Python Machine Learning for Beginner: How to Learn Digital Programming in 12 Hours

By: Gary Gold
Narrated by: Andrew McDermott
Length: 2 hrs
Categories: Business, Career Skills
5 out of 5 stars (51 ratings)

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Publisher's Summary

Python Machine Learning for Beginner is a professional guidance, made easy, for everyone who wants to move rapidly in his first steps within the Python world and for those are quite expert but willing to improve.
 

Machine learning has become an integral part of many commercial applications and research projects, but this field does not belong to a mega-corporation.
 

Therefore, whether you are a beginner, this audiobook will teach you the best technique to improve and mastering the Python solutions.
 

Unlike other books that confine themselves to a mere description of this program, Python Machine Learning will show you how to improve your results in just 12 hours!
 

And this for you means, save time having practical examples and easy descriptions from one of the major experts on the market.

©2019 Sape Ltd (P)2019 Sape Ltd

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First of all, I've read many of the other well re


First of all, I've read many of the other well reviewed, up-to-date, Python books (yes, all of them were shorter), and being new to Python, I ended up spending most of my time searching online trying to fill in the gaps that the other authors failed to fill in. With this book you don't need to reference anything else because the author does a great job of answering every question. You can tell he's dedicated his life to teaching Python and knows what problems his readers will run into.

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A very well-written book that takes you beyond the

A very well-written book that takes you beyond the "heavy curiosity" phase of your machine learning education. You need this book if you want to *understand*, from a critical perspective, how to accomplish things like - selecting features, culling data, creating provably-suitable ML models, model/data validation, and what it takes to actually get an ML platform to do something truly useful and meaningful to you!

In return for being so useful, the author requests something from you - get your hands on the keyboard, and actually work with Python Scikit-tools as well as the Jupyter workbooks that accompany the book (on github). You should have knowledge of Python, as many of the ML concepts are reinforced through concrete implementations in Python code. And work you must, as - after all - the book's title includes the words "Hands-On"!

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The author does a good job in trying to present in

The author does a good job in trying to present information in such a way that it gets you thinking about the problems you are trying to solve. The second chapter is a full blown project to take you through the full development cycle.

There is quite a bit of code and beginnings of mathematical models presented in the book. So, there is plenty to step off from.

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This is one of the most accessible machine learnin

This is one of the most accessible machine learning books out there for developers. It strikes a nice balance between intuition, mathematical details and implementation specifics. I would highly recommend this book to beginners and intermediate ML practitioners. I'm only giving it four stars because despite the content itself being great, the print does have some issues like missing diagrams (see attached pictures).

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I am studying machine learning on my own after sit

I am studying machine learning on my own after sitting through a graduate computer science course on the subject. I am only up to Chapter 3, and have liked the book so far. I was looking for a "hands-on" approach with a lot of examples. Unfortunately, several of the examples in Chapter 3 don't work as shown.

In the second code block on page 85, y_train_perfect_predictions is not defined. The line could be written as:
>>> confusion_matrix(y_train_5, y_train_5)

On page 86, first code block, second line, y_pred should be y_train_pred.

Likewise, in the code block at the top of page 87, y_pred should be y_train_pred.

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Hands-On PythonMachine Learning strikes a perfect

Hands-On Python Machine Learning strikes a perfect blend between application and theory. Beginners to machine learning will find it clear to follow and will be able to build complete systems within a few chapters while those with an intermediate level of experience will find a comprehensive, up-to-date guide to this exciting field.

Pros:
+ Practical: The book focuses on examples and implementations of the algorithms rather than the mathematics allowing readers to quickly build their own machine learning models
+ Readable: Geron does not get too caught up in the details, and he provides warnings when the next section is heavy on theory
+ Online Jupyter Notebooks: The Jupyter Notebooks that accompany this book (and can even be viewed for free with no purchase from the author's GitHub) are worth the entire purchase price. They feature examples of all the code in the book, plus additional explanatory material. The end-of-chapter solutions to the coding exercises are gradually being added to the notebooks.
+ Up-to-date: The leading edge of machine learning (and in particular deep learning) is constantly shifting, and Geron does his best to keep the notebooks updated. Multiple times I have read an ML paper and then found the technique implemented in the notebooks within weeks of the publication of the article. Some of the techniques in the book may not be at the absolute forefront of the field, but they are still good enough for learning the fundamentals.
+ Engaging: The book is a joy to read, and the author is quick to respond to issues pointed out by readers in the book or in the Jupyter Notebooks. Clearly, the author enjoys machine learning and teaching it to others.

Cons:
- Experts may find this book lacks enough depth because it is more focused on getting up and running rather than optimization. It also is specifically aimed towards Python (and Tensorflow for deep learning) so those looking for implementations in other frameworks will have to search elsewhere.
- Due to the rapidly-evolving nature of the field, a print book on machine learning will always need to be periodically re-issued to stay on top of all the developments. Nonetheless, the fundamentals covered in this book will remain relevant and the Jupyter Notebooks are constantly updated with new techniques.

Final Line: If you have some basic experience with Python (loops, conditionals, dictionaries, and especially Numpy) and zero to a medium level of experience with machine learning, this book is an optimal choice. I would recommend it both for those wishing to self-study and quickly develop working models, and for students in machine learning who want to learn the implementations of more theoretical coursework. I have enjoyed spending time working through the chapters and the exercises and have found this book extremely useful.

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I have been a collector of books.

I have been a collector of books and classes of machine learning and deep learning for the last few years. Even though I come from a strong theoretical background, I have to say one must do hands on tinkering to be able to solve one's own problem successfully. Then for deep learning one must work with Tensorflow or Theano. However, I have been searching for a good hands-on book on tensorflow and had found none until this book.

I purchased the kindle version so I can dive into this book early before the book comes out. I am not disappointed. It gives you the code on the familiar Python notebook to work on. The author really knows about Tensorflow and machine learning, and his teaching shows. There are pieces of information hard to find somewhere else, and I have spent hundreds to thousands to attend workshops.

Needless to say, I have not done all the exercises yet. But I like this book enough that I will work on all the problems I am interested in

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Writing style is good.

This has to be at the top of my list of most highly recommended books! The amount of material it covers is awesome, and I can find almost no fault with it. The writing is extremely clear, easy to read, written in impeccable English. Very well edited. I don't think I came across any spelling or grammar errors, or any real errors at all. Truly solid writing.

The breadth of information covered if quite wide. The choice to start with Scikit-Learn was interesting, but makes sense on some level while he's introducing the more basic machine learning concepts. Simple machine learning techniques like logistic regression, data conditioning, dealing with training, validation, test set. Even if you've read about these concepts a million times, you might still glean useful information from these pages.

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This book provides a great introduction to deep an

This book provides a great introduction to deep and reinforcement learning. First, It does a good job at explaining in detail the basics of neural networks. Then, it gradually introduces more complex models like convolutional and recurrent networks in an easy to understand way.

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Python Deep Learning by Andrew McDermott

Python Deep Learning by Andrew McDermott starts with a short introduction to deep learning, followed by examples that build your understanding of neural networks by starting with logistic regression implemented with the same structure as a neural net, shallow nets, and deep nets. Key topics include computational graphs and derivatives on graphs, gradient descent, vectorizing code, neural network representations, activation functions, back propagation, and deep nets. Moreover, the book touches high level concepts and considerations to frame learning.