Numerical Computing with Python. Harness the power of Python to analyze and find hidden patterns in the data

Pratap Dangeti, Allen Yu, Claire Chung, Aldrin Yim, ...

Data mining, or parsing the data to extract useful insights, is a niche skill that can transform your career as a data scientist Python is a flexible programming language that is equipped with a strong suite of libraries and toolkits, and gives you the perfect platform to sift through your data and mine the insights you seek. This Learning Path is designed to familiarize you with the Python libraries and the underlying statistics that you need to get comfortable with data mining.You will learn how to use Pandas, Python's popular library to analyze different kinds of data, and leverage the power of Matplotlib to generate appealing and impressive visualizations for the insights you have derived. You will also explore different machine learning techniques and statistics that enable you to build powerful predictive models.By the end of this Learning Path, you will have the perfect foundation to take your data mining skills to the next level and set yourself on the path to become a sought-after data science professional.This Learning Path includes content from the following Packt products:• Statistics for Machine Learning by Pratap Dangeti• Matplotlib 2.x By Example by Allen Yu, Claire Chung, Aldrin Yim• Pandas Cookbook by Theodore Petrou

Statistics for Machine Learning. Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R

Pratap Dangeti

Complex statistics in machine learning worry a lot of developers. Knowing statistics helps you build strong machine learning models that are optimized for a given problem statement.This book will teach you all it takes to perform the complex statistical computations that are required for machine learning. You will gain information on the statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. You will see real-world examples that discuss the statistical side of machine learning and familiarize yourself with it. You will come across programs for performing tasks such as modeling, parameter fitting, regression, classification, density collection, working with vectors, matrices, and more.By the end of the book, you will have mastered the statistics required for machine learning and will be able to apply your new skills to any sort of industry problem.