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
- 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
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
- Title Page
- Copyright and Credits
- About Packt
The Python Machine Learning Ecosystem
- Data science/machine learning workflow
- Python libraries and functions for each stage of the data science workflow
- Setting up your machine learning environment
- Build an App to Find Underpriced Apartments
- Build an App to Find Cheap Airfares
Forecast the IPO Market Using Logistic Regression
- The IPO market
- Data cleansing and feature engineering
- Binary classification with logistic regression
- Generating the importance of a feature from our model
Create a Custom Newsfeed
- Creating a supervised training set with Pocket
- Using the Embedly API to download story bodies
- Basics of Natural Language Processing
- Support Vector Machines
- IFTTT integration with feeds, Google Sheets, and email
- Setting up your daily personal newsletter
Predict whether Your Content Will Go Viral
- What does research tell us about virality?
- Sourcing shared counts and content
- Exploring the features of shareability
- Building a predictive content scoring model
Use Machine Learning to Forecast the Stock Market
- Types of market analysis
- What does research tell us about the stock market?
- How to develop a trading strategy
- Building the regression model
- Classifying Images with Convolutional Neural Networks
- Building a Chatbot
- Build a Recommendation Engine
- What's Next?
- Other Books You May Enjoy
- Title: Python Machine Learning Blueprints - Second Edition
- Release date: January 2019
- Publisher(s): Packt Publishing
- ISBN: 9781788994170