Training Systems Using Python Statistical Modeling

▶Book Description
Python's ease of use and multi-purpose nature has led it to become the choice of tool for many data scientists and machine learning developers today. Its rich libraries are widely used for data analysis, and more importantly, for building state-of-the-art predictive models. This book takes you through an exciting journey, of using these libraries to implement effective statistical models for predictive analytics.

You'll start by diving into classical statistical analysis, where you will learn to compute descriptive statistics using pandas. You will look at supervised learning, where you will explore the principles of machine learning and train different machine learning models from scratch. You will also work with binary prediction models, such as data classification using k-nearest neighbors, decision trees, and random forests. This book also covers algorithms for regression analysis, such as ridge and lasso regression, and their implementation in Python. You will also learn how neural networks can be trained and deployed for more accurate predictions, and which Python libraries can be used to implement them.

By the end of this book, you will have all the knowledge you need to design, build, and deploy enterprise-grade statistical models for machine learning using Python and its rich ecosystem of libraries for predictive analytics.

▶What You Will Learn
- Understand the importance of statistical modeling
- Learn about the various Python packages for statistical analysis
- Implement algorithms such as Naive Bayes, random forests, and more
- Build predictive models from scratch using Python's scikit-learn library
- Implement regression analysis and clustering
- Learn how to train a neural network in Python

▶Key Features
- Get introduced to Python's rich suite of libraries for statistical modeling
- Implement regression, clustering and train neural networks from scratch
- Includes real-world examples on training end-to-end machine learning systems in Python

▶Who This Book Is For
If you are a data scientist, a statistician or a machine learning developer looking to train and deploy effective machine learning models using popular statistical techniques, then this book is for you. Knowledge of Python programming is required to get the most out of this book.

▶What this book covers
- Chapter 1, 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.

- Chapter 2, 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.

- Chapter 3, 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.

- Chapter 4, 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.

- Chapter 5, 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.

- Chapter 6, Clustering Techniques, goes into detail about unsupervised learning. You'll learn about clustering and various approaches to clustering. You'll also learn how to implement those approaches for various purposes, such as image compression.

- Chapter 7, Dimensionality Reduction, focuses on dimensionality reduction techniques. You will learn about various techniques, such as PCA, SVD, and MDS.