9781838820640_

Leverage the power of Python and statistical modeling techniques for building accurate predictive models

Key Features

Get introduced to Pythons 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

Book Description

Pythons 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.

Youll 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 Pythons scikit-learn library
Implement regression analysis and clustering
Learn how to train a neural network 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.

(*_*)9781838820640_

Leverage the power of Python and statistical modeling techniques for building accurate predictive models

Key Features

Get introduced to Pythons 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

Book Description

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 Pythons scikit-learn library
Implement regression analysis and clustering
Learn how to train a neural network in Python

Who this book is for

...(*_*)9781838820640_

Leverage the power of Python and statistical modeling techniques for building accurate predictive models

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

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.

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

Who this book is for

n(*_*)9781838820640_

Leverage the power of Python and statistical modeling techniques for building accurate predictive models

Key Features

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

Book Description

Python's ease-of-use and multi-purpose nature has made it one of the most popular tools for data scientists and machine learning developers. Its rich libraries are widely used for data analysis, and more importantly, for building state-of-the-art predictive models. This book is designed to guide you through using these libraries to implement effective statistical models for predictive analytics. You’ll start by delving into classical statistical analysis, where you will learn to compute descriptive statistics using pandas. You will focus on supervised learning, which will help you explore the principles of machine learning and train different machine learning models from scratch. Next, you will work with binary prediction models, such as data classification using k-nearest neighbors, decision trees, and random forests. The book will also cover algorithms for regression analysis, such as ridge and lasso regression, and their implementation in Python. In later chapters, you will learn how neural networks can be trained and deployed for more accurate predictions, and understand which Python libraries can be used to implement them. By the end of this book, you will have 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 different Python packages for statistical analysis
Implement algorithms such as Naive Bayes and random forests
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

Training Systems Using Python Statistical Modeling

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