This project is my learning journey into the fascinating world of Machine Learning. A month ago, I decided to start reading the famous book "Hands on Machine Learning" by Aurélien Géron. I thought it would be a good idea to consolidate my theoretical learning in the field and also to improve my practice skills. Nevertheless, learning on my own is not that fun, so I came up with this little project that aims to connect with other students and consists of two parts. The first part is already finished and contains a pdf with the notes and summary of the part I of the book (Fundamentals of Machine Learning). Currently, I am working on the second part of the book (Neural Networks and Deep Learning). Both parts will contain a pdf with the theoretical part and some jupyter notebooks where I practise what I have learnt (either with examples from the book or with datasets I took from Kaggle).
I hope you find this interesting and helpful for your learning! Feel free to connect with me if you are also reading the book :)
This section contains:
- A pdf with the summary of the book's content.
- A folder named "Classification - Supervised learning" that contains jupyter notebooks and the datasets used to consolidate the learning on the topic (Logistic Regression and Ensemble Learning, among others).
- A folder named "Regression - Supervised learning" that contains jupyter notebooks and the datasets used to consolidate the learning on the topic (different types of linear regression and regularization mainly).
- A folder named "Unsupervised learning" that contains jupyter notebooks and the datasets used to consolidate the learning on the topic (K-means, DBSCAN and Gaussian Mixtures, among others).
Each jupyter is unique and can contain different things (most of them contain a previous exploratory data analysis, hyperparameter tuning, etc). I will add more exercises as I develop them.
⏳I am currently working on this section ... ⌛