What to read after Machine Learning?

Machine Learning - 2 BOOK BUNDLE!!

Python Machine Learning

Machine learning is the science of getting machines and computers to act and learn on their own without being programmed explicitly. In just the past decade, this field has given us practical speech recognition, self-driving cars, greatly improved understanding of the overall human genome, effective web search and much more. Therefore, there is no wondering why machine learning is so pervasive today.

In this book, you will learn more about interpreting machine learning techniques using Python. You will also gain practice as you implement the most popular machine learning techniques on some real-world examples and you will learn both about the theoretical and practical machine learning implementation using Python's machine learning libraries.

At the end of the book, you will be able to cope with more complex machine learning issues solving your own problems using Python and its libraries specifically crafted for machine learning.

Here Is A Preview Of What You’ll Learn Here…

Basics behind machine learning techniques

Different machine learning algorithms

Fundamental machine learning applications and their importance

Getting started with machine learning in Python, installing and starting SciPy

Loading data and importing different libraries

Data summarization and data visualization

Evaluation of machine learning models and making predictions

Most commonly used machine learning algorithms, linear and logistic regression, decision trees support vector machines, k-nearest neighbors, random forests

Solving multi-clasisfication problems

Data visualization with Matplotlib and data transformation with Pandas and Scikit-learn

Solving multi-label classification problems

And much, much more...

Machine Learning with TensorFlow

TensorFlow is a powerful open source software library for performing various numerical data flow graphs. With its powerful resources, TensorFlow is perfect for machine learning enthusiasts offering plenty of workspace where you can improve your machine learning techniques and build your own machine learning algorithms.

Thanks to its capability, in recent times TensorFlow definitely has made its way into the software mainstream, so everyone who is interested in machine learnings definitely should considers getting hands on TensorFlow practices.

With this book as your guide, you will get your hands on TensorFlow machine learning techniques, learn how to perform different neural network operations, learn how to deal with massive datasets and finally build your first machine learning model for data classification.

Here Is a Preview of What You’ll Learn Here…

What is machine learning

Main uses and benefits of machine learning

How to get started with TensorFlow, installing and loading data

Data flow graphs and basic TensorFlow expressions

How to define your data flow graphs and how to use TensorBoard for data visualization

Main TensorFlow operations and building tensors

How to perform data transformation using different techniques

How to build high performance data pipelines using TensorFlow Dataset framework

How to create TensorFlow iterators

Creating MNIST classifiers with one-hot transformation

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