Python Machine Learning Blueprints - Second 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 KerasKey FeaturesGet to grips with Pythons machine learning libraries including scikit-learn TensorFlow and KerasImplement advanced concepts and popular machine learning algorithms in real-world projectsBuild analytics computer vision and neural network projects Book DescriptionMachine 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 youll 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 learnUnderstand the Python data science stack and commonly used algorithmsBuild 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 feedCreate applications that will recommend GitHub repositories based on ones you’ve starred watched or forkedGain the skills to build a chatbot from scratch using PySparkDevelop a market-prediction app using stock dataDelve into advanced concepts such as computer vision neural networks and deep learningWho this book is forThis 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.