Embark on Your AI and ML Journey
Are you a data scientist looking to open new doors and enhance your skill – set? We’ve carefully curated a list of the best AI books to start your exciting exploration into the world of machine learning (ML) and artificial intelligence (AI). Our selection features books that simplify complex concepts, presenting them with relatable examples and real – world applications. Whether you’re a Python or R enthusiast, there’s something here for everyone.
Quotation from Sundar Pichai
“Machine learning is a core, transformative way by which we’re rethinking everything we’re doing. We’re thoughtfully applying it across all our products, be it search, ads, YouTube, or Play. We’re in the early days, but you’ll see us in a systematic way think about how we can apply machine learning to all these areas.” – Sundar Pichai, CEO, Google
Table of Contents
- Machine Learning Yearning
- The Hundred – Page Machine Learning Book
- Programming Collective Intelligence
- Machine Learning for Hackers
- Machine Learning by Tom M Mitchell
- The Elements of Statistical Learning
- Learning from Data
- Pattern Recognition and Machine Learning
- Natural Language Processing with Python
- Artificial Intelligence: A Modern Approach
- Artificial Intelligence for Humans
- Paradigm of Artificial Intelligence Programming
- Artificial Intelligence: A New Synthesis
- Superintelligence
- The Singularity is Near
- Life 3.0 – Being Human in the Age of Artificial Intelligence
- The Master Algorithm
Machine Learning Yearning
Author: Andrew NG
Andrew NG’s Machine Learning Yearning is a highly – regarded guide for ML enthusiasts who want to understand the practical aspects of ML in real – world applications. It focuses on teaching you how to make effective decisions while structuring ML projects. NG, a well – known AI expert, shares valuable insights and strategies for creating successful ML systems that can make accurate predictions. The book emphasizes handling issues like bias, variance, and overfitting, and delves into error analysis and data collection.
The Hundred – Page Machine Learning Book
Author: Andriy Burkov
The Hundred – Page Machine Learning Book by Andriy Burkov is a concise yet comprehensive guide to ML fundamentals. Written in a straightforward manner, it aims to make ML accessible to a wide audience, including software developers, data scientists, and business professionals. It covers topics like supervised and unsupervised learning, neural networks, deep learning, reinforcement learning, and support vector machines, along with feature engineering, model evaluation, and selection techniques.
Programming Collective Intelligence
Author: Toby Segaran
Popularly known as PCI, Programming Collective Intelligence is one of the best books to start learning machine learning. It covers topics such as collaborative filtering techniques, search engine features, Bayesian filtering, and Support vector machines, and uses Python to present machine learning in an engaging way.
There are many more books in this list, each offering unique insights into the world of AI and ML. Whether you’re a beginner looking for an introduction or an advanced user seeking to challenge your mind, there’s a book here for you. Remember, while books are a great source of knowledge, applying what you learn to real – world problems is essential for growth in this field.
