Python Machine Learning Blueprints: Put your machine learning concepts to the test by developing real-world smart projects, 2nd Edition

Discover a project-based approach to mastering machine learning concepts by applying them to everyday problems using libraries such as scikit-learn, TensorFlow, and Keras

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

Book Description

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.

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

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.

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?

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. Saurabh Chhajed is a technologist with vast professional experience in building Enterprise applications that span across product and service industries. He has experience building some of the largest recommender engines using big data analytics and machine learning, and also enjoys acting as an evangelist for big data and NoSQL technologies. With his rich technical experience, Saurabh has helped some of the largest financial and industrial companies in USA build their large product suites and distributed applications from scratch. He shares his personal experiences with technology at http://saurzcode.in. Saurabh has also reviewed books by Packt Publishing, Apache Camel Essentials and Java EE 7 Development with NetBeans 8, in the past. 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.