Machine-Leaning-Basics

Documenting my journey into ML/AI.
Best video overview of all ML toolkits: https://www.youtube.com/watch?v=WQt4H1Bo0jM

Progress so far:

Unsupervised Learning

1. K-Means Clustering with K-Means++ Seeding
2. Kernel Density Estimation
   • Non-Parametric vs Parametric DE

Supervised Learning

1. Naive Baye's Classifier
2. Logistic Regression
3. Linear Regression
4. K-NN (K Nearest Neighbors)

Books I'm currently following:

1. Basics of Linear Algebra for Machine Learning by Jason Brownlee
2. Machine Learning for Developers by Rodolfo Bonnin
3. Machine Learning Algorithms by Giuseppe Bonaccorso
4. Artificial Intelligence: A Modern Approach by Russell & Norvig
5. Python Machine Learning by Sebastian Raschka
6. Pattern Recognition and Machine Learning by Christopher M. Bishop
7. Python Data Science Handbook by Jake Vanderplas

Useful Links:

1. Python Exercises: https://www.pythonmorsels.com/exercises/list/
2. Sebastian Raschka Pattern Classification Repo: /~https://github.com/aoyshi/pattern_classification
3. Jake Vanderplas Python Handbook Repo: /~https://github.com/aoyshi/PythonDataScienceHandbook
4. Vassilis ML/AI Course: http://vlm1.uta.edu/~athitsos/courses/
5. AIMA Pseudocode Repo: /~https://github.com/aoyshi/aima-pseudocode
6. ML Projects Ideas:

• https://www.springboard.com/blog/machine-learning-projects/
• https://www.analyticsvidhya.com/blog/author/pulkits/
• https://medium.mybridge.co/30-amazing-machine-learning-projects-for-the-past-year-v-2018-b853b8621ac7
• https://www.upgrad.com/blog/6-interesting-machine-learning-project-ideas-for-beginners/
• https://elitedatascience.com/machine-learning-projects-for-beginners
• https://www.dezyre.com/article/top-10-machine-learning-projects-for-beginners/397

7. YouTube Tutorials:

• ML: https://www.youtube.com/playlist?list=PLEiEAq2VkUULYYgj13YHUWmRePqiu8Ddy
• ML: https://www.youtube.com/playlist?list=PL9ooVrP1hQOHUfd-g8GUpKI3hHOwM_9Dn
• AI: https://www.youtube.com/playlist?list=PL9ooVrP1hQOGHNaCT7_fwe9AabjZI1RjI