Grokking Machine Learning

Grokking Machine Learning MEAP V07 cover
Grokking Machine Learning
By 作者: Serrano G. Luis
Pub Date: 2020
ISBN: 9781617295911
Pages 页数: 350
Language 语言: English
Format: PDF/EPUB
Size: 60 Mb
The Book Description robot was collected from Amazon and arranged by Finelybook
MEAP Version 7
It’s time to dispel the myth that machine learning is difficult. Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using readily-available machine learning tools!
Machine learning is a collection of mathematically-based techniques and algorithms that enable computers to identify patterns and generate predictions from data. This revolutionary data analysis approach is behind everything from recommendation systems to self-driving cars, and is transforming industries from finance to art. Whatever your field, knowledge of machine learning is becoming an essential skill. Python, along with its libraries like NumPy, Pandas, and scikit-learn, has become the go-to language for machine learning.
In Grokking Machine Learning, expert machine learning engineer Luis Serrano introduces the most valuable ML techniques and teaches you how to make them work for you. You’ll only need high school math to dive into popular approaches and algorithms. Practical examples illustrate each new concept to ensure you’re grokking as you go. You’ll build models for spam detection, language analysis, and image recognition as you lock in each carefully-selected skill. Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert. When you’re done, you’ll have an intuitive understanding of the right approach for any machine learning task or project.
What’s inside

Different types of machine learning, including supervised and unsupervised learning
Algorithms for simplifying, classifying, and splitting data
Machine learning packages and tools
Hands-on exercises with fully-explained Python code samples


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