Machine Learning A-Z™: Hands-On Python In Data Science (Udemy course)

This repository contains modified notebooks from a popular udemy machine learning course.

Learner's Comments

The course doesn't really go in-depth in regards to the inner-workings of the algorithms, and uses tools like sklearn library (or other libraries) primarily for visualising the data for beginners to get a sense of the expected output and understand the underlying concepts. Hence, notes were taken to make up for some of the aspects that I would like to know in details, and there might be more notebooks created apart from the existing notebooks to explore other algorithms as well.

Setup

1. Ensure that > python 3.6 is installed and Java JRE 8.
2. Ensure that jupyter notebook is installed.
3. Run pip install -r requirements.txt.
4. Install Stanford CRF NER here, Stanford CoreNLP parser here and unzip into 07 - Natural Language Processing.

These are the notes for each of the sections,

1. Part 1: Data Preprocessing
2. Part 2: Regression
3. Part 3: Classification
4. Part 4: Clustering
5. Part 5: Association Rule Learning
6. Part 6: Reinforcement Learning
7. Part 7: Natural Language Processing
8. Part 8: Deep Learning
9. Part 9: Dimensionality Reduction
10. Part 10: Model Selection (not in-depth)

Much thanks to the medium.com, towardsdatascience.com, analyticsvidhya.com, machinelearningmastery.com, geeksforgeeks.com (and many others) for their awesome articles that I could refer and take notes from to rewrite and get a sense of my own.

Image credits

The credits of the images stored in the repository solely belongs to the original authors/organisations/universities. These are used for personal reference in my notes as part of my learning journey and are not used for any other purposes.