ML-SelfEducation

Updated 27 February 2024.

Lists of books, classes, MOOCs I have completed (or completing) on machine learning

University Classes

Completed Completed (@ The University of Georgia)

1. CSCI 1360 Foundations for Informatics and Analytics | Fall 2018 | Link
   
2. STAT 6250 Applied Multivariate Analysis and Statistical Learning | Spring 2019 | Link
   
3. ARTI 8950 Machine Learning | Spring 2020 | Link

MOOCs

Completed

1. Learning MATLAB | Linkedin learning | July 2016 | Link
   
2. STAT 505 Applied Multivariate Statistical Analysis (Penn State) | Audit | April 2019
   
3. Mathematics for Machine Learning: Linear Algebra | Coursera | Jan 2020 | Link
   
4. Mathematics for Machine Learning: Multivariate Calculus | Coursera | Mar 2020 | Link
   
5. Mathematics of Machine Learning: PCA | Coursera | May 2020 | Link
   
6. Neural Networks and Deep Learning | Coursera | Jul 2020 | Link
   
7. Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization | Coursera | Aug 2020 | Link
   
8. Structuring Machine Learning Project | Coursera | Aug 2020 | Link
   
9. Specialized Models: Time Series and Survival Analysis | Coursera | Feb 2022 | I did not complete, I skipped the following topics, ARMA, ARIMA, SARIMA models, deep learning for forecasting. | Course Link |
   
10. Practical Introduction to Natural Language Processing | Independent | May 2023 | Course Link |
    
11. ChatGPT Prompt Engineering for Developers| deeplearning.ai / short course | June 2023 | Course Link |
    
12. LangChain for LLM Application Development | deeplearning.ai / short courses| July 2023 | Course Link |
    
13. Large Language Models: Application through Production| Databricks| Aug 2023 | Link
    
14. LangChain: Chat with your Data | deeplearning.ai / short courses| December 2023 | Course Link |
    Coursera Guided Classes: Completed
15. Perform Real-Time Object Detection with YOLOv3 | Coursera | Nov 2020 | Link
    
16. Explaining Tree Based Models Using SHAP | Coursera | Dec 2020 | Link
17. Automatic Machine Learning Learning with H2O AutoML and Python | Coursera | Mar 2021 | Link
18. Deep Learning with PyTorch : Image Segmentation| Coursera | Feb 2022 | Link

Ongoing

• Functions, Tools and Agents with LangChain | deeplearning.ai /short courses
• LLMs: Foundation Models From the Ground Up | Databricks

Books

Completed

1. Data Science from Scratch: First Principles with Python by Joel Grus | Technical my review
   
2. Machine Learning by Alpaydin | non-technical | my review
   
3. Deep Medicine by Eric Topol | non-technical | my review
   
4. A Course in Machine Learning by Hal Daumé III | technical | my review
   
5. The Book of Why: The New Science of Cause and Effect | Semi-technical | my review
   
6. Introduction to Statistical Learning: with Applications in R by Gareth James et al | Technical | my review
   
7. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurelien Geron | Technical | Technical | my review-1
   
8. Interpretable Machine Learning by C. Molnar | Technical | my review
   
9. Hands-On Gradient Boosting with XGBoost and scikit-learn by Corey Wade | Technical | my review
   
10. Survival analysis: A self learning text | Technical | my review
11. GPT-3 Building Innovative NLP Products Using Large Language Models | Technical | my review
12. Modern Computer Vision with PyTorch| Technical | my review
13. Machine Learning Automation with TPOT Build, validate, and deploy fully automated machine learning models with Python | Technical

Reading

• Applied Machine Learning Explainability Techniques Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more Aditya Bhattacharya
• Understanding deep learning.

Bookmarked Video Materials

These are some of my go-to online video materials anytime I want to learn a new concept or refresh my memory about a concept, some of which I have not watched,

1. Stat Quest with Josh Starmer Machine Learning Playlist (Link)
   Comment: Watched some videos. These are great, intuitive introductory videos to machine learning, and some ML algorithms.
2. Deeplearning.ai (Link)
3. Standford's Convolutional Neural Networks for Visual Recognition (Link)
4. MIT 18.065: Gilbert Strang's Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Link)
5. Bloomberg's Foundations of Machine Learning (Link)
6. Stat Quest with Josh Starmer Statistics Fundamentals Playlist (Link)
   
7. Ali Ghodsi's STAT 946: Deep Learning Class @ UWaterloo (Link, Syllabus)
   
8. DeepMind x UCL | Deep Learning Lectures (Link)
9. Deep Learning in Life Sciences, MIT Course (Link)