Link to Content:

Hands-On Machine Learning with Scikit-Learn and TensorFlow

Created/Published/Taught by:

Aurélien Géron

O'Reilly

Content Found Via:

Ayman Abuelela

Free? No

Cost Range:

$25.00 - $31.57

Tags: deep learning / machine learning / python / scikit-learn / TensorFlow

Difficulty Rating:

From Amazon:

“Perhaps you would like to give your homemade robot a brain of its own? Make it recognize faces? Or learn to walk around? Or maybe your company has tons of data (user logs, financial data, production data, machine sensor data, hotline stats, HR reports, etc.), and more than likely you could unearth some hidden gems if you just knew where to look….

This book assumes that you know close to nothing about Machine Learning. Its goal is to give you the concepts, the intuitions, and the tools you need to actually implement programs capable of learning from data.

We will cover a large number of techniques, from the simplest and most commonly used (such as linear regression) to some of the Deep Learning techniques that regularly win competitions. Rather than implementing our own toy versions of each algorithm, we will be using actual production-ready Python frameworks:

- Scikit-Learn is very easy to use, yet it implements many Machine Learning algorithms efficiently, so it makes for a great entry point to learn Machine Learning.
- TensorFlow is a more complex library for distributed numerical computation using data flow graphs. It makes it possible to train and run very large neural networks efficiently by distributing the computations across potentially thousands of multi-GPU servers. TensorFlow was created at Google and supports many of their large-scale Machine Learning applications. It was open-sourced in November 2015.”

Recommended Prerequisites: "This book assumes that you have some Python programming experience and that you are familiar with Python’s main scientific libraries, in particular NumPy, Pandas, and Matplotlib. Also, if you care about what’s under the hood you should have a reasonable understanding of college-level math as well (calculus, linear algebra, probabilities, and statistics)."

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