This is the code repository for Python Machine Learning Blueprints - Second Edition, published by Packt.

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

This book covers the following exciting features: 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

If you feel this book is for you, get your copy today!

All of the code is organized into folders. For example, Chapter02.

The code will look like the following:

import requests
r = requests.get(r"https://api.github.com/users/acombs/starred")
r.json()

Following is what you need for this book: 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.

With the following software and hardware list you can run all code files present in the book (Chapter 1-11).

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.

• Building Machine Learning Systems with Python - Third Edition [Packt] [Amazon]

• Machine Learning Algorithms - Second Edition [Packt] [Amazon]

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.

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.

Python Machine Learning Blueprints: Intuitive data projects you can relate to

Python: Step into the World of Machine Learning

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