Machine Learning Bookcamp:Build a portfolio of real-life projects


Machine Learning Bookcamp:Build a portfolio of real-life projects
by:Alexey Grigorev
Publisher Finelybook 出版社:Manning Publications (July 27, 2021)
Language 语言:English
pages 页数:475 pages
ISBN-10 书号:1617296813
ISBN-13 书号:9781617296819

Book Description
The only way to learn is to practice! In Machine Learning Bookcamp, you’ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects. Taking you from the basics of machine learning to complex applications such as image and text analysis, each new project builds on what you’ve learned in previous chapters. by:the end of the bookcamp, you’ll have built a portfolio of business-relevant machine learning projects that hiring managers will be excited to see.

Machine learning is an analysis technique for predicting trends and relationships based on historical data. As ML has matured as a discipline, an established set of algorithms has emerged for tackling a wide range of analysis tasks in business and research. by:practicing the most important algorithms and techniques, you can quickly gain a footing in this important area. Luckily, that’s exactly what you’ll be doing in Machine Learning Bookcamp.

In Machine Learning Bookcamp you’ll learn the essentials of machine learning by:completing a carefully designed set of real-world projects. Beginning as a novice, you’ll start with the basic concepts of ML before tackling your first challenge:creating a car price predictor using linear regression algorithms. You’ll then advance through increasingly difficult projects, developing your skills to build a churn prediction application, a flight delay calculator, an image classifier, and more. When you’re done working through these fun and informative projects, you’ll have a comprehensive machine learning skill set you can apply to practical on-the-job problems.

What’s inside

Code fundamental ML algorithms from scratch
Collect and clean data for training models
Use popular Python tools, including NumPy, Pandas, Scikit-Learn, and TensorFlow
Apply ML to complex datasets with images and text
Deploy ML models to a production-ready environment

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