Machine Learning Fundamentals: Use Python and scikit-learn to get up and running with the hottest developments in machine learning

Hyatt Saleh

€29.99

5 (3 Ratings)

Paperback Nov 2018 240 pages 1st Edition

Hyatt Saleh

€29.99

5 (3 Ratings)

Paperback Nov 2018 240 pages 1st Edition

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Key benefits

Explore scikit-learn uniform API and its application into any type of model
Understand the difference between supervised and unsupervised models
Learn the usage of machine learning through real-world examples

Description

As machine learning algorithms become popular, new tools that optimize these algorithms are also developed. Machine Learning Fundamentals explains you how to use the syntax of scikit-learn. You'll study the difference between supervised and unsupervised models, as well as the importance of choosing the appropriate algorithm for each dataset. You'll apply unsupervised clustering algorithms over real-world datasets, to discover patterns and profiles, and explore the process to solve an unsupervised machine learning problem. The focus of the book then shifts to supervised learning algorithms. You'll learn to implement different supervised algorithms and develop neural network structures using the scikit-learn package. You'll also learn how to perform coherent result analysis to improve the performance of the algorithm by tuning hyperparameters. By the end of this book, you will have gain all the skills required to start programming machine learning algorithms.

Who is this book for?

Machine Learning Fundamentals is designed for developers who are new to the field of machine learning and want to learn how to use the scikit-learn library to develop machine learning algorithms. You must have some knowledge and experience in Python programming, but you do not need any prior knowledge of scikit-learn or machine learning algorithms.

What you will learn

Understand the importance of data representation
Gain insights into the differences between supervised and unsupervised models
Explore data using the Matplotlib library
Study popular algorithms, such as k-means, Mean-Shift, and DBSCAN
Measure model performance through different metrics
Implement a confusion matrix using scikit-learn
Study popular algorithms, such as Naïve-Bayes, Decision Tree, and SVM
Perform error analysis to improve the performance of the model
Learn to build a comprehensive machine learning program

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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

Frequently bought together

€29.99

Artificial Intelligence and Machine Learning Fundamentals

€24.99

€41.99

Total € 96.97

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Shipping Details

USA:

Economy: Delivery to most addresses in the US within 10-15 business days

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UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

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Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

India:

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Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

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Unfortunately, due to several restrictions, we are unable to ship to the following countries:

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Cihan Aug 30, 2019

Why did I spent a lot of money to buy ALL other machine learning books but not this one? After 50 pages, you were lost. However, with this book, finally someone explained what is X, Y, and basic terminology beneath all this process. I mean, author describes every line starting from "import" statements and helps you remember it with immediate exercises. Even though I purchased 4 other books, this is the only text which helps me understand cleaning messy data, dealing with NaN values, normalization and so on. It works with real world data. MUST BUY! Bravo!

Amazon Verified review

Nicolás Aristizábal Jul 24, 2019

Hyatt treats this complex topic in very simple words. Highly recommended to read!

JC Dec 26, 2018

This was my first approch to the topic and this book was the best starting point as it explains all concepts very clearly and with detailed examples and activities that helped me cement the knowledge. Even though the theory for each algorithm is very complete, it also teaches how to effectively apply the knowledge in real-life scenarios. I totally recommend it for those starting their journey in machine learning!

Machine Learning Fundamentals: Use Python and scikit-learn to get up and running with the hottest developments in machine learning

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