Curtis Miller - Training Systems Using Python Statistical Modeling: Explore popular techniques for modeling your data in Python

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Curtis Miller is a doctoral candidate at the University of Utah studying mathematical statistics. He writes software for both research and personal interest, including the R package (CPAT) available on the Comprehensive R Archive Network (CRAN). Among Curtis Miller's publications are academic papers along with books and video courses all published by Packt Publishing. Curtis Miller's video courses include Unpacking NumPy and Pandas, Data Acquisition and Manipulation with Python, Training Your Systems with Python Statistical Modelling, and Applications of Statistical Learning with Python. His books include Hands-On Data Analysis with NumPy and Pandas.

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Python, a multi-paradigm programming language, has become the language of choice for data scientists for data analysis, visualization, and machine learning. Training Systems Using Python Statistical Modeling takes you through various different concepts that get you acquainted and working with the different aspects of machine learning.

You will get acquainted and work with the different aspects of machine learning and statistical analysis. You will start with classical statistical analysis. You will explore the principles of machine learning and train different machine learning models, and work with binary prediction models, decision trees, and random forests. You will implement different types of regression, work on neural networks, and train them. You will also learn how to evaluate cluster model results. By the end of this book, you will be confident in using various machine learning models, training them, evaluating model results, and implementing various dimensionality reduction techniques.

If you are a statistician or a data analyst and want to learn how to build statistical modeling systems using Python tools and packages, then this is the perfect book for you. You will dive into various different aspects of statistical modeling and training, and will learn how to employ those concepts using the Python programming language.

, Classical Statistical Analysis , helps you apply your knowledge of Python and machine learning to create data models and perform statistical analysis. You will learn about various statistical learning techniques and learn how to apply them in data analysis.

, Introduction to Supervised Learning , discusses what's involved in machine learning and what it is all about. We start by discussing the principles involved in machine learning, with a particular focus on binary classification. Then, we will look at various techniques used when training models. Finally, we will look at some common metrics that people use to judge how well an algorithm is performing.

, Binary Prediction Models , looks at various methods for classifying data, focusing on binary data. We will see how we can extend algorithms for binary classification to algorithms that are capable of multiclass classification.

, Regression Analysis and How to Use It , covers a different variant of supervised learning. It focuses on different modes of linear regression and how to apply them for various purposes.

, Neural Networks , talks about classification and regression using neural networks. We will learn about perceptrons. We will also discuss the idea behind neural networks, including the different types of perceptrons, and what a multilayer perceptron is. You will also learn how to train a neural network for various purposes.