Introduction to Machine Learning with Python: A Guide for Data Scientists
Product ID: 51719904
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Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book. With this book, you’ll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data aspects to focus on Advanced methods for model evaluation and parameter tuning The concept of pipelines for chaining models and encapsulating your workflow Methods for working with text data, including text-specific processing techniques Suggestions for improving your machine learning and data science skills
| Dimensions | 17.53 x 1.78 x 23.11 cm |
| Edition | 1st |
| Isbn 10 | 1449369413 |
| Isbn 13 | 978-1449369415 |
| Item Weight | 680 g |
| Language | English |
| Part Of Series | Learning Python |
| Print Length | 400 pages |
| Publication Date | 7 Oct. 2016 |
| Publisher | O′Reilly |
User
A great first book on machine learning!
A great book for getting started. Covers loads of models you can build with scikitlearn and walks you through good practice in machine learning. If you know python but have little to no experience with ML, this is a great place to start! Decision trees, random forests, gradient boosting, support vector machines, etc!
User
Good guide to starting ML
Decent guide to starting machine learning. The difficulty level is geared towards those with a little knowledge of computer science & programming, but might be too gentle for more experienced coders. I'm only part-way in, so will try to remember to update once I've completed the book.
User
IMO, a thorough introduction to ML. I liked the code examples and also the real world chapter.
Gave a thorough introduction to ML in a relatively digestible form. It filled in holes in my knowledge. I was a little surprised that what I thought were "just" statistical methods were classified as ML too(e.g. PCA, random forest and k means clustering). All good though, so many of us know more than we think. :-) I managed to read over a week of hour each way train commuting and some evenings.Like many such books, the code is invaluable and all the examples I tried and played with worked, using python3 on linux on a Chromebook.Most was at the right level for me, some I will need to revisit after a month. I emerged with a far more informed mind and increased confidence.I particularly liked the chapter on how to apply one's new found knowledge in the overall context of projects and the notes of caution for the real world.As some correctly reviewed, yes one can find similar information on line for free, to me I find it easier to have a thorough set of information in one place, that I could travel with, could write in and get away from a screen.(I am an experienced bioinfomatician with MSc Advanced Computing that already knew the broader concepts, and had the used the results of ML before, but not the detail or the how in python).
User
Five stars
I love this book. Practical, simple and explains most machine learning concepts very clealy.
User
Very informative
takes you through the fundamentals in a very clear way
User
Great for begginers
This book is great if you want to learn ML and don't know where to start
User
Better then browsing endlessly
Good structured book ideal for learning ML.
User
Lear how to apply ML DL techniques to datasets.
Great summary of open source available ML DL techniques.Various algos employed, detailed, explained.Perfect to start building skills on these topics. Great accessory if you are teaching yourself online.
User
Good introduction into machine learning
"Introduction to Machine Learning" comes with an exelent overview over standard techniques of machine learning. It focuses on the techniques and implementation in python using mostly the standard samples. The math side is sufficent for people who want to apply the algorithms and not explore the mechanics of the algorithms itself in depth. The lanuage is easy to follow and to the point. All chapter are nicely structured, ending with an excellent summary.I can recommend the book not only to people who want to implement the algorithms in Python but to everybody who want an introduction to machine learning.
User
Ótimo livro
Ótimo livro, está me ajudando bastante a responder questões de concursos.
User
Good
Book is good price is cheap
User
Not the original book
The book is in greyscale and I can't follow the author's guide into different colors of an image. Really disappointed!
User
Good examples and fair learning curve.
Fantastic intro into sklearn,
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