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Key benefits

Updated to include new algorithms and techniques

Code updated to Python 3.8 & TensorFlow 2.x

New coverage of regression analysis, time series analysis, deep learning models, and cutting-edge applications

Description

Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.

What you will learn

Understand the characteristics of a machine learning algorithm

Implement algorithms from supervised, semi-supervised, unsupervised, and RL domains

Learn how regression works in time-series analysis and risk prediction

Create, model, and train complex probabilistic models

Cluster high-dimensional data and evaluate model accuracy

Discover how artificial neural networks work – train, optimize, and validate them

Work with autoencoders, Hebbian networks, and GANs

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Amazon verified reviews

Lars Oct 21, 2022

I like this book, because there are mathematical backgrounds involved which you don't find too often. From a style perspective I would rate this book a 1/5. But still, it shares knowledge and interesting thoughts and algorithms and you can see the author made efforts to share his knowledge. In this second edition, the form just is not too reader-friendly in my opinion. You can get the ideas, but not always the details. And here is why.The mathematical notation is not precise enough. There shouldn't be any room in mathematics and in mathematics we have the means to make anything non-interpretable. As pointed out above, there are the most basic mistakes in calculations and there are mistakes in advanced formulas which leads to spending hours on researching those, and ending in: They are only in this book. The hell does the author really want to say?On the coding perspective, the code is anything else but clean. Why are weights sometimes denoted as w and sometimes as q? What is trh? What is nb? What are X_a or X_n? There are many code duplications. All that adds to the unreadability. At some points the author says "This function computes the following expression: ... math formula ...", but it simply does not. Is the code wrong? Is the formula wrong? What Python version is the author using? Which package versions?Sometimes the descriptions are in detail and perfectly comprehensible. At other times the author leaves out the very details needed for full understanding. I personally do not like that style. If you start explaining something on a more basic level, you have to stick with that, because that means whatever you are writing also addresses people with a more basic understanding. If you then switch your explanations to a more adept level, people with a more basic understanding have no chance although your letters address them.

Amazon Verified review

Chidi Aug 28, 2021

Bought this book 2 days ago based on the reviews posted. Not sure I'm reading the same book because a few pages into the book, page 9, there are "ridiculous" errors. Given the set A = {1,2,3,5,7,9}, how can mean (A) = 4.67? Or median(A) = 4.5? The errors just continue as example is developed.We can argue that the book is for advanced readers but with errors this basic, I worry that I would spend valuable time proofreading.Kudos to the author for putting together a book this big. Aside the errors, the font/print quality is disappointing e.g. Table of Contents page is visually unappealing!Maybe my views will change if I complete reading the book but I have lost interest already. I most likely will return and try a different book/publisher.

TD59 Jun 13, 2021

I have used this book (1st and 2nd edition) in my Machine Learning class for a couple of years. Together with the Raschka and Mirjalili book (Python Machine Learning), Bonnacorso's provides a solid foundation for understanding the key algorithms, how they work, and how to fine-tune them. Both amanuals re required in my class and my students have only had positive feedback about both books. They are complementary as Bonnacorso does not cover some topics such as Natural Language Processing compared with Raschka, for instance.

andi Apr 07, 2021

almost to the point were I was overwhelmed. AMAZING RESOURCE

Anwar Nur Dec 05, 2020

Worst Alogorithm book i have ever seen.

Mastering Machine Learning Algorithms: Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work , Second Edition

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