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Grokking Machine Learning
Discover valuable machine learning techniques you can understand and apply using just high-school math.
In Grokking Machine Learning you will
Supervised algorithms for classifying and splitting data
Methods for cleaning and simplifying data
Machine learning packages and tools
Neural networks and ensemble methods for complex datasets
Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using Python and readily available machine learning tools. Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the technology
Discover powerful machine learning techniques you can understand and apply using only high school math! Put simply, machine learning is a set of techniques for data analysis based on algorithms that deliver better results as you give them more data. ML powers many cutting-edge technologies, such as recommendation systems, facial recognition software, smart speakers, and even self-driving cars. This unique book introduces the core concepts of machine learning, using relatable examples, engaging exercises, and crisp illustrations.
About the book
Grokking Machine Learning presents machine learning algorithms and techniques in a way that anyone can understand. This book skips the confused academic jargon and offers clear explanations that require only basic algebra. As you go, you’ll build interesting projects with Python, including models for spam detection and image recognition. You’ll also pick up practical skills for cleaning and preparing data.
What's inside
Supervised algorithms for classifying and splitting data
Methods for cleaning and simplifying data
Machine learning packages and tools
Neural networks and ensemble methods for complex datasets
About the reader
For readers who know basic Python. No machine learning knowledge necessary.
About the author
Luis G. Serrano is a research scientist in quantum artificial intelligence. Previously, he was a Machine Learning Engineer at Google and Lead Artificial Intelligence Educator at Apple.
Table of Contents
1 What is machine learning? It is common sense, except done by a computer
2 Types of machine learning
3 Drawing a line close to our Linear regression
4 Optimizing the training Underfitting, overfitting, testing, and regularization
5 Using lines to split our The perceptron algorithm
6 A continuous approach to splitting Logistic classifiers
7 How do you measure classification models? Accuracy and its friends
8 Using probability to its The naive Bayes model
9 Splitting data by asking Decision trees
10 Combining building blocks to gain more Neural networks
11 Finding boundaries with Support vector machines and the kernel method
12 Combining models to maximize Ensemble learning
13 Putting it all in A real-life example of data engineering and machine learning
In Grokking Machine Learning you will
Supervised algorithms for classifying and splitting data
Methods for cleaning and simplifying data
Machine learning packages and tools
Neural networks and ensemble methods for complex datasets
Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using Python and readily available machine learning tools. Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the technology
Discover powerful machine learning techniques you can understand and apply using only high school math! Put simply, machine learning is a set of techniques for data analysis based on algorithms that deliver better results as you give them more data. ML powers many cutting-edge technologies, such as recommendation systems, facial recognition software, smart speakers, and even self-driving cars. This unique book introduces the core concepts of machine learning, using relatable examples, engaging exercises, and crisp illustrations.
About the book
Grokking Machine Learning presents machine learning algorithms and techniques in a way that anyone can understand. This book skips the confused academic jargon and offers clear explanations that require only basic algebra. As you go, you’ll build interesting projects with Python, including models for spam detection and image recognition. You’ll also pick up practical skills for cleaning and preparing data.
What's inside
Supervised algorithms for classifying and splitting data
Methods for cleaning and simplifying data
Machine learning packages and tools
Neural networks and ensemble methods for complex datasets
About the reader
For readers who know basic Python. No machine learning knowledge necessary.
About the author
Luis G. Serrano is a research scientist in quantum artificial intelligence. Previously, he was a Machine Learning Engineer at Google and Lead Artificial Intelligence Educator at Apple.
Table of Contents
1 What is machine learning? It is common sense, except done by a computer
2 Types of machine learning
3 Drawing a line close to our Linear regression
4 Optimizing the training Underfitting, overfitting, testing, and regularization
5 Using lines to split our The perceptron algorithm
6 A continuous approach to splitting Logistic classifiers
7 How do you measure classification models? Accuracy and its friends
8 Using probability to its The naive Bayes model
9 Splitting data by asking Decision trees
10 Combining building blocks to gain more Neural networks
11 Finding boundaries with Support vector machines and the kernel method
12 Combining models to maximize Ensemble learning
13 Putting it all in A real-life example of data engineering and machine learning
512 pages, Paperback
Published December 14, 2021
63 people are currently reading
390 people want to read
About the author
Luis G. Serrano
4Ìýbooks14ÌýfollowersLuis G. Serrano is a research scientist in quantum and classical machine learning, living in Toronto, Canada. He has a PhD in mathematics from the University of Michigan, has taught at the University of Quebec and Quest University, and has worked in machine learning at Apple, Google, and Udacity. He maintains a popular educational youtube channel, .
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Displaying 1 - 8 of 8 reviews
December 3, 2020
The word "grokking" means to "understand profoundly and intuitively", and in Grokking Machine Learning, Luis Serrano helps his readers grok the key concepts and techniques of Machine Learning by sharing the knowledge he has gained from his experience at some of the world's most famous organizations: Apple, Google/YouTube, and Udacity.
I am currently teaching a graduate course called Foundations of Artificial Intelligence, and in preparation for the first module on Machine Learning a few weeks ago, I decided to turn the class over to the students. They split into groups, watched one of Luis's YouTube videos (some of which have been seen by 800,000 people), and gave mini-lectures to teach each other the core concepts of Decision Trees, Logistic Regression, Support Vector Machines, and k-Means Clustering.
They will understand these concepts even better once they read Luis's new book on Grokking Machine Learning! I highly recommend this amazing resource.
I am currently teaching a graduate course called Foundations of Artificial Intelligence, and in preparation for the first module on Machine Learning a few weeks ago, I decided to turn the class over to the students. They split into groups, watched one of Luis's YouTube videos (some of which have been seen by 800,000 people), and gave mini-lectures to teach each other the core concepts of Decision Trees, Logistic Regression, Support Vector Machines, and k-Means Clustering.
They will understand these concepts even better once they read Luis's new book on Grokking Machine Learning! I highly recommend this amazing resource.
December 20, 2023
This book, if you start at the beginning, starts off well, and then, after you get to the training and test sets, it starts to get confusing. The author says that the example violates the law, but I don't see the violation, and the author doesn't make a good case for it.
All in all a very bad book, to start machine learning should read, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
All in all a very bad book, to start machine learning should read, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
July 8, 2023
I was inspired by what neural networks can do and decided to dig deeper into the deep learning concepts. I stumbled upon this book after being dissapointed by many other books on the same topic. After the first few chapters I felt that this was great advice as I've spent more than two years trying to understand basic deep learning concepts and made no progress. The 'aliens' examples in the book I found to be very useful and I think that's what made it click. The book covers many deep learning concepts, simplifies complex concepts enough for simplicity of explanation and provides appropriate explanations with relevant examples.
It's important to keep in mind that no book is perfect, and there are errors and mistakes present in the data tables and programming examples. If you read the book carefully, work out the examples, and play with the codebase provided, you will have learned a substantial amount of deep learning concepts and you will not be bothered by any errors.
Nevertheless astonishing authoring skills and ability to communicate. The author style is engaging, the content well motivated, and he does not hide the maths - without being hard to follow.
It's important to keep in mind that no book is perfect, and there are errors and mistakes present in the data tables and programming examples. If you read the book carefully, work out the examples, and play with the codebase provided, you will have learned a substantial amount of deep learning concepts and you will not be bothered by any errors.
Nevertheless astonishing authoring skills and ability to communicate. The author style is engaging, the content well motivated, and he does not hide the maths - without being hard to follow.
March 1, 2021
The easiest way to learn machine learning for techies and non-techies alike. Highly recommended!
May 3, 2024
Loaded with math but also with good explanations and examples. Rather easy to read but still requires quite a bit of focus. Enjoyed it very much and would totally recommend to anyone interested in machine learning.
August 15, 2023
Thank you to the author for writing this book!! Made concepts easy to understand and gave great practical advice and tips. Love this.
December 7, 2024
Goes over some details of machine learning in a readable and understandable way. Good review of concepts.
April 16, 2025
Really liked it, nice, simple, easy to follow text about machine learning systems.
Displaying 1 - 8 of 8 reviews
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