Important: WORK IN PROGRESS, notes can contain mistakes: I will appreciate any correction or suggestion. Furthermore, I am not a native english speaker. Please, forgive me for any mistake.
Important: I highly recommend to view these notebooks online, using Google Colab, or locally, using Jupyer Lab, for two main reasons: you can run python snippets and LaTeX formulas will get visualized properly (GitHub does not display some formulas).
- Concept Learning 🔍
- Model evaluation and validation 📈
2.1. Data Analysis & Feature Engineering 📊 - Decision tree learning 🌳
- Regularization in supervised learning models ⚖️
- Logistic Regression 🌺
- Introduction to Neural Networks and Deep Learning 🧠
6.1. Feedforward Neural Networks 🕸
6.2. Optimization Algorithms 🎯 - Bayesian Learning 🧞♂️
- Support Vector Machines 🛣
- Cluster Analysis 🦠
I created this collection of notebooks while I was studying for the machine learning exam at my university. Initially, they were intended as notes for personal use, but as soon as I started falling in love with this subject, I started to delve deeper into the topics, reading lots of interesting books and extending my notes to the point I decided to share them with everyone, with the hope that they will be useful for other students. Some notebooks are more theoretical while others are more practical. The first notebook is about concept learning, and it is completely based on the Mitchell's book, which was the book adopted at my university (which, to be honest, I didn't like too much 🤷🏻♂️). In the references section, you can find all the resources I used. Some of them are easier and targeted to novices, while others (e.g., Bishop, Hastie, Goodfellow) delve deeper into the mathematics behind the algorithms.
- Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow, 2nd edition - Aurélien Géron - O'Reilly, 2019 (Practice oriented)
- Python Machine Learning, 3rd edition - Sebastian Raschka, Vahid Mirjalili - Packt Publishing Ltd. (Practice oriented)
- An Introduction to Statistical Learning - James G., Witten D., Hastie T., Tibshirani R. - Springer (Both theoretical and practical)
- Pattern Recognition and Machine Learning - Christopher Bishop - Springer, 2006 (Theoretical)
- The Elements of Statistical Learning, 2nd edition - Hastie, Tibshirani, Friedman, Springer, 2009 (Theoretical)
- Machine Learning - Tom Mitchell - McGraw Hill, 1997 (Theoretical)
- Machine Learning Engineer Nanodegree (Udacity online course)
- How to Win a Data Science Competition (Coursera online course)
- Deep Learning Specialization (Coursera online course)
- Deep Learning - Ian Goodfellow, Yoshua Bengio, Aaron Courville - MIT Press, 2016 (Theoretical)
- Dive into Deep Learning - Aston Zhang, Zachary C. Lipton, Mu Li, Alexander J. Smola - 2020 (Practice oriented)