Introduction to Machine Learning with Python

Posted on by

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 Muller 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 evenfrom this book.With this book, you ll learn Fundamental concepts and applications of machine learningAdvantages and shortcomings of widely used machine learning algorithmsHow to represent data processed by machine learning, including which data aspects to focus onAdvanced methods for model evaluation and parameter tuningThe concept of pipelines for chaining models and encapsulating your workflowMethods for working with text data, including text specific processing techniquesSuggestions for improving your machine learning and data science skills Download Introduction to Machine Learning with Python By Andreas C. Müller – kino-fada.fr I have not read much of this book, to be honest my teacher covered all literature during his classes so I didn t feel a need to read what I had heard and seen already But this book is really nicely written it ...finally, I could read this great book I have found that some topic that I didn t know about them like chaining and pipeline the working with text data hasprofit for meWhat a useful book it focuses mostly on scikit learn with some numpy, pandas and matplotlib thrown in, you could say it s an in depth tour of some of theuseful methods in scikit learn classifying, regression, a bit of clustering, PCA, all the different ways to measure the outcome of your model, how to use the incredibly useful scikit learn Pipeline to test parameters and models, etc.The examples are useful and interesting, especially the face picture clustering and classification is am What a useful book it focuses mostly on scikit learn with some numpy, pandas and matplotlib thrown in, you could say it s an in depth tour of some of theuseful methods in scikit learn classifying, regression, a bit of clustering, PCA, all the different ways to measure the outcome of your model, how to use the incredibly useful scikit learn Pipeline to test parameters and models, etc.The examples are useful and interesting, especially the face picture clustering and classification is amazing There s not much mathematics involved, if I remember correctly there s maybe two formulas in the w...I do not like this book as much as as An Introduction to Statistical Learning With Applications in R, but if you are constrained or committed to using Python instead of R, it is the best available alternative as of 2018, and I do think it...This is a great book.It is a nice introduction to Machine Learning scikit learn specifically without much maths needed It will by no means make you an expert, but it will give you a good sense of the basics, a walkthrough of scikit learn and hopefully some i...It is a nice book to start Machine Learning Book explains about all the algorithms.This book should be the first book for anyone who has a bit of programming background and want to overview how machine learning would look like without deep diving into the linear algebra and or any relevant math.The book uses Python, scikit learn, bumpy, etc that are well defined and have been widely used, and take examples one by one, but not with serious math or from the scratch but using existing scikit learn Probably some people would like to learn all the stuff from the scratch, including This book should be the first book for anyone who has a bit of programming background and want to overview how machine learning would look like without deep diving into the linear algebra and or any relevant math.The book uses Python, scikit learn, bumpy, etc that are well defined and have been widely used, and take examples one by one, but not with serious math or from the scratch but using existing scikit learn Probably some people would like to learn all the stuff from the scratch, including ...Great way to get started with Python and ML Gives overview of tools libraries you ll likely need Broad overview of algorithms, with a good explanation on how they work and insight into how the main parameters influence behavior with examples in the book and code to demonstrate how to use Code is also available on Github Many guidances as when to use what depending on which kind of data problem you ve got which techniques work better it gives an idea on which techniques are typically Great way to get started with Python and ML Gives overview of tools libraries you ll likely need Broad overview of algorithms, with a good explanation on how they work and insight into how the main parameters influence behavior with examples in the book and code to demonstrate how to use Code is also available on Github Many guidances as when to use what depending on which kind of data problem you ve got which techniques work better it gives an idea on which techniques are typically used, and which only...

Introduction to Machine Learning with Python
  • English
  • 05 June 2017
  • Paperback
  • 400 pages
  • 1449369413
  • Andreas C. Müller
  • Introduction to Machine Learning with Python