Thoughtful Machine Learning with Python: A Test-Driven Approach Paperback - 2017by Matthew Kirk
Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext.
Featuring graphs and highlighted code examples throughout, the book features tests with Python's Numpy, Pandas, Scikit-Learn and SciPy data science libraries. If you're a software engineer or business analyst interested in data science, this book will help you:
- Reference real-world examples to test each algorithm through engaging, hands-on exercises
- Apply test-driven development (TDD) to write and run tests before you start coding
- Explore techniques for improving your machine-learning models with data extraction and feature development
- Watch out for the risks of machine learning, such as underfitting or overfitting data
- Work with K-Nearest Neighbors, neural networks, clustering, and other algorithms
Python Machine Learning: Machine Learning and Deep Learning with Python, sci-kit-learn, and TensorFlow, 2nd Edition, Kindle Edition
by Jeffrey Jackovich, Ruze Richards
Use artificial intelligence and machine learning on AWS to create engaging applications
- Explore popular AI and ML services with their underlying algorithms
- Use the AWS environment to manage your AI workflow
- Reinforce key concepts with hands-on exercises using real-world datasets
Machine Learning with AWS is the right place to start if you are a beginner interested in learning useful artificial intelligence (AI) and machine learning skills using Amazon Web Services (AWS), the most popular and powerful cloud platform. You will learn how to use AWS to transform your projects into apps that work at high speed and are highly scalable.
By the end of this book, you will have developed the skills you need to efficiently use AWS in your machine learning and artificial intelligence projects.
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python Paperback - 28 Jul 2017
by Manohar Swamynathan
Master machine learning with Python in six steps and explore fundamental to advanced topics, all designed to make you a worthy practitioner.
This book's approach is based on the ‚??Six degrees of separation‚?? theory, which states that everyone and everything is a maximum of six steps away. Mastering Machine Learning with Python in Six Steps presents each topic in two parts: theoretical concepts and practical implementation using suitable Python packages.
What You'll Learn
- Examine the fundamentals of Python programming language
- Review machine Learning history and evolution
- Understand machine learning system development frameworks
- Implement supervised/unsupervised/reinforcement learning techniques with examples
- Explore fundamental to advanced text mining techniques
- Implement various deep learning frameworks
Machine Learning: The New AI (The MIT Press Essential Knowledge Series) Paperback - October 7, 2016
by Ethem Alpaydin
A concise overview of machine learning-computer programs that learn from data-which underlies applications that include recommendation systems, face recognition, and driverless cars.
Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition-as well as some we don't yet use every day, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data; the idea is to learn algorithms for the tasks automatically from data. As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as "Big Data" has gotten bigger, the theory of machine learning-the foundation of efforts to process that data into knowledge-has also advanced. In this book, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications.