Learn to solve challenging data science problems by building powerful machine learning models using Python

About This Book

• Understand which algorithms to use in a given context with the help of this exciting recipe-based guide
• This practical tutorial tackles real-world computing problems through a rigorous and effective approach
• Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale

Who This Book Is For

This Learning Path is for Python programmers who are looking to use machine learning algorithms to create real-world applications. It is ideal for Python professionals who want to work with large and complex datasets and Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. Experience with Python, Jupyter Notebooks, and command-line execution together with a good level of mathematical knowledge to understand the concepts is expected. Machine learning basic knowledge is also expected.

What You Will Learn

• Use predictive modeling and apply it to real-world problems
• Understand how to perform market segmentation using unsupervised learning
• Apply your new-found skills to solve real problems, through clearly-explained code for every technique and test
• Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms
• Increase predictive accuracy with deep learning and scalable data-handling techniques
• Work with modern state-of-the-art large-scale machine learning techniques
• Learn to use Python code to implement a range of machine learning algorithms and techniques

In Detail

Machine learning is increasingly spreading in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. Machine learning is transforming the way we understand and interact with the world around us.

In the first module, Python Machine Learning Cookbook, you will learn how to perform various machine learning tasks using a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.

The second module, Advanced Machine Learning with Python, is designed to take you on a guided tour of the most relevant and powerful machine learning techniques and you'll acquire a broad set of powerful skills in the area of feature selection and feature engineering.

The third module in this learning path, Large Scale Machine Learning with Python, dives into scalable machine learning and the three forms of scalability. It covers the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.

This Learning Path will teach you Python machine learning for the real world. The machine learning techniques covered in this Learning Path are at the forefront of commercial practice.

This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:

• Python Machine Learning Cookbook by Prateek Joshi
• Advanced Machine Learning with Python by John Hearty
• Large Scale Machine Learning with Python by Bastiaan Sjardin, Alberto Boschetti, Luca Massaron

Style and approach

This course is a smooth learning path that will teach you how to get st

Learn to build powerful machine learning models quickly and deploy large-scale predictive applications

About This Book

• Design, engineer and deploy scalable machine learning solutions with the power of Python
• Take command of Hadoop and Spark with Python for effective machine learning on a map reduce framework
• Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale

Who This Book Is For

This book is for anyone who intends to work with large and complex data sets. Familiarity with basic Python and machine learning concepts is recommended. Working knowledge in statistics and computational mathematics would also be helpful.

What You Will Learn

• Apply the most scalable machine learning algorithms
• Work with modern state-of-the-art large-scale machine learning techniques
• Increase predictive accuracy with deep learning and scalable data-handling techniques
• Improve your work by combining the MapReduce framework with Spark
• Build powerful ensembles at scale
• Use data streams to train linear and non-linear predictive models from extremely large datasets using a single machine

In Detail

Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy.

Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.

Style and Approach

This efficient and practical title is stuffed full of the techniques, tips and tools you need to ensure your large scale Python machine learning runs swiftly and seamlessly.

Large-scale machine learning tackles a different issue to what is currently on the market. Those working with Hadoop clusters and in data intensive environments can now learn effective ways of building powerful machine learning models from prototype to production.

This book is written in a style that programmers from other languages (R, Julia, Java, Matlab) can follow.