The Hundred Page Machine Learning Book

The Hundred Page Machine Learning Book

The Hundred-Page Machine Learning Book by Andriy Burkov is a concise, highly acclaimed guide that distills the vast field of modern machine learning into an accessible, practical primer. It is engineered for exceptional clarity, offering busy practitioners and newcomers exactly what they need to build systems or ace interviews without wading through overwhelming theory.

Key Points

  • Covers essential machine learning algorithms and concepts in a concise format.
  • Includes practical examples and applications for real-world scenarios.
  • Designed for beginners and practitioners looking to enhance their skills.
  • Focuses on clarity, making complex topics accessible and understandable.
,
  • Jake
114
/ 152
The
Hundred-
Page
Machine
Learning
Book
Andriy Burkov
“All models are wrong, but some are useful.”
— George Box
The book is distributed on the “read first, buy later” principle.
Andriy Burkov The Hundred-Page Machine Learning Book - Draft
Preface
Let’s start by telling the truth: machines don’t learn. What a typical “learning machine”
does, is finding a mathematical formula, which, when applied to a collection of inputs (called
“training data”), produces the desired outputs. This mathematical formula also generates the
correct outputs for most other inputs (distinct from the training data) on the condition that
those inputs come from the same or a similar statistical distribution as the one the training
data was drawn from.
Why isn’t that learning? Because if you slightly distort the inputs, the output is very likely
to become completely wrong. It’s not how learning in animals works. If you learned to play
a video game by looking straight at the screen, you would still be a good player if someone
rotates the screen slightly. A machine learning algorithm, if it was trained by “looking”
straight at the screen, unless it was also trained to recognize rotation, will fail to play the
game on a rotated screen.
So why the name “machine learning” then? The reason, as is often the case, is marketing:
Arthur Samuel, an American pioneer in the field of computer gaming and artificial intelligence,
coined the term in 1959 while at IBM. Similarly to how in the 2010s IBM tried to market
the term “cognitive computing” to stand out from competition, in the 1960s, IBM used the
new cool term “machine learning” to attract both clients and talented employees.
As you can see, just like artificial intelligence is not intelligence, machine learning is not
learning. However, machine learning is a universally recognized term that usually refers
to the science and engineering of building machines capable of doing various useful things
without being explicitly programmed to do so. So, the word “learning” in the term is used
by analogy with the learning in animals rather than literally.
Who This Book is For
This book contains only those parts of the vast body of material on machine learning developed
since the 1960s that have proven to have a significant practical value. A beginner in machine
learning will find in this book just enough details to get a comfortable level of understanding
of the field and start asking the right questions.
Practitioners with experience can use this book as a collection of directions for further
self-improvement. The book also comes in handy when brainstorming at the beginning of a
project, when you try to answer the question whether a given technical or business problem
is “machine-learnable” and, if yes, which techniques you should try to solve it.
How to Use This Book
If you are about to start learning machine learning, you should read this book from the
beginning to the end. (It’s just a hundred pages, not a big deal.) If you are interested
Andriy Burkov The Hundred-Page Machine Learning Book - Draft 3
/ 152
End of Document
114
You May Also Like

FAQs of The Hundred Page Machine Learning Book

What are the main topics covered in The Hundred Page Machine Learning Book?
The book covers a wide range of topics in machine learning, including supervised and unsupervised learning, key algorithms like linear regression, decision trees, and support vector machines. It also discusses practical applications of these algorithms in real-world scenarios, making it a comprehensive guide for both beginners and experienced practitioners. Additionally, the book emphasizes the importance of feature engineering, model evaluation, and hyperparameter tuning.
Who is the target audience for The Hundred Page Machine Learning Book?
This book is targeted at both newcomers to machine learning and experienced practitioners looking to refresh their knowledge. It is particularly useful for busy professionals who need a concise yet comprehensive resource to understand key concepts and algorithms without getting lost in excessive theory. Additionally, it serves as a valuable tool for students preparing for technical interviews in data science and machine learning roles.
How does the book approach the topic of machine learning algorithms?
The Hundred Page Machine Learning Book presents machine learning algorithms in a clear and straightforward manner. Each algorithm is explained with practical examples, making it easier for readers to grasp their applications. The book emphasizes understanding the underlying principles of each algorithm, allowing readers to choose the right approach for their specific problems. This practical focus helps bridge the gap between theory and real-world implementation.
What makes this book different from other machine learning resources?
Unlike many extensive textbooks, The Hundred Page Machine Learning Book distills complex topics into a concise format, making it accessible for readers with limited time. It prioritizes clarity and practical applications over overwhelming theoretical details. This approach allows readers to quickly grasp essential concepts and apply them effectively in real-world scenarios, setting it apart from more traditional, lengthy resources.
Can this book help with preparing for machine learning interviews?
Yes, The Hundred Page Machine Learning Book is an excellent resource for preparing for machine learning interviews. It covers key concepts, algorithms, and practical applications that are often discussed in technical interviews. The concise format allows readers to quickly review essential topics, making it a valuable tool for candidates looking to enhance their understanding and confidence before interviews.

Related of The Hundred Page Machine Learning Book