Applied-Machine-Learning-Course
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This course covers the applied side of algorithmics in machine learning and deep learning, focusing on hands-on coding experience in Python.
Course: Applied Machine Learning
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This course covers the applied/coding side of algorithmics in machine learning, with some deep learning and evolutionary algorithms thrown in as well.
(The course is now in its third iteration, and I update this repo continually)
Moshe Sipper’s Cat-a-log of Writings
Some Pros and Cons of Basic ML Algorithms, in 2 Minutes
Course Topics ✧ Resources
Syllabus
❖ Python ❖ Artificial Intelligence ❖ Date Science ❖ Machine Learning Intro ❖ Scikit-learn ❖ ML Models ❖ Decision Trees ❖ Random Forest ❖ Linear Regression ❖ Logistic Regression ❖ Linear Models ❖ Regularization: Ridge & Lasso ❖ AdaBoost ❖ Gradient Boosting ❖ AddGBoost ❖ Ensembles ❖ XGBoost ❖ Comparing ML algorithms ❖ Gradient Descent ❖ SVM ❖ Bayesian ❖ Metrics ❖ Data Leakage ❖ Dimensionality Reduction ❖ Clustering ❖ Hyperparameters ❖ Some Topics in Probability ❖ Feature Importances ❖ Semi-Supervised Learning ❖ Neural Networks ❖ Deep Learning ❖ Large Language Models ❖ DL and AI ❖ Evolutionary Algorithms: Basics ❖ Evolutionary Algorithms: Advanced
Topics (according to order of instruction)
(: my colab notebooks,
: my medium articles)
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Python
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Artificial Intelligence
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Date Science
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Machine Learning Intro
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Scikit-learn
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ML Models
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Decision Trees
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Random Forest
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Linear Regression
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Logistic Regression
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Linear Models
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Regularization: Ridge & Lasso
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AdaBoost
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Gradient Boosting
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AddGBoost
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Ensembles
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XGBoost
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Comparing ML algorithms
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Gradient Descent
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SVM
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Bayesian
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Metrics
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Data Leakage
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Dimensionality Reduction
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Clustering
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Hyperparameters
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Some Topics in Probability
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Feature Importances
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Semi-Supervised Learning
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Neural Networks
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Deep Learning
- Neural Networks with À La Carte Selection of Activation Functions
- PyTorch
- PyTorch
- Double Descent
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Overparameterization, Backpropagation, Alimentation: Them and Us
- conv demo
- convolution
- A simple image convolution
- Implementing Image Processing Kernels from scratch using Convolution in Python
- Introduction to image generation (diffusion)
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Loss is Boss
and other articles in the DL section
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Large Language Models
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DL and AI
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Evolutionary Algorithms: Basics
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Evolutionary Algorithms: Advanced
Resources
Cheat Sheets
- Machine Learning Glossary
- Some Pros and Cons of Basic ML Algorithms, in 2 Minutes
- Cheat Sheets for Machine Learning and Data Science
- The Illustrated Machine Learning Website
Vids
- John Koza Genetic Programming (YouTube)
- גיא כתבי - אלגוריתמים אבולוציוניים (YouTube) [גיא בוגר הקורס שלי: אלגוריתמים אבולוציוניים וחיים מלאכותיים]
- StatQuest with Josh Starmer
- ML YouTube Courses
- Machine Learning Essentials for Biomedical Data Science: Introduction and ML Basics
- Artificial Intelligence Under Fire: Attacking and Defending Deep Neural Networks
Basic Reads
- Genetic and Evolutionary Algorithms and Programming
- Choosing Representation, Mutation, and Crossover in Genetic Algorithms
- Introduction to Evolutionary Computing (course/book slides)
- 26 Top Machine Learning Interview Questions and Answers: Theory Edition
- 10 Popular Machine Learning Algorithms In A Nutshell
- Machine learning preparatory week @PSL
- Neural Networks and Deep Learning (coursera)
- Tinker With a Neural Network in Your Browser
- Common Machine Learning Algorithms for Beginners
Advanced Reads
- What can LLMs never do?
- Foundational Challenges in Assuring Alignment and Safety of Large Language Models
- “Explainability” Is a Poor Band-Aid for Biased AI in Medicine
- Some Techniques To Make Your PyTorch Models Train (Much) Faster
- GPT in 60 Lines of NumPy
- ROC-AUC
- Why video games are essential for inventing artificial intelligence
Books (🡇 means free to download)
- M. Sipper, Evolved to Win, Lulu, 2011 🡇
- M. Sipper, Machine Nature: The Coming Age of Bio-Inspired Computing, McGraw-Hill, New York, 2002
- A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing, Springer, 1st edition, 2003, Corr. 2nd printing, 2007
- R. Poli, B. Langdon, & N. McPhee, A Field Guide to Genetic Programming, 2008 🡇
- J. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, MA, 1992.
- S. Luke, Essentials of Metaheuristics, 2013 🡇
- A. Geron, Hands On Machine Learning with Scikit Learn and TensorFlow, 2017 🡇
- G. James, D. Witten, T. Hastie, R. Tibshirani, An Introduction to Statistical Learning, 2nd edition, 2021 🡇
- J. VanderPlas, Python Data Science Handbook
- K. Reitz, The Hitchhiker’s Guide to Python
- M. Nielsen, Neural Networks and Deep Learning
- Z. Michalewicz & D.B. Fogel, How to Solve It: Modern Heuristics, 2nd ed. Revised and Extended, 2004
- Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag, Berlin, 3rd edition, 1996
- D. Floreano & C. Mattiussi, Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies, MIT Press, 2008
- A. Tettamanzi & M. Tomassini, Soft Computing: Integrating Evolutionary, Neural, and Fuzzy Systems, Springer-Verlag, Heidelberg, 2001
- M. Mohri, A. Rostamizadeh, and A. Talwalka, Foundations of Machine Learning, MIT Press, 2012 🡇
- Simon J.D. Prince, Understanding Deep Learning, MIT Press, 2023 🡇
Software
- EC-KitY: Evolutionary Computation Tool Kit in Python with Seamless Machine Learning Integration
- gplearn: Genetic Programming in Python, with a scikit-learn inspired and compatible API
- LEAP: Library for Evolutionary Algorithms in Python
- DEAP: Distributed Evolutionary Algorithms in Python
- Swarm Intelligence in Python (Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Algorithm, Immune Algorithm, Artificial Fish Swarm Algorithm in Python)
- Scikit-learn: Machine Learning in Python
- Mlxtend (machine learning extensions)
- PyTorch (deep networks)
- Best-of Machine Learning with Python
- Fundamental concepts of PyTorch through self-contained examples
- Faster Python calculations with Numba
Datasets