English | March 18, 2018 | ASIN: B07BK3GJRH | 134 pages | AZW3 | 0.30 MB

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…