your ML models using an ensemble library: picknmix by Cheuk Ting Ho
- Hummingbird - python library that compiles trained ML models into tensor computation for faster inference. Supported models include sklearn decision trees, random forest, lightgbm, xgboost
- Scikit-Lego v.0.4.4
- PerceptiLabs is a dataflow driven, visual API for TensorFlow
- Hermione ML: pypi | GitHub
- Speed ML: github | Notebook
- Embedded Learning Library: HomePage | GitHub
- Precision vs significance / accuracy vs precision / bias vs variance
- Tour of Evaluation Metrics for Imbalanced Classification
- How to Use ROC Curves and Precision-Recall Curves for Classification in Python
- Loss Functions Deep Dive | Part I of that blog post
- Thoughtful Machine Learning with Python: A Test-Driven Approach (Book)
- Tests in TrainingHelpers and Tests in TFHelpers by Jack Devlin
- Tests in picknmix by Cheuk Ting Ho
- Thoughtful Machine Learning with Python: A Test-Driven Approach (Book)
- Machine Learning Books
- The "Python Machine Learning (1st edition)" book code repository and info resource
- ML Engineering EBook | The Machine Learning Engineering book written by Andriy Burkov
- The Hundred-Page Machine Learning Book written by Andriy Burkov
- The Hundred-Page Machine Learning Book by Andriy Burkov
- GitHub topic: uncertainties
- Charles's talk on Probability, Uncertainty: The Surprising Utility of Surprise
- Many models workflows in python: part i: Do you want some uncertainty with that?
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Back to main page (table of contents)
- Demystification of the key concepts of Artificial Intelligence and Machine Learning
- What do you need for ML?
- Do you really need ML?
- ML use cases
- Introduction to ML
- Machine Learning Theory
- Flyte accelerate your Machine Learning and Data workflows to production
- A curated list of awesome Machine Learning frameworks, libraries and software
- Open Sourcing Manifold, a Visual Debugging Tool for Machine Learning
- 3 types of projects you should do if you are just diving into #datascience, #machinelearning
- Machine Learning From Scratch | NumPy | Aims to cover everything from data mining to deep learning.
- Machine Learning for Forecasting Chaos by Dr. Edward Ott
- Recommender Engine - Under The Hood
- Supercharged Prediction with Ensemble Learning by James Briggs
- Technical Notes On Using Data Science & Artificial Intelligence
- Complete Hands-Off Automated Machine Learning
- Bayesian Machine Learning
- How to put machine learning to intellegent apps
- Machine Learning in Oracle Database
- 34 External Machine Learning Resources and Related Articles
- R and Python Code for some popular machine learning algorithm
- DeepSpeed: Extreme-scale model training for everyone! Trillion parameter model training with 3D parallelism:
- 50 external machine learning / data science resources and articles
- Machine Learning in Parallel with Support Vector Machines, Generalized Linear Models, and Adaptive Boosting
- Machine Learning Technical Landscape in one picture
- Calibrating classifiers
- Understanding and Selecting Recommenders
- Supervised vs Unsupervised Learning Workflow
- Concrete Compressive Strength Prediction using Machine Learning
- Learning from unlabelled data
- Difference Between Classification and Regression in One Picture
- One-Class Classification Algorithms for Imbalanced Datasets
- 🔥The things that are changing in an experiment are called 𝙑𝘼𝙍𝙄𝘼𝘽𝙇𝙀𝙎.
- Machine Learning Basics: Polynomial Regression
- Your roadmap to a full fledge Machine Learning Engineer or Data Engineer
- PYCARET 1.0.0 - A simple, fast and efficient way to do machine learning in Python
- Huawei’s MindSpore: A new competitor for TensorFlow and PyTorch?
- Data science GUI programs with the awesome PySimpleGUI package!
- Understanding Maximum Likelihood Estimation (MLE)
- Model? Or do you mean Weight of Evidence (WoE) and Information Value (IV)? by Jackie Tan
- [Performance Metrics in Binary Classification](https://www.linkedin.com/posts/nabihbawazir_datascience-machinelearning-artificialintelligence-activity-6627411911984197633-wZEG](https://machinelearningmastery.com/tour-of-evaluation-metrics-for-imbalanced-classification/)
- Free Books: Data Science & AI
- Two New Free Books on Machine Learning
- Top ML Books to Read in 2019
- Dive into Machine Learning with Python Jupyter notebook and scikit-learn!
- Aggregated resources on the topic "uncertainty"
- Sebastian Ruder, Research scientist, DeepMind - Machine Learning, Deep Learning, & NLP
- 𝗣𝘆𝘁𝗵𝗼𝗻 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀
- [Selecting and Tuning Hyperparameters](https://www.linkedin.com/posts/alastairkerrmuir_hyperparameter-selection-and-tuning-meetup-ugcPost-6621172444340240384-9WBj)