Regression Analysis with Python, Massaron Luca, Boschetti Alberto

• Updated for JIRA 7, this book covers all the new features introduced in JIRA 7 with a dedicated chapter on JIRA Service Desk--one of the biggest new add-ons to JIRA
• This book lays a strong foundation to work with agile projects in JIRA from both the administrator and end user's perspective
• Learn to solve challenging data science problems by building powerful machine learning models using Python

Book Description

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:

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

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

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.

While machine learning expertise doesn't quite mean you can create your own Turing Test-proof android--as in the movie Ex Machina--it is a form of artificial intelligence and one of the most exciting technological means of identifying opportunities and solving problems fast and on a large scale. Anyone who masters the principles of machine learning is mastering a big part of our tech future and opening up incredible new directions in careers that include fraud detection, optimizing search results, serving real-time ads, credit- scoring, building accurate and sophisticated pricing models--and way, way more.

Unlike most machine learning books, the fully updated 2^nd Edition of Machine Learning For Dummies doesn't assume you have years of experience using programming languages such as Python (R source is also included in a downloadable form with comments and explanations), but lets you in on the ground floor, covering the entry-level materials that will get you up and running building models you need to perform practical tasks. It takes a look at the underlying--and fascinating--math principles that power machine learning but also shows that you don't need to be a math whiz to build fun new tools and apply them to your work and study.

Understand the history of AI and machine learning Work with Python 3.8 and TensorFlow 2.x (and R as a download) Build and test your own models Use the latest datasets, rather than the worn out data found in other books Apply machine learning to real problems

Whether you want to learn for college or to enhance your business or career performance, this friendly beginner's guide is your best introduction to machine learning, allowing you to become quickly confident using this amazing and fast-developing technology that's impacting lives for the better all over the world.

Deep learning provides the means for discerning patterns in the data that drive online business and social media outlets. Deep Learning for Dummies gives you the information you need to take the mystery out of the topic--and all of the underlying technologies associated with it.

In no time, you'll make sense of those increasingly confusing algorithms, and find a simple and safe environment to experiment with deep learning. The book develops a sense of precisely what deep learning can do at a high level and then provides examples of the major deep learning application types.

• Includes sample code
• Provides real-world examples within the approachable text
• Offers hands-on activities to make learning easier
• Shows you how to use Deep Learning more effectively with the right tools

This book is perfect for those who want to better understand the basis of the underlying technologies that we use each and every day.