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Python Machine Learning
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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
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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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.
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.
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 ...more
I ...more
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.
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.
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 ...more
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 ...more
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.
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 ...more
This excellent book is a practical introduction to ML using the Python programming language, along with relevant components of Python’s rich ...more
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.
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.
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.
Excellent introduction to machine learning. I tried one other book which I couldn't follow, so I could really appreciate how well this was written. I read an earlier edition which had a few errors/bugs but I learnt a lot fixing them. Got completely stuck on chapter 12 though with the mnist dataset, which I can't find any way to download. Really good for the concepts.
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.
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.
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.
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.
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.
My baseline in Machine Learning research
Excelente livro.
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.
...aaaaa, Polish translation is awful!!!!! I hope that original English version doesn’t have this huge disadvantage.
Challenging, yet so rewarding for the Microsoft Imagine Cup Competition
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.
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 ...more
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 ...more
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.
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.
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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Accession No: DL028603
Accession No: DL028603
Great introduction on machine learning in Python. Good to have access to code: https://github.com/rasbt/python-machi...
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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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
I am a big advocate of working in teams and the concept of "open source." In my opinion, it is a positive ...more
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