Python Machine Learning Blueprints Second Edition

Put your machine learning concepts to the test by developing real-world smart projects

Alexander Combs , Michael Roman 지음

Packt(GCO Science) 2019년 01월 31일 출간

작품소개

책소개

▶What You Will Learn
？ Understand the Python data science stack and commonly used algorithms
？ Build a model to forecast the performance of an Initial Public Offering (IPO) over an initial discrete trading window
？ Understand NLP concepts by creating a custom news feed
？ Create applications that will recommend GitHub repositories based on ones you've starred, watched, or forked
？ Gain the skills to build a chatbot from scratch using PySpark
？ Develop a market-prediction app using stock data
？ Delve into advanced concepts such as computer vision, neural networks, and deep learning

▶Key Features
？ Get to grips with Python's machine learning libraries including scikit-learn, TensorFlow, and Keras
？ Implement advanced concepts and popular machine learning algorithms in real-world projects
？ Build analytics, computer vision, and neural network projects

▶Who This Book Is For
This book is for machine learning practitioners, data scientists, and deep learning enthusiasts who want to take their machine learning skills to the next level by building real-world projects. The intermediate-level guide will help you to implement libraries from the Python ecosystem to build a variety of projects addressing various machine learning domains. Knowledge of Python programming and machine learning concepts will be helpful.

목차

▶TABLE of CONTENTS
1 The Python Machine Learning Ecosystem
2 Build an App to Find Underpriced Apartments
3 Build an App to Find Cheap Airfares
4 Forecast the IPO Market Using Logistic Regression
5 Create a Custom Newsfeed
6 Predict whether Your Content Will Go Viral
7 Use Machine Learning to Forecast the Stock Market
8 Classifying Images with Convolutional Neural Networks
9 Building a Chatbot
10 Build a Recommendation Engine
11 What's Next?

책 속으로

▶What this book covers
？ Chapter 1, The Python Machine Learning Ecosystem, discusses the features of key libraries and explains how to prepare your environment to best utilize them.

？ Chapter 2, Build an App to Find Underpriced Apartments, explains how to create a machine learning application that will make finding the right apartment a little bit easier.

？ Chapter 3, Build an App to Find Cheap Airfares, covers how to build an application that continually monitors fare pricing, checking for anomalous prices that will generate an alert we can quickly act on.

？ Chapter 4, Forecast the IPO Market Using Logistic Regression, takes a closer look at the IPO market. We'll see how we can use machine learning to help us decide which IPOs are worth a closer look and which ones we may want to take a pass on.

？ Chapter 5, Create a Custom Newsfeed, explains how to build a system that understands your taste in news, and will send you a personally tailored newsletter each day.

？ Chapter 6, Predict whether Your Content Will Go Viral, tries to unravel some of the mysteries. We'll examine some of the most commonly shared content and attempt to find the common elements that differentiate it from content people were less willing to share.

？ Chapter 7, Use Machine Learning to Forecast the Stock Market, discusses how to build and test a trading strategy. We'll spend more time, however, on how not to do it.

？ Chapter 8, Classifying Images with Convolutional Neural Networks, details the process of creating a computer vision application using deep learning.

？ Chapter 9, Building a Chatbot, explains how to construct a chatbot from scratch. Along the way, we'll learn more about the history of the field and its future prospects.

？ Chapter 10, Build a Recommendation Engine, explores the different varieties of recommendation systems. We'll see how they're implemented commercially and how they work. Finally, we'll implement our own to recommendation engine for finding GitHub repositories.

？ Chapter 11, What's Next?, summarizes what has been covered so far in this book and what the next steps are from this point on. You will learn how to apply the skills you have gained to other projects, real-life challenges in building and deploying machine learning models, and other common technologies that data scientists frequently use.

출판사 서평

▶ Preface
Machine learning is transforming the way we understand and interact with the world around us. This book is the perfect guide for you to put your knowledge and skills into practice and use the Python ecosystem to cover key domains in machine learning. This second edition covers a range of libraries from the Python ecosystem, including TensorFlow and Keras, to help you implement real-world machine learning projects.

The book begins by giving you an overview of machine learning with Python. With the help of complex datasets and optimized techniques, you'll go on to understand how to apply advanced concepts and popular machine learning algorithms to real-world projects. Next, you'll cover projects from domains such as predictive analytics to analyze the stock market and recommendation systems for GitHub repositories. In addition to this, you'll also work on projects from the NLP domain to create a custom news feed using frameworks such as scikit-learn, TensorFlow, and Keras. Following this, you'll learn how to build an advanced chatbot, and scale things up using PySpark. In the concluding chapters, you can look forward to exciting insights into deep learning and you'll even create an application using computer vision and neural networks.

By the end of this book, you'll be able to analyze data seamlessly and make a powerful impact through your projects.

작가정보

저자(글) Alexander Combs

Alexander Combs is an experienced data scientist, strategist, and developer with a background in financial data extraction, natural language processing and generation, and quantitative and statistical modeling. He currently lives and works in New York City.

저자(글) Michael Roman

Michael Roman is a data scientist at The Atlantic, where he designs, tests, analyzes, and productionizes machine learning models to address a range of business topics. Prior to this he was an associate instructor at a full-time data science immersive program in New York City. His interests include computer vision, propensity modeling, natural language processing, and entrepreneurship.