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Key benefits

Discover how to apply the scikit-learn uniform API in all types of machine learning models

Understand the difference between supervised and unsupervised learning models

Reinforce your understanding of machine learning concepts by working on real-world examples

Description

Machine learning algorithms are an integral part of almost all modern applications. To make the learning process faster and more accurate, you need a tool flexible and powerful enough to help you build machine learning algorithms quickly and easily. With The Machine Learning Workshop, you'll master the scikit-learn library and become proficient in developing clever machine learning algorithms. The Machine Learning Workshop begins by demonstrating how unsupervised and supervised learning algorithms work by analyzing a real-world dataset of wholesale customers. Once you've got to grips with the basics, you'll develop an artificial neural network using scikit-learn and then improve its performance by fine-tuning hyperparameters. Towards the end of the workshop, you'll study the dataset of a bank's marketing activities and build machine learning models that can list clients who are likely to subscribe to a term deposit. You'll also learn how to compare these models and select the optimal one. By the end of The Machine Learning Workshop, you'll not only have learned the difference between supervised and unsupervised models and their applications in the real world, but you'll also have developed the skills required to get started with programming your very own machine learning algorithms.

What you will learn

Understand how to select an algorithm that best fits your dataset and desired outcome

Explore popular real-world algorithms such as K-means, Mean-Shift, and DBSCAN

Discover different approaches to solve machine learning classification problems

Develop neural network structures using the scikit-learn package

Use the NN algorithm to create models for predicting future outcomes

Perform error analysis to improve your model s performance

Write unit tests to validate your code with JUnit

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

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Amazon verified reviews

Allen G. Feb 26, 2021

This is an extremely well written title, and is easy to follow and learn effectively from the content. I strongly recommend it to anyone who's looking to pursue machine learning.

Amazon Verified review

Robert John Jan 08, 2021

There are two approaches to learning ML: a theoretical approach, and a practical one. Theoretical approaches are useful if you have a background in a numerical field like mathematics or statistics. Practical approaches are useful for getting your hands dirty with the application without first trying to understand why things work. This book takes the latter approach and does an excellent job of showing you how to get things done using the very popular scikit-learn library and the Python programming language. The workshop format gives you lots of practice. You can't do ML without data, and data is rarely clean. There is an entire chapter dedicated to data preprocessing. The book teaches you how to work with various algorithms, both supervised and unsupervised. It also covers evaluation metrics. If you would like to get started with putting Machine Learning, this is a good book that I would recommend!

Bert Gollnick Oct 24, 2020

I enjoyed reading the book. It helps you to start in the field of Machine Learning with Python. You learn the main concepts based on the main Python library for this purpose: scikit-learn. You learn algorithms from different fields like unsupervised learning (e.g. different clustering techniques) and supervised learning (e.g. SVM, Naive Bayes, Decision Trees, Artificial Neural Networks). The algorithms are well explained.Overall it is a good book for Python developers who want to dive into the field of Machine Learning.

Hakuna Matata Nov 15, 2020

This is a well-written book with good breadth and depth - that covers all the key concepts and modules of ML. I love all the new examples - especially real world examples. I also like a quick introduction to NN - giving a taste of what's beyond the basic ML concepts. The best part is the demo environment where I got to play around with all the code snippets by each of the concepts.

Amazon Customer Mar 18, 2021

This book is a good introduction to data analysis and the basics of machine learning. The author does a good job covering the basic algorithms (kmeans, naive bayes, svm, decision trees and even neural networks), but does not go into too much depth regarding the mathematical or statistical underpinnings. There are sample problems with straight forward code applying each of these algorithms. One thing I liked about this book was that the author sticks to well-known machine learning packages such as scikit-learn, pandas, numpy, and matplotlib, and avoids bringing in a whole lot of extraneous minor packages. This has the benefit of not requiring the user to spend a lot of time installing other dependencies, as well as building some familiarity with these packages -- which are generally powerful and popular for good reason.Overall, if you're new to the field and looking for a quick, basic introduction to some of the most well-known machine learning algorithms and techniques, this book would be a good choice.

The Machine Learning Workshop: Get ready to develop your own high-performance machine learning algorithms with scikit-learn , Second Edition

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