Training Systems Using Python Statistical Modeling

Explore popular techniques for modeling your data in Python

Curtis Miller 지음

Packt(GCO Science) 2019년 05월 20일 출간

작품소개

책소개

▶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.

목차

▶TABLE of CONTENTS
1. Classical Statistical Analysis
2. Introduction to Supervised Learning
3. Binary Prediction Models
4. Regression Analysis and How to Use It
5. Neural Networks
6. Clustering Techniques
7. Dimensionality Reduction

책 속으로

▶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.

출판사 서평

▶ Preface
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.

인물정보

저자(글) Curtis Miller

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.