Even if you have zero knowledge about Artificial Intelligence, you can still become an AI expert. Excited to know-how. Then, read this blog post completely
Have you heard this word tsundoku (積ん読)? It’s a japanese word which means having a collection of books in your shelf. That’s what most people do.
They buy books after seeing all the “top 10” articles. That book would be sleeping in your shelf for indefinite time. Money waste, Time waste. I don’t want you to do that.
This blog is a magic wand that reveals the best 10 books that you are looking for.
Most of the industry is using AI to make their product or service more advanced. Lie detectors and brain-reading are some of the fascinating applications of AI.
With AI, you can do process automation, fraud detection, predictive analysis, optimising customer experience, understanding customers better, etc.
Artificial Intelligence is making waves everywhere. Each and every technology rolled out in recent years involves Artificial Intelligence. That’s the power of Artificial Intelligence. AI is booming and there are tons of opportunities waiting for people like you.
Students and professionals are craving to learn Artificial Intelligence and Machine Learning. There are so many ways you can learn AI & Machine Learning.
But, why books?
Books help you understand concepts better. You can learn at your own pace. The content that you see on the Internet is inspired by books. It’s good to learn from the source.
Don’t worry. We have done all the groundwork for you and curated a list comprising the best books on Artificial Intelligence and Machine Learning. Every beginner should read these books to headstart their Data Science career.
1. Artificial Intelligence – A Modern Approach (3rd Edition) Authored By Stuart Russell & Peter Norvig
One of the best books for beginners to kick start learning AI. The authors have explained the concepts in a clear way which is easy to understand. There are technical terms that you might not be familiar with. It doesn’t matter. Those terms are broken down and explained in a clear fashion.
So many topics are covered in this book. Few important concepts include multi-agent systems, local search planning methods, search algorithms, game theory, partial observability,etc.
You need to read the book at least 2 times to register the concepts in your mind. Because initially it takes some to get familiarised with Norvig’s way of teaching.
Rating Factors
Easy to understand
4/5
Writing style
4/5
Engagement
4/5
Topic Depth
5/5
Overall
4.5/5
2. Machine Learning for Dummies Authored By John Paul Mueller and Luca Massaron
Machine Learning for Dummies is best for people who want to put their first foot forward. It covers all the fundamental concepts in a clear and elaborate style. A dose of coding is added to make the learning process spicy.
You get to know Python and R language from this book. The best part of the book is that you learn how to implement the concepts in the real world. Most of the books fail to do this.
2 data science experts crafted this book to provide valuable information in an easy way. Even a layman would find it easy to read.
Rating Factors
Easy to understand
4/5
Writing style
4/5
Engagement
4/5
Topic Depth
5/5
Overall
4.5/5
3. Make Your Own Neural Network
Authored By Tariq Rashid
This is a different kind of book. They have used mathematics of Neural Networks to explain the concepts. They start with a simple idea and then they elaborate it gradually. If you are a skimmer, then you will find this book to be odd.
This book primarily focuses on Neural networks and the concepts are divided into 3 parts.
- The first is all about mathematical concepts which forms the foundation of neural networks.
- Now, its Python. You will learn how to code practically to build neural networks.
- In the third part, you can explore the Raspberry Pi using appropriate codes.
Rating Factors
Easy to understand
4/5
Writing style
3/5
Engagement
4/5
Topic Depth
3/5
Overall
3.75/5
4. Machine Learning: The New AI Authored byEthem Alpaydin
The book on Machine Learning starts from root and explains the evolution in a concise manner. Some decades before, people use machines that are of room size for typing. Traveling back to present, we use mobile phones that fits into hand and does everything.
This is how they have explained the concepts in this book. Regarding Artificial Intelligence, the applications are explained in a clear fashion. It’s all about data privacy and security. You can easily connect with this book because they talk about implications on our day to day lives.
Readers who don’t come from Computer Science background might find this book to be interesting and entertaining.
Rating Factors
Easy to understand
4/5
Writing style
4/5
Engagement
4/5
Topic Depth
3/5
Overall
3.7/5
5. Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies
Authored By John D. Kelleher, Brian Mac Namee, Aoife D’Arcy
This book is featured in this list for a reason. The practical applications are top notch and easy to understand and apply. There are plenty of examples and case studies that help you learn Machine Learning fundamentals. Predictive analysis is vast, but this book makes it easy for us.
No technical terms that makes you keep a dictionary close to you. Four important approaches are illustrated in depth using algorithms and mathematical models. Too much of mathematical flavour makes this book less engaging.
Rating Factors
Easy to understand
4/5
Writing style
4/5
Topic Depth
3/5
Engagement
3/5
Overall
3.5/5
6. The Hundred-Page Machine Learning Book Authored By Andriy Burkov
Hundred page title might tempt you to buy this book because 100 pages means short and easy. Don’t calculate things that way. Always look for what the book provides you. That’s the main thing. I won’t say the book covers topics in depth.
This book is like an introduction. You can say it’s like a trailer. You will get an idea what machine learning is once you read this book. The book covers a wide range of topics, namely deep learning, support vector machines, linear and logistic regression, etc.
Those who want to learn the mathematics behind Machine Learning should definitely buy this book.
Rating Factors
Easy to understand
5/5
Writing style
3/5
Topic Depth
3/5
Engagement
2/5
Overall
3.2/5
7. Artificial Intelligence for Humans Authored By Jeff Heaton
This book is tailor made for those are not from a mathematical background. That’s a big plus. If you basics of algebra and computer programming, then you can just dive in. You will learn distance metrics, linear regression, dimensionality, etc.
There are so many examples and case studies which help readers to practise what they learn. Numerical calculations might feel daunting when you see for the first time. But, just follow the steps one by one and learn things quickly.
Rating Factors
Easy to understand
4/5
Writing Style
3/5
Topic Depth
3/5
Engagement
3/5
Overall
3.2/5
8. Machine Learning for Beginners Authored By Chris Sebastian
The title says everything. From early days to the present day, the evolution of Machine Learning is explained with engaging content. Also, you can learn the importance and influence of big data on machine learning. AI, swarm intelligence, advance neural networks are some of the concepts that hooks you to read more.
Complex mathematical stuff like probability and statistics are explained in such a way that even a layman can understand. Real world applications and examples are real plus.
Rating Factors
Easy to understand
4/5
Writing Style
3/5
Topic Depth
3/5
Engagement
3/5
Overall
3/5
9. Artificial Intelligence: The Basics Authored By Kevin Warwick
The history explained in a simple way, that’s what this book is all about. Past, Present and Future of AI and Machine Learning depiction will leave you thinking about it for a long time. The best part about this book is that they refer other books where you can learn the concepts in depth.
Right from the start, you will get hooked to the book. That’s because the authors have adopted narrative writing style to keep you engaged. This book is a quick read and you can just finish it off in a short time period.
Rating Factors
Easy to understand
3/5
Writing Style
3/5
Topic Depth
3/5
Engagement
3/5
Overall
3/5
10. Machine Learning for Absolute Beginners: A Plain English Introduction Authored By Oliver Theobald
Simple English will reach more people and that’s what readers like you want. Visual representation of any information reaches a wide range of people and engages them to a great extent. Complex algorithms and concepts are represented in simple and subtle manner.
The content is visually rich and appealing, that’s the essence of this book. This is just an introduction, once you read the book you will get a clear cut idea.
Rating Factors
Easy to understand
3/5
Writing Style
3/5
Topic Depth
3/5
Engagement
3/5
Overall
3/5
Conclusion
The content is visually rich and appealing, that’s the essence of this book. This is just an introduction, once you read the book you will get a clear cut idea.
What do you do?
Start with one book. Next question, which book?
Select any one book from this list and also read the description. Then see the table of contents of that particular book. This will help you to get an idea about what you are going to read.
Most of the books mentioned here deal with basics. Once you are clear with the basics, you can accelerate and become an expert. The foundation should be strong and sturdy.
Keep Reading! Keep Learning!