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Python Machine Learning

Sebastian Raschka

4.27 · Rating details · 583 ratings · 29 reviews

Link to the GitHub Repository containing the code examples and additional material: https://github.com/rasbt/python-machi...

Many of the most innovative breakthroughs and exciting new technologies can be attributed to applications of machine learning. We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine ...more

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Paperback, 454 pages

Published October 1st 2015 by Packt Publishing (first published September 23rd 2015)

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Lists with This Book

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Dec 21, 2015 Pēteris rated it it was amazing

This is a great book.

It gives the mathematical definitions of popular machine learning algorithms and shows you how to implement them. Then it explains how to use them with scikit-learn which has much more efficient implementations.

What is great is that this book has chapters on data cleaning, what to do with missing data, etc. Compliments greatly the Andrew Ng's ML course which lacked lectures about all of these things.

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May 16, 2016 Hashem Koohy rated it it was amazing

This is a fantastic introductory book in machine learning with python. It provides enough background about the theory of each (covered) technique followed by its python code. One nice thing about the the book is that it starts implementing Neural Networks from the scratch, providing the reader the chance of truly understanding the key underlaying techniques such as back-propagation. Even further, the book presents an efficient (and professional) way of coding in python, key to data science.
I str ...more

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Mar 19, 2016 Tihomir rated it really liked it

Shelves: 2016_tech, big-data, programming

The book aims to cover a lot of topics on Machine Learning. The chapters on training Machine Learning algorithms and clustering analysis were very useful. However, embedding machine learning into web applications and training with Theano were a bit out of the scope.

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Feb 15, 2020 Luis Lector rated it it was ok · review of another edition

El original es muy buen libro para conocer el Aprendizaje Automático, pero esta traducción al español de Marcombo tiene muchos errores que habrá que resolver el próximas ediciones.

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Apr 11, 2018 Fuzzball Baggins rated it it was amazing

Really good book, useful. Nice concise explanations, the maths is all included, and there's even example code! I downloaded a pdf version to my computer because I think I'll be referring to it pretty often.

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Jun 06, 2020 Nico Ogawa rated it really liked it

** I think it is important to disclose your skill level when reviewing these types of textbooks. The complete beginner, as opposed to a statistics major with minimal programming experience will gain different essential skills from the textbook. I am an Economics major with fundamental knowledge in most of the rudimentary parts of Machine Learning (Statistics, programming, analytics). I also read PY4E, Intro to Statistical Learning, and Python for Data Analysis before this. (First 2 are available ** I think it is important to disclose your skill level when reviewing these types of textbooks. The complete beginner, as opposed to a statistics major with minimal programming experience will gain different essential skills from the textbook. I am an Economics major with fundamental knowledge in most of the rudimentary parts of Machine Learning (Statistics, programming, analytics). I also read PY4E, Intro to Statistical Learning, and Python for Data Analysis before this. (First 2 are available online for free by the way) **

Dr. Raschka hits a personal sweet spot as he offers a lot more mathematical theory compared to most of the other introductory Python Machine Learning textbooks I browsed through. This ultimately lead me to picking this text based on my intermediate programming level and background knowledge of machine learning.

Aside from the math, I loved his occasional coding-from-scratch approach to models like the Perceptron, Adaline and even a neural network! I think that this under-the-hood approach is tedious in isolation for every model and could easily turn to be overwhelming for a beginner, but it improves your skill to think computationally(Which I still feel like I lack) and not just take advantage of the readily available libraries. Another huge plus was that Dr. Raschka puts links to academic papers and lecture slides for supplementary information. They provides an extensive, but mainly theoretical approach to some models. If you feel a little lost with some of the brief algorithm descriptions, these can really help bring them to clarity.

That being said, I sometimes found the algorithms vaguely written, and there could have been a greater emphasis on the assumptions of each model's implementation. But there are plenty of resources out there to help demystify any misinterpretations you have.At times, I felt they could’ve touched on some other models like QDA. mixture models, and Partial Least Squares. But it is an introductory textbook and cannot give you everything, I would have found a couple more hands-on approaches of end-to-end pipelining helpful so the reader feels ready to undertake their own mini-project at the end.

I liked this book and it was a great introduction to some important models. It also gives you a basic grasp of libraries like sci-kit learn, nltk, and TensorFlow (I say fundamental because sci-kit learn user guide is over 2,700 pages long). In my opinion, I think this book is well-suited for a reader with a quantitative background (Stats, Comp Sci, Engineering etc.) as the proofs and algorithms can get a little hairy for a complete beginner. I would read this with my Jupyter notebook open, and with access to other library manuals open (Numpy, sci-kit learn, pandas etc) in the browser to get the most out of this book.

I was motivated to write a review for this textbook because I have found there is an ‘information overload’(Packt, Oreilly, and Manning are all pushing out books like the world is ending tomorrow) for data science textbooks which make it incredibly difficult to pick which one to avenue to take. I have frequently changed my mind on which activity to do next.. I’ll think about reading an NLP-based textbook, to a pure python based textbook , to doing Kaggle competitions, then to MOOCs, or to YouTube tutorials. Finding an appropriate resource isn’t easy and it’s different for everyone. The software engineer would understand the from-scratch python code very quickly while a math major would whip right through the mathematical proofs.

Everybody has a different path to becoming a self-taught data scientist and I just wanted to share my personal experience with the textbook. If anybody has questions I can also happily answer them and I'd love to hear any experienced reader's recommendations/ advice for me as well: (As long you’re not a YouTuber pushing some junk-grade course)

I am looking for a textbook that is heavily applied which works through ML project(s) or application(s) and is also directed for the intermediate to advanced level. Specifically, Something challenging but that will mainly help give me guidance and advice on how to get a personal project off the ground.

Thanks!

9/10 ...Out of 10 scale is taken from my friend Ryan Bergen who also invoked me to write the review. ...more

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Aug 27, 2018 Michael Burnam-Fink rated it it was amazing · review of another edition

Shelves: 2018, data-science, academic

This a fantastic introduction to machine learning.

Textbooks in computer science in general, and machine learning in particular, have to walk a delicate line. At one level of high abstraction, everything is mathematical proofs. At a level of low-level cookbookls, it's a matter of just plugging and chugging, treating code as magical invocations without getting at the why. Raschka's book hits the sweet spot between the two exactly, explaining the underlying math, how that math is represented in Pyt ...more

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Jul 21, 2019 Minervas Owl rated it really liked it

This is a book for starters. It combines introductions to machine learning and its python implementations (scikit-learn and others), but does not go deep into either of them. If you have already taken online courses on machine learning or read introductory materials, you wouldn't learn much from the book. Among the accompanying python codes, I find the graphing ones most useful.

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May 10, 2020 Ewan rated it really liked it

A good practical tour round your bread and butter ML techniques with scikit learn, not the most efficient library for the more complex methods such as gradient boosting or DL but good for SVM and regression techniques and the book offers solid explanations and examples .

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Jan 26, 2016 BCS rated it it was amazing

There is significant interest, across many diverse application domains, in developing applications that exploit the large and rich data sets that are acquired by computer systems in order to identify trends and correlations and to make predictions and recommendations. These kinds of applications broadly fit into the field of machine learning (ML).

This excellent book is a practical introduction to ML using the Python programming language, along with relevant components of Python’s rich open-sourc There is significant interest, across many diverse application domains, in developing applications that exploit the large and rich data sets that are acquired by computer systems in order to identify trends and correlations and to make predictions and recommendations. These kinds of applications broadly fit into the field of machine learning (ML).

This excellent book is a practical introduction to ML using the Python programming language, along with relevant components of Python’s rich open-source ecosystem of machine learning, numerical computing, graph plotting and other cognate libraries.

The author does not assume any prior ML knowledge, though a basic understanding of the Python language, linear algebra and calculus is useful. An introductory chapter introduces basic machine learning terminology, describing the various types of machine learning, and overviewing the key steps of typical machine learning processes. This introduction also discusses, in general terms, how to install and configure Python and the various libraries required by the remainder of the book.

As may be expected, a significant part of the book is given to the discussion of a variety of different learning algorithms, including the mathematical details of how they work, how they are trained and how they perform against other algorithms. Crucially, the book describes how to use library implementations of each algorithm.

While ML algorithms play a central part in any machine learning application, there are a variety of other issues involved in making these algorithms work effectively in any particular application. The book therefore contains comprehensive discussion of key topics such as data exploration, visualization and preparation, techniques for generating test and validation data sets, algorithm training and evaluation.

The author describes appropriate tools and techniques that can assist with these processes. To help put ML in context, a chapter is also devoted to describing a simple but complete online ML system embedded inside a web application.

The book covers a considerable amount of material over its four hundred pages. Rather than being a dry exposition of ML techniques, the author encourages readers to engage with the text by experimenting with the examples. Naturally, good data sets are required and the book provides links to a variety of real-world data sets that are freely available for download. In addition, the source code for the examples may be downloaded from the book’s website.

Open-source libraries are used extensively, providing efficient implementations and enabling the reader to focus on the machine learning application itself rather than getting bogged down with implementational detail. The text contains lots of practical advice as well as inline links to other sources of information. While this book is a good end-to-end read, it would also serve as a useful reference text. Recommended.

Review by Patrick Hill CEng MBCS CITP
Originally posted http://www.bcs.org/content/conWebDoc/... ...more

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Oct 19, 2017 Samaneh rated it really liked it

This book is a tour of Machine Learning. If you are a complete beginner on the topic, you’ll get the big picture. However it is up to you to go deeper on each concept. I think there could have been a final chapter with a few exercise which incorporated all the concepts which were covered in the book.

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Jan 02, 2017 Oleksandr Fialko rated it really liked it

A good book to learn scikit-learn library. Chapters on theano and embedding the code into a web application are out of scope. I would use scikit-flow instead of theano, since the former is similar to scikit-learn.

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Jan 31, 2019 Robert rated it it was amazing

Highly recommend. Several years ago I was a scientist looking to learn machine learning. This book hit the perfect sweet spot between technical accuracy and accessibility and gave me a great foundation. Highly recommend for an accessible, but not watered down introduction. Early in the book you will write your own neural network and keep going from there. I give this to other scientists who want to learn machine learning.

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Sep 19, 2018 Robert Muller rated it it was amazing · review of another edition

A solid and practical intro to machine learning

The combination of reasonably explained math and excellent code examples are what make this book excellent. It also always gives a good reference for going deeper.

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Jun 11, 2017 Elie De Brauwer rated it it was amazing

Shelves: technical, non-fiction, read-in-english

Okay, not 5 stars, 4.75 really. The book finds a sweet balance between mathematics and python, although throughout reading the book I found the balance was shifting a but (in the bad sense of shifting). One of the better books I've read on this subject.

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Feb 18, 2018 Aileen rated it it was amazing

Shelves: code-and-design, personal-favorites

I really appreciate the well documented code repository on github. Book was easy to follow despite being relatively math heavy. Explanations on common ML algorithms and clustering analysis were on point and easy to follow. This book is a good reference point for learners.

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Sep 30, 2018 Frank rated it it was amazing

This is a good book that combines mathematical explanation, intuition, python implementation, and off-the-shelf model usage. For my background, this is a very logical way of approaching machine learning.

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May 02, 2018 Reza Rahutomo rated it it was amazing

My baseline in Machine Learning research

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Jan 19, 2019 Renata Galdino rated it it was amazing

Excelente livro.

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Apr 12, 2020 MrWeckx rated it it was amazing

Shelves: python, machine-learning

Perfect book for introduction on machine learning to intermediate level. Very comprehensive for graduate or undergraduate students.

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Apr 25, 2019 Rafal Klat rated it liked it

Lots of examples (every idea has an example), but...

...aaaaa, Polish translation is awful!!!!! I hope that original English version doesn’t have this huge disadvantage.

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Aug 12, 2017 Christian Alvarez rated it it was amazing

Challenging, yet so rewarding for the Microsoft Imagine Cup Competition

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Mar 09, 2018 Hungy Ye rated it it was amazing

The best intro Python ML book, becuase it actually tries to teach you. Read the Author's reponse to 'why this book' on the goodreads page, it's accurate.

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Jan 17, 2016 Rafal Szymanski rated it it was amazing

A great book, it will teach you exactly what it promises - how to use the most common ML algorithms using Python and libraries like sklearn/numpy/pandas.

I found it to have a great balance between the theoretical math and implementation in Python; the split is somewhere around 20/80 in favour of implementation and actually using the algorithms on real data-sets. If you are trying to get a good understanding of the theory, then this book is a good starting point but you will most definitely need t A great book, it will teach you exactly what it promises - how to use the most common ML algorithms using Python and libraries like sklearn/numpy/pandas.

I found it to have a great balance between the theoretical math and implementation in Python; the split is somewhere around 20/80 in favour of implementation and actually using the algorithms on real data-sets. If you are trying to get a good understanding of the theory, then this book is a good starting point but you will most definitely need to supplement it with something else. I would recommend it even if you have no previous experience with Machine Learning. It's a good topic to approach top-down by seeing how you can actually use ML with some easy-to-use Python libraries and get great results, than to get bogged down in mathematical details and discouraged from further exploration.

In short, the book covers all the popular supervised and unsupervised learning algorithms. You will learn techniques for feature selection, feature extraction, hyperparameter tuning, and model evaluation. The final two chapters will introduce you to artificial neural nets and running them on GPUs using the Python libraries Theano and Keras. The emphasis is on being hands-on, so you will be working along in an iPython notebook, downloading data-sets, and following the book on how to create your models in the sklearn library. The book also has an associated github repository with the iPython notebooks which is handy.

Estimated time commitment: 20-35 hours depending on your level of interest in following along with deriving the mathematics behind the algorithms and on whether you have any previous experience with ML.

Here's what you will learn the basic theory of and how to do it in Python:

* Perceptrons, ADALINE (Adaptive Linear Neurons), classification with linear regressors, L1 and L2 regularization
* Support Vector Machines (SVM), Principal Component Analysis (PCA), kernel functions, kernel trick, kernel PCA, decision trees, random forests, K-nearest-neighbour.
* Data pre-precessing, handling missing data (data imputation), one-hot encoding for categorical features, data standardization, data normalization, test/train set splitting, L1 norm as feature selector, One-vs-Rest classification for multi-class labelling, random forest as feature selector.
* Unsupervised dimensionality reduction using PCA, supervised dimensionality reduction using Linear Discriminant Analysis (LDA), kernel PCA for non-linear mappings (using kernel trick to project into higher-dimensional space)
* Model evaluation, pipelines in sklearn, k-fold cross validation, stratified k-fold cross validation, learning and validation curves, confusion matrices, precision and recall rates, grid-search for hyperparameter tuning.
* Ensemble learning, majority-vote classification, bagging, classification with weak classifiers using boosting (AdaBoost)
* Worked example of sentiment analysis of IMDB movie reviews using linear regression with bag-of-words model and minibatch gradient descent for large data set optimization. NLP terms like idf, tf-idf, word2vec.
* Worked example of creating a web app with Flask for IMDB movie review sentiment analysis.
* Predicting continous variables (regression), worked example on housing prices data-set, Ordinary Least Squares (OLE) regression, RANSAC for reducing bias and variance due to outliers, Mean Squared Error (MSE), R^2, L1 and L2 regularization, Elastic Nets, polynomial linear regression, scatter matrix plots, nonlinear relationships with random forests,
* Unsupervised learning, k-means, k-means++, agglomerative clustering into hierarchical trees, dendrograms, high-density clustering using DBSCAN
* Artificial Neural Nets (ANNs), multi-layer perceptrons, handwritten digit recognition from the MNIST data-set, backpropagation, gradient checking, convolutional neural nets, recurrent neural nets
* Using Theano and Keras to implement a multi-layer perceptron on GPU, choosing activation functions ...more

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May 07, 2016 Haozhe Xu rated it it was amazing

Finished this book a while back. The author Sebastian really did a good job at explaining how those machine learning algorithms work. What I especially like about the book is that not only the author explain the math behind the scene very well but also show some really hands-on examples in python code. At the end of the book, Sebastian discussed a bit about deep learning which is very interesting. Currently I am reading Grokking Deep Learning hoping to get a good understanding of it.

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Jun 07, 2017 f1yegor rated it really liked it

Good book with intuition, examples and implementation of many ML algorithms.

In my version of book I've found a bug in code implementation in chapter 3. That leads to wrong conclusion. Hope it's fixed in errata somewhere.

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Nov 04, 2016 Delhi Irc added it

Shelves: new-arrival-4-nov-2016

Location: ND6 IRC
Accession No: DL028603

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Apr 25, 2016 David rated it really liked it

Great introduction on machine learning in Python. Good to have access to code: https://github.com/rasbt/python-machi... ...more

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Jul 01, 2017 Mayank Prakash rated it really liked it

Recommended to Mayank by: Search Results

This is an amazing book!! I mean it does explain what is being done and how its being done!! Nice Nice Book.. Helped a lot in learning.. and is helping as well :-)

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Michael Baron rated it it was amazing
Apr 08, 2016

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About Sebastian Raschka

Sebastian Raschka

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Some of my greatest passions are "Data Science" and machine learning. I enjoy everything that involves working with data: The discovery of interesting patterns and coming up with insightful conclusions using techniques from the fields of data mining and machine learning for predictive modeling.

I am a big advocate of working in teams and the concept of "open source." In my opinion, it is a positive ...more