If you’re stepping into the expansive world of machine learning (ML) and looking for comprehensive resources, you’re in the right place! This blog post will guide you through the treasure trove of free ML reading materials categorized by key topics, making it easier for you to find the right references for your learning journey.
Table of Contents
- EDA, Visualization, and Data Cleaning (7)
- Mathematics for ML (12)
- Statistics and Probability (11)
- Linear Regression (5)
- Optimization (2)
- Machine Learning (39)
- R Related (18)
- Feature Engineering (2)
- Explainability/Interpretability (2)
- Deep Learning / Neural Networks (15)
- Reinforcement Learning (4)
- Recommender Systems (2)
- Anomaly Detection (1)
- Computer Vision (2)
- NLP and Large Language Models (LLM) (10)
- Causal Inference (9)
- Conformal Prediction (3)
- Time Series: Forecasting (7)
1. EDA, Visualization, and Data Cleaning
Start your journey with these essential reads:
- Python for Data Analysis (3rd Edition) by Wes McKinney
- Flexible Imputation of Missing Data by Stef van Buuren
- Fundamentals of Data Visualization by Claus O. Wilke
- R Graphics Cookbook by Winston Chang
- Modern Data Visualization with R by Robert Kabacoff
- Think Stats: Exploratory Data Analysis in Python by Allen B. Downey
- SQL Notes for Professionals
2. Mathematics for ML
Understanding mathematics is crucial before diving deeper into machine learning. Picture mathematics in machine learning as the foundation of a house. It needs to be strong and well-structured for the entire building (the algorithms) to stand tall. Here are some fundamental readings:
- Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
- The Matrix Calculus You Need For Deep Learning by Terence Parr, and Jeremy Howard
- Matrix Analysis by Joel A. Tropp
- Linear Algebra Done Wrong by Sergei Treil
- Linear Algebra Done Right by Sheldon Axler
- Linear Algebra, Theory and Applications by Kenneth Kuttler
- Algebra, Topology, Differential Calculus, and Optimization Theory for Computer Science and Machine Learning by Jean Gallier and Jocelyn Quaintance
3. Statistics and Probability
Statistics acts like a compass that guides you through the vast sea of data. With the right statistical knowledge, you can navigate and extract meaningful insights from your data analysis journey. Recommended resources include:
- Probability and Statistics – The Science of Uncertainty by Michael J. Evans and Jeffrey S. Rosenthal
- Probability in High Dimensions by Joel A. Tropp
- Introduction to Probability by Joseph K. Blitzstein and Jessica Hwang
- A History of the Central Limit Theorem by Hans Fischer
- Think Bayes: Bayesian Statistics Made Simple by Allen B. Downey
...
(Continue with additional sections from the Compendium)
... Troubleshooting
If you run into any issues accessing these resources or need any help in navigating through the dense material, consider the following troubleshooting tips:
- Ensure your internet connection is stable when accessing any online resources.
- Use an updated browser for better compatibility with resource links.
- Contact the respective authors if you find broken links or missing materials.
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Conclusion
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.