In this chapter, we learned about the data science/machine learning workflow. We learned how to take our data step by step through each stage of the pipeline, going from acquisition all the way through to deployment. We also learned key features of each of the most important libraries in the Python scientific stack. We will now take this knowledge and these lessons and begin to apply them to create unique and useful machine learning applications. Let's get started!
Python Machine Learning Blueprints - Second Edition
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Python Machine Learning Blueprints - Second Edition
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Overview of this book
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.
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Current Title:
Python Machine Learning Blueprints - Second Edition
Table of Contents (13 chapters)
Preface
Free Chapter
The Python Machine Learning Ecosystem
Build an App to Find Underpriced Apartments
Build an App to Find Cheap Airfares
Forecast the IPO Market Using Logistic Regression
Create a Custom Newsfeed
Predict whether Your Content Will Go Viral
Use Machine Learning to Forecast the Stock Market
Classifying Images with Convolutional Neural Networks
Building a Chatbot
Build a Recommendation Engine
What's Next?
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Customer Reviews