Book Recommendations
Books that have shaped my thinking and sparked new ideas
Atomic Habits
A practical guide to building good habits and breaking bad ones.
The Power of Your Subconscious Mind
Explores how the subconscious shapes reality and offers techniques to reprogram it.
Machine Learning
A foundational text covering machine learning algorithms and theories.
An Introduction to Statistical Learning
An accessible introduction to statistical learning techniques with applications in R.
The Prophet
A poetic masterpiece offering timeless wisdom on love, freedom, and work.
Rubaiyat
A collection of quatrains exploring themes of love, mortality, and beauty.
Python for Data Analysis
A practical guide to data analysis using Python, covering pandas, NumPy, and visualization.
Think Python
Beginner-friendly guide to Python programming covering functions, recursion, and OOP.
Build a Career in Data Science
Practical guide for aspiring data scientists covering career paths and skill-building.
Conversations on Data Science
Explores real-world practice of data science, including decision-making and communication.
The Data Science Handbook
Comprehensive guide covering statistics, machine learning, and practical applications.
Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow
Step-by-step practical guide to machine learning and deep learning using Python libraries.
Python Machine Learning
Covers machine learning with Python including scikit-learn and deep learning.
Elements of Statistical Learning
Classic statistical learning book focusing on algorithms, theory, and applications.
Deep Learning for Natural Language Processing
Introduces NLP using deep learning, covering text representation and sequence models.
Build a Large Language Model from Scratch
Practical guide to building LLMs from scratch, covering tokenization and training.
Designing Data-Intensive Applications
Covers principles for building scalable, reliable, and maintainable data systems.
Kimball: The Data Warehouse Toolkit (3rd Edition)
Comprehensive guide to dimensional modeling and data warehouse design.
MLOps Engineering at Scale
Guide to deploying ML systems at scale, including CI/CD pipelines.