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Python Machine Learning, 1st Edition

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Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics

Key Features


Book Description

Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data - its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.

Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization.

What you will learn


Who this book is for

If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning - whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.

  1. ISBN-10
    9781783555130
  2. ISBN-13
    978-1783555130
  3. Publisher
    Packt Publishing
  4. Publication date
    September 1, 2015
  5. Language
    English
  6. Dimensions
    7.5 x 1.03 x 9.25 inches
  7. Print length
    454 pages

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From the brand

Editorial Reviews

About the Author

Sebastian Raschka

Sebastian Raschka is a PhD student at Michigan State University, who develops new computational methods in the field of computational biology. He has been ranked as the number one most influential data scientist on GitHub by Analytics Vidhya. He has a yearlong experience in Python programming and he has conducted several seminars on the practical applications of data science and machine learning. Talking and writing about data science, machine learning, and Python really motivated Sebastian to write this book in order to help people develop data-driven solutions without necessarily needing to have a machine learning background. He has also actively contributed to open source projects and methods that he implemented, which are now successfully used in machine learning competitions, such as Kaggle. In his free time, he works on models for sports predictions, and if he is not in front of the computer, he enjoys playing sports.

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Sebastian Raschka
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Sebastian Raschka, PhD is an LLM Research Engineer with over a decade of experience in artificial intelligence. His work bridges academia and industry, including roles as senior engineering staff at an AI company and a statistics professor.

As an independent researcher and industry expert, Sebastian collaborates with companies on AI solutions and serves on the Open Source Advisory Board at University of Wisconsin–Madison.

Sebastian specializes in LLMs and the development of high-performance AI systems, with a deep focus on practical, code-driven implementations.

Customer reviews

4.3 out of 5 stars
264 global ratings

Customer Reviews, including Product Star Ratings help customers to learn more about the product and decide whether it is the right product for them.

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Customers say

Customers find the book easy to understand, with clear explanations of machine learning concepts and useful Python code examples. Moreover, the information quality receives positive feedback for its detailed introduction to machine learning and data visualization capabilities, while the writing style and page layout are well-received. Additionally, customers appreciate the author's work and consider the book worth its price. However, the depth of the content receives mixed reviews, with some customers finding it lacking.
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26 customers mention ease of understanding, 19 positive, 7 negative
Customers find the book easy to understand, with clear explanations of machine learning concepts that start with simple examples.
Very well explained Step by step.Easy to followRead more
...I found theexplanations of machine learning quite clear and easily understandable...Read more
...This isnot a beginner's book, and I'm confused by people who are surprised at that - it says as much in the preface....Read more
Excellent, detailed intro into ML.Examples are simple, but understandable. I downloaded book's code from the website listed....Read more
14 customers mention code quality, 13 positive, 1 negative
Customers appreciate the code examples in the book, with one customer noting they are concise and another mentioning that downloading and following them enhances the learning experience.
This book has a lot to offer both in the way of theory andcode....Read more
...It alsohas a lot of code examples (for basically everything)....Read more
...The author does have an iPython notebook repo, whichmakes following the code much nicer; but for reading in bed, not needing to switch back-n-forth...Read more
...Thecodes are pretty helpful but at some point I got weary of reading about how to implement pre-written packages of the algorithms instead of...Read more
14 customers mention information quality, 13 positive, 1 negative
Customers praise the book's information quality, particularly its detailed introduction to machine learning and visualizing data, with one customer noting it provides numerous references for further reading.
...Also agreat reference for visualizing data with a specific purpose in mind. It's very handy to have this book on hand.Read more
...There a lot ofuseful links and other resources scattered throughout the book and the author lets you know where to go if you need more information....Read more
...Thebook has a few useful nuggets, but largely lacks depth and big picture orientation to help master scikit-learn and ML....Read more
...He presents adetailed discussion of back propagation in two separate pieces: one that is intuitive and “top down”; the other a more mathematical, “...Read more
10 customers mention writing style, 10 positive, 0 negative
Customers appreciate the writing style of the book, with one customer noting that all steps are systematically presented.
It's a good andwell written book, covering a lot of different topics. It also has a lot of code examples (for basically everything)....Read more
This is aclearly written and straightforward introduction to some of the latest Python libraries for Machine Learning....Read more
This text isreally well written, but I would not advise someone who isn't intimately familiar with Python to jump straight into this book.Read more
Very well written, was a little past my ability as a coder but was able to digest the concepts and understand it very well. Great resource.Read more
8 customers mention value for money, 7 positive, 1 negative
Customers find the book worth every cent.
...A good intro book to Machine Learning overall and definitelyworth working through. Additionally, it has a lot of value for the price.Read more
A must buy. Great book, author does a good job of explaining topicsRead more
...i bought 3 books. this one was the bestWorth the price and a real reference for my projectRead more
Everything was intact. Good book.Good buy. Thank You.Read more
7 customers mention page layout, 5 positive, 2 negative
Customers find the book well laid out, with one customer noting it is very well organized.
...files that accompany the book using Anaconda extremely useful andwell laid out....Read more
Thebook is very well organized, well written and has good on-line resources for downloading quality examples....Read more
...Then you click to go back to page. Thishorrible formatting makes reading the book tiring and frustrating....Read more
...Finally got the replacement from Amazon. This one is inperfect shape, i.e., no alignment issues at all. Woohoo! Just as promised....Read more
6 customers mention author quality, 5 positive, 1 negative
Customers appreciate the author's work, with one noting their effective use of visualizations throughout the book.
...sebastian is anoutstanding author and hopefully his upcoming deep learning book will be just as good....Read more
...I believe the content is great, theauthor has done a good job, and he's clearly knowledgeable on the subject matter....Read more
...Theauthor does have an iPython notebook repo, which makes following the code much nicer; but for reading in bed, not needing to switch back-n-forth...Read more
...Concepts are displayed clearly and theauthors use of visualizations throughout the book really aids in gaining a working understanding....Read more
6 customers mention depth, 4 positive, 2 negative
Customers have mixed opinions about the depth of the book, with some finding it lacking.
...blend (for me at least) of theory and practice, as well asbreadth and depth....Read more
It's a okay book. Thoughnot much depth.Read more
...Some exploration of GPU computing, withenough detail to permit the reader to experiment with this. Bad: -...Read more
...The breadth of information is immense, and thedepth on each topic is enough that I would consider this a great scikit-learn reference....Read more
Customer image
5.0 out of 5 stars

Images in this review
Great Book.
5 out of 5 stars
Great Book.
In my opinion this is a great book to get you up and running with machine learning. It manages to not only cover the basics but also talks about some of the more advanced topics. There are a couple of things that I really liked about this book. 1. You learn a lot of things that you can't find online and that are APPLICABLE to the real world. Even if you just want to get into machine learning and use it but don't necessarily want to become a data scientist this is a great buy. Machine Learning can be really useful when put into good use. I for example, after reading the book, was able to quickly write up a python program to predict what time I would wake up based on what time I slept, what day it was etc. As well as having tons of fun playing with data from http://archive.ics.uci.edu/ml/ . 2. Although this book is focusing on python the math that you need to implement the algorithms are all there. What's great about that is that I was able to "Translate" most of the examples from the book to C++ code without much hustle. Not only that but the math behind these algorithms made a lot more sense after reading this book. So even if you don't necessarily want to use python but want to gain intuition over how these algorithms work this book will also come in handy. 3. This book isn't just about Machine Learning algorithms. It actually talks quite a bit about preparing and getting good data in general. Which is crucial for every data scientist since almost 80% of your job is getting good data. And another 20% finding a good model and training it. Overall I would say that this book helped me and that I learnt a bunch of new things. If the above didn't convince you (Along with other reviews) here are some small details that made the reading of this book a joyful experience. - I felt that reading the book was actually really fun and motivating since at every chapter there were several examples of applying the theory taught. Which motivated me to move on and read more. - Although this may not seem as important. I have to say that the font of the book as well as the tone of the writing made the reading of the book really comfortable and joyful. I didn't feel that I was getting tired and was easy for me to pick it up where I left off. I have to say though that there where some typos here and there (I thing I found 2-3 in total as well as 2 pictures where swapped) but they were easy to spot so it wasn't that big of a problem. Reasons why you shouldn't buy this book: Unless you are a Machine Learning expert and you look into the deeper insights and more advanced stuff in Machine Learning you shouldn't be looking into buying this book since most of the stuff taught is already known to you. (Although I doubt that you would be looking through this reviews thinking whether to buy it or not in this case). I have also included some pictures. Great Book. Highly Recommend it!
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Top reviews from the United States

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  • Reviewed in the United States on October 10, 2016
    Format: PaperbackVerified Purchase
    In my opinion this is a great book to get you up and running with machine learning. It manages to not only cover the basics but also talks about some of the more advanced topics.

    There are a couple of things that I really liked about this book.

    1. You learn a lot of things that you can't find online and that are APPLICABLE to the real world. Even if you just want to get into machine learning and use it but don't necessarily want to become a data scientist this is a great buy. Machine Learning can be really useful when put into good use. I for example, after reading the book, was able to quickly write up a python program to predict what time I would wake up based on what time I slept, what day it was etc. As well as having tons of fun playing with data from http://archive.ics.uci.edu/ml/ .

    2. Although this book is focusing on python the math that you need to implement the algorithms are all there. What's great about that is that I was able to "Translate" most of the examples from the book to C++ code without much hustle. Not only that but the math behind these algorithms made a lot more sense after reading this book. So even if you don't necessarily want to use python but want to gain intuition over how these algorithms work this book will also come in handy.

    3. This book isn't just about Machine Learning algorithms. It actually talks quite a bit about preparing and getting good data in general. Which is crucial for every data scientist since almost 80% of your job is getting good data. And another 20% finding a good model and training it.

    Overall I would say that this book helped me and that I learnt a bunch of new things.

    If the above didn't convince you (Along with other reviews) here are some small details that made the reading of this book a joyful experience.

    - I felt that reading the book was actually really fun and motivating since at every chapter there were several examples of applying the theory taught. Which motivated me to move on and read more.

    - Although this may not seem as important. I have to say that the font of the book as well as the tone of the writing made the reading of the book really comfortable and joyful. I didn't feel that I was getting tired and was easy for me to pick it up where I left off.

    I have to say though that there where some typos here and there (I thing I found 2-3 in total as well as 2 pictures where swapped) but they were easy to spot so it wasn't that big of a problem.

    Reasons why you shouldn't buy this book:
    Unless you are a Machine Learning expert and you look into the deeper insights and more advanced stuff in Machine Learning you shouldn't be looking into buying this book since most of the stuff taught is already known to you. (Although I doubt that you would be looking through this reviews thinking whether to buy it or not in this case).

    I have also included some pictures.

    Great Book. Highly Recommend it!
    Customer image
    5.0 out of 5 stars
    Great Book.

    Reviewed in the United States on October 10, 2016
    In my opinion this is a great book to get you up and running with machine learning. It manages to not only cover the basics but also talks about some of the more advanced topics.

    There are a couple of things that I really liked about this book.

    1. You learn a lot of things that you can't find online and that are APPLICABLE to the real world. Even if you just want to get into machine learning and use it but don't necessarily want to become a data scientist this is a great buy. Machine Learning can be really useful when put into good use. I for example, after reading the book, was able to quickly write up a python program to predict what time I would wake up based on what time I slept, what day it was etc. As well as having tons of fun playing with data from http://archive.ics.uci.edu/ml/ .

    2. Although this book is focusing on python the math that you need to implement the algorithms are all there. What's great about that is that I was able to "Translate" most of the examples from the book to C++ code without much hustle. Not only that but the math behind these algorithms made a lot more sense after reading this book. So even if you don't necessarily want to use python but want to gain intuition over how these algorithms work this book will also come in handy.

    3. This book isn't just about Machine Learning algorithms. It actually talks quite a bit about preparing and getting good data in general. Which is crucial for every data scientist since almost 80% of your job is getting good data. And another 20% finding a good model and training it.

    Overall I would say that this book helped me and that I learnt a bunch of new things.

    If the above didn't convince you (Along with other reviews) here are some small details that made the reading of this book a joyful experience.

    - I felt that reading the book was actually really fun and motivating since at every chapter there were several examples of applying the theory taught. Which motivated me to move on and read more.

    - Although this may not seem as important. I have to say that the font of the book as well as the tone of the writing made the reading of the book really comfortable and joyful. I didn't feel that I was getting tired and was easy for me to pick it up where I left off.

    I have to say though that there where some typos here and there (I thing I found 2-3 in total as well as 2 pictures where swapped) but they were easy to spot so it wasn't that big of a problem.

    Reasons why you shouldn't buy this book:
    Unless you are a Machine Learning expert and you look into the deeper insights and more advanced stuff in Machine Learning you shouldn't be looking into buying this book since most of the stuff taught is already known to you. (Although I doubt that you would be looking through this reviews thinking whether to buy it or not in this case).

    I have also included some pictures.

    Great Book. Highly Recommend it!
    Images in this review
    Customer imageCustomer imageCustomer imageCustomer imageCustomer imageCustomer imageCustomer imageCustomer image
    30 people found this helpful
    Report
  • Reviewed in the United States on February 7, 2016
    Format: PaperbackVerified Purchase
    I'm a senior undergraduate student in electrical and computer engineering, and decided to make use of machine learning for my senior design project. Having had some experience in python (but not much with matplotlib or scipy), I decided on this book because of all the other good reviews. I have since picked up a few other books related to machine learning, but none can even compare to this. It's stellar! In three weeks I have managed to give myself a comprehensive crash course in classification algorithms using Python which is enough to give me a rolling start on my design project. I am about half way through the book, and apart from very few minor errors (to be expected in a first edition book), I cannot find any faults in it. It's a great resource for someone who wants to learn about machine learning but doesn't know where to start. I'm going to keep an eye out for further books by Mr. Raschka, because his ability to clearly and concisely explain things is superb. Additionally, I enjoy the fact that the book attempts to give a solid foundation on the mathematics behind various machine learning algorithms, since that is enjoyable for someone like me, who always likes to understand what is happening beneath the surface.

    Update: Having finished the book now, I can definitely reaffirm my original position. This is one of the best technical books I have ever read. The last few chapters especially, image recognition with MLP networks and parallelizing networks with Theano and Keras are extremely interesting. I have taken these ideas and applied them in several of my own projects now. Also, as I'm planning on going to graduate school in the very near future, I'm thinking that machine learning and ANNs will likely be at the top of my list of areas to specialize in. The research that is going on in this field is huge, and this book manages to touch at the very base of neural networks, but enough to get your feet wet and show you where to go from there.
    21 people found this helpful
    Report

Top reviews from other countries

  • Y Zhao
    5.0 out of 5 starsgreat book for ML practitioners
    Reviewed in Canada on April 13, 2017
    Format: PaperbackVerified Purchase
    I have been an ML practitioner for years. The majority of my time has been spent on deducting formulas and work with stats models. I like this book as it provides some great tips for ML production in Python. Before reading the book, I did not know some of the utility functions, such as stratified k-fold, are already there in sklearn. Because I do not worry about the theory and the implementation, I quickly flew through the book in days and learned some interesting points.

    I would recommend this book to the software engineers/developers who want to start a career in data science. It may not be a good one for research community as at many points the discussion could be superficial. However, this makes sense as the depth is not the focus of the book:)

    One improvement I expect from the next version(if possible) is the color -- b/w makes the figures extremely hard to follow.
  • Oscar d.
    1.0 out of 5 starsCancelar la adquisición
    Reviewed in Mexico on June 2, 2019
    Format: KindleVerified Purchase
    No me interesa adquirir el producto
  • Miler Silva
    5.0 out of 5 starsAmazing
    Reviewed in Brazil on May 28, 2016
    Format: PaperbackVerified Purchase
    Great intro to machine learning algorithms. Since the author focus mainly on algorithms (using Python's scientific libraries), the explanations may be non-mathematicians friendly.
  • stefano fedele
    5.0 out of 5 starsprima volta con machine learning
    Reviewed in Italy on August 25, 2018
    Format: PaperbackVerified Purchase
    E' stato il mio primo approccio al Machine Learning, avendo una base di matematica e statistica a livello universitario e di programmazione in Python per applicazioni scientifiche (Numpy, Pandas, Scipy, Matplotlib). L'ho trovato molto chiaro e molto bello. Credo sia utile anche per coloro che vogliano approfittare per imparare a lavorare in Python. Gli ultimi 2 capitoli riguardano il deep learning e sembra esser un po l'introduzione di un altro libro da studiare...
  • Daniel Man
    5.0 out of 5 starsGreat Introduction to Machine Learning with Scikit-Learn
    Reviewed in the United Kingdom on March 17, 2016
    Format: PaperbackVerified Purchase
    Great Introduction to Machine Learning with Scikit-Learn! Very well written, lots of examples. Very suitable for machine learning beginners with python experience!
Python Machine Learning, 1st Edition