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Python Machine Learning - Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2nd ed. Edition
There is a newer edition of this item:
Purchase options and add-ons
Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries.
Key Features
- Second edition of the bestselling book on Machine Learning
- A practical approach to key frameworks in data science, machine learning, and deep learning
- Use the most powerful Python libraries to implement machine learning and deep learning
- Get to know the best practices to improve and optimize your machine learning systems and algorithms
Book Description
.
Publisher's Note: This edition from 2017 is outdated and is not compatible with TensorFlow 2 or any of the most recent updates to Python libraries. A new third edition, updated for 2020 and featuring TensorFlow 2 and the latest in scikit-learn, reinforcement learning, and GANs, has now been published.
Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka's bestselling book, Python Machine Learning. Using Python's open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.
Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow 1.x deep learning library. The scikit-learn code has also been fully updated to v0.18.1 to include improvements and additions to this versatile machine learning library.
Sebastian Raschka and Vahid Mirjalili's unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you'll be ready to meet the new data analysis opportunities.
If you've read the first edition of this book, you'll be delighted to find a balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You'll be able to learn and work with TensorFlow 1.x more deeply than ever before, and get essential coverage of the Keras neural network library, along with updates to scikit-learn 0.18.1.
What You Will Learn
- Understand the key frameworks in data science, machine learning, and deep learning
- Harness the power of the latest Python open source libraries in machine learning
- Explore machine learning techniques using challenging real-world data
- Master deep neural network implementation using the TensorFlow 1.x library
- Learn the mechanics of classification algorithms to implement the best tool for the job
- Predict continuous target outcomes using regression analysis
- Uncover hidden patterns and structures in data with clustering
- Delve deeper into textual and social media data using sentiment analysis
Who this book is for
If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential and unmissable resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for developers and data scientists who want to teach computers how to learn from data.
- ISBN-109781787125933
- ISBN-13978-1787125933
- Edition2nd ed.
- PublisherPackt Publishing
- Publication dateSeptember 15, 2017
- LanguageEnglish
- Dimensions9.25 x 7.5 x 1.28 inches
- Print length622 pages
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From the Publisher
What's the key takeaway from your book?
That machine learning can be useful in almost every problem domain. I cover a lot of different subfields of machine learning in my book; by providing hands-on examples for each one of those topics, my hope is that people can find inspiration for applying these fundamental techniques to drive their research or industrial applications.
Also, using well-developed and maintained open source software makes machine learning very accessible to a broad audience of experienced programmers, as well as people who are new to programming. And by introducing the basic mathematics behind machine learning, we can appreciate machine learning being more than just black box algorithms, giving readers an intuition of the capabilities but also limitations of machine learning, and how to apply those algorithms wisely.
What’s new & updated in this second edition of Python Machine Learning?
Oh, where should I start. As time and the software world moved on after the first edition was released in September 2015, we decided to replace the introduction to deep learning via Theano. Don’t worry - we didn't remove it - but it got a substantial overhaul and is now based on TensorFlow, which has become a major player in my research toolbox since its release by Google in November 2015.
Along with the new introduction to deep learning using TensorFlow, the biggest additions to this new edition are three brand new chapters focusing on deep learning applications. In a similar vein to the rest of the book, these new chapters not only provide readers with practical instructions and examples, but also introduce the fundamental mathematics behind those concepts, which are an essential building block for understanding how deep learning works.
What makes this book stand out from other machine learning titles?
I certainly can't speak about all books on the market. However, since the first edition was released, I engaged in countless discussions with my readers, to help them with particular questions and to get their opinion on the parts they found unclear or topics they wish I had covered.
The connection between theory and praxis in particular was what readers found most helpful and somewhat lacking from other introductory texts (which, I heard, were either too theoretical or too practical). This constructive feedback has been invaluable for the second edition, helping me to focus on those parts that were still left unclear.
In a nutshell, the second edition of Python Machine Learning provides a healthy mix of theory and practical examples that most people found so helpful in the first edition, and the second edition adds on top of it with many refinements and additional topics based on the large corpus of invaluable reader feedback.
Editorial Reviews
Review
"I bought the first version of this book, and now also the second. The new version is very comprehensive. If you are using Python - it's almost a reference. I also like the emphasis on neural networks (and TensorFlow) - which (in my view) is where the Python community is heading.
I am also planning to use this book in my teaching at Oxford University. The data pre-processing sections are also good. I found the sequence flow slightly unusual - but for an expert level audience, it's not a major issue."
--Ajit Jaokar, Data Science for IoT Course Creator and Lead Tutor at the University of Oxford / Principal Data ScientistAbout the Author
Product details
- ASIN : 1787125939
- Publisher : Packt Publishing; 2nd ed. edition (September 15, 2017)
- Language : English
- Paperback : 622 pages
- ISBN-10 : 9781787125933
- ISBN-13 : 978-1787125933
- Item Weight : 2.35 pounds
- Dimensions : 9.25 x 7.5 x 1.28 inches
- Best Sellers Rank: #628,914 in Books (See Top 100 in Books)
- #195 in Business Intelligence Tools
- #336 in Data Processing
- #601 in Python Programming
- Customer Reviews:
About the authors
Sebastian Raschka is a deep learning & AI researcher with a strong passion for education. He is best known for his work on open source projects.
After his PhD, Sebastian joined the University of Wisconsin-Madison as a professor in the Department of Statistics, where he focused deep learning and machine learning research until 2023. He joined Lightning AI in 2022, where he currently focuses on AI and LLM research, developing open-source software, and creating educational material.
Discover more of the author’s books, see similar authors, read book recommendations and more.
Customer reviews
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Learn more how customers reviews work on AmazonCustomers say
Customers find the book practical and useful for learning machine learning concepts. It provides a good overview of how to tackle learning problems with examples and scripts. They consider it worth the price, though some readers find the writing style difficult to read and disrespectful to readers.
AI-generated from the text of customer reviews
Customers find the book provides a practical guide on Python machine learning. They appreciate the reasonable explanations of math and code examples. The book is described as a key reference in deep learning, making heavy use of the scikit-learn library.
"...There is linear algebra, concepts like minimizing cost functions, bias/variance tradeoff, learning from errors, etc...." Read more
"...'s not particularly an "intro course to M.L.", but it contains enough details that you could easily follow along and learn how to use the..." Read more
"Book gives a good overview of how to tackle a learning problem. Preparing learning data and evaluation of learning model...." Read more
"...It makes heavy use of the scikit-learn library; and the latter chapters give an excellent high-level overview of TensorFlow...." Read more
Customers find the book helpful and worth the price. They mention it covers linear algebra and concepts like minimizing cost functions and bias/variance tradeoffs.
"...There is linear algebra, concepts like minimizing cost functions, bias/variance tradeoff, learning from errors, etc...." Read more
"...This book is clearly worth the price. The level of technical content (AI and Python) is quite high...." Read more
"This book REALLY helps. It worth the price...." Read more
"Good quality! Great value! The size fit perfectly as well." Read more
Customers find the book hard to read and disrespectful to readers. They also say the writers are lazy.
"...It is VERY respectless to the readers...." Read more
"...I'm giving this book one star because the writers are lazy--they ultimately just repackaged their previous edition into a new book." Read more
"Hard to read..." Read more
Reviews with images
Good book for starters in Neural Networks
Top reviews from the United States
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- Reviewed in the United States on March 12, 2018This book is excellent for the following demographic:
People who already have a decent level of skill and experience in statistics who want to:
- 1) Elevate their understanding of ML techniques without absolutely breaking their skull on dense theory
- 2) Learn how to implement the algorithms in Python and gain moderate proficiency in sci-kit learn
I would say it's not a beginner's book, but more for intermediates. I am half-way through and find it a little challenging, but definitely attainable. This balance I consider to be putting me right in the sweet spot for learning. To judge whether you're a good candidate for this book, you can compare your experience and skill to me :
I started this book after earning a PhD in the social sciences, which basically gave me good coverage in inferential and applied statistics (T, F distributions, p-values, confidence intervals, linear regression, one-way and factorial ANOVA, PCA, etc.). I also took a machine learning graduate course at my university and a few online courses in introductory ML for R. All of this background gave me solid grounding in statistics. With all this I still find this book somewhat challenging, but definitely not too hard. I'd say without my background I would find this book hard to get through. There is linear algebra, concepts like minimizing cost functions, bias/variance tradeoff, learning from errors, etc. So, if you are just starting out or reading the previous sentence and don't know what I'm talking about, I would recommend learning more stats fundamentals before starting this.
After you gain some proficiency in stats, come learn this book and elevate your understanding of the algorithms, add nuance to them, integrate them into your mental conceptual structures more fully (e.g. you'll know more nuances of ML, e.g. which subsets of algorithms are preferred for controlling more of the bias, variance, how random forest is basically bagging with a twist, how adaboost's treatment of classification errors has kind of an element of perceptron implementation, and many more).
- Reviewed in the United States on August 14, 2018This book will stay on your reference shelf for years to come!
The authors clearly have taught these materials many times before, and their significant mathematical and technical prowess is delivered using a very approachable style. This book seems best suited for someone who wants to sit down and begin to apply Python Machine Learning to a problem that they already know they have. It's not particularly an "intro course to M.L.", but it contains enough details that you could easily follow along and learn how to use the various tools and techniques of the field if you've never seen or heard of them before.
The copious notes scattered throughout this book are pure gold, mined from the obvious experiences of the authors while working in the field. If there ever is a Machine Learning equivalent to the venerable "Forrest M. Mims Engineering Notebook" for electronics, I feel these two authors could write it!
Once you use this book to work on your current M.L. problem in Python, you will find yourself returning to it as a reference for other problems in the M.L. space. Its lucid explanations will help reinforce the topics presented, and cement your understanding of the materials.
This book will get you writing Python Machine Learning code to work your current M.L. problem in no time flat!
- Reviewed in the United States on November 10, 2018Book gives a good overview of how to tackle a learning problem.
Preparing learning data and evaluation of learning model.
Witch python libraries to use and a lot of examples.
Was very useful l for me
Thanks guys
4.0 out of 5 stars Good book for starters in Neural NetworksBook gives a good overview of how to tackle a learning problem.
Reviewed in the United States on November 10, 2018
Preparing learning data and evaluation of learning model.
Witch python libraries to use and a lot of examples.
Was very useful l for me
Thanks guys
Images in this review - Reviewed in the United States on September 25, 2017(I own the 1st edition, and was given early access to a pre-release PDF of the 2nd ed. My paperback copy just arrived.)
This is the best book I've seen for professional software engineers to bootstrap themselves into Data Science, Machine Learning and (with the 2nd ed) Deep Learning. It makes heavy use of the scikit-learn library; and the latter chapters give an excellent high-level overview of TensorFlow. Books in this space can often feel either too basic or too academic. Not this one -- for me it hits the sweet spot of explaining and doing.
What I love about Raschka's writing is how he builds up from theory to practical code. It lays out the concepts, math, and code together which helps comprehension. So, if you happen to be rusty in math, like me, you can look to the code to help explain what the equations actually do. The chapters of the book build up from each other; so many of the examples feel like they can be used as recipes for building your own custom models.
- Reviewed in the United States on August 8, 2018Very steep learning curve.
I almost gave up in chapter two at perceptron but since that algorithm is the foundation of all I spent a whole week to understand it. The code the author uses is pretty much optimized and it was not in sync with the mathematical introduction. But the first 30 pages are absolutely neccessary to read and understand deeply in order to move on.
After page 30 it became a little faster to proceed with the book since topics from page 30 - 107 are mostly the extension of the perceptron. At page 107- 160 I am already accustomedto the authors style and to the books logic so it is now quite effective to read and digest the models.
And that is where I am at the moment. I gave this book 5 stars since I wanted a high quality ML and python book which leads me through the models in a step-by-step way no matter how hard it is mathematically or programmtechnically. And I got this.
negative:
The pdf version has color pictures which is nice especially for multiline charts ( like page 212) where the b&w book just visually flat and some chart elements cannot be identified.
Top reviews from other countries
- SatyakrishnanReviewed in India on May 3, 2022
5.0 out of 5 stars An excellent beginner's book
This is one of the best beginner's books out there. If anyone wants to start ML they have to go through this book, although the DL part of the book uses TF version 1 which is not used anymore. You will also learn a lot of numpy, pandas and matplotlib features
- XunReviewed in Canada on September 15, 2019
5.0 out of 5 stars Nice book for implementation
Nice book that constructs a bridge between theory and implementation. It doesnt include detailed theory. But it mentions many methods that can help one know the knowledge framework that can facilitate future study.
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VladimirtwitterReviewed in Brazil on May 4, 2018
5.0 out of 5 stars Good Book!
Sabastian Raschka is a Data Scientist, in this book he goes deeper into machine learning problems, using mathematical approaches and several examples.
- GGReviewed in Italy on August 6, 2019
5.0 out of 5 stars I love this book. S U P E R!!
I love this book. It provides a very good view about ML..with a lot of tips & tricks. Very clear and well written. The deep learning part has not yet fully covered, but it can provides a good basic overview.
GG
Reviewed in Italy on August 6, 2019
Images in this review -
Daniel S.Reviewed in Spain on November 18, 2018
5.0 out of 5 stars Muy ilustrativo, con explicación teórica y ejemplos de código
Recomendable 100%.
En la descripción de los diferentes algoritmos incluye descripción teórica, implementación pura en Python y su uso con scikit learn.