Time to flex your machine learning muscles! Take on the carefully designed challenges of the Machine Learning Bookcamp and master essential ML techniques through practical application.SummaryIn Machine Learning Bookcamp you Collect and clean data for training modelsUse popular Python tools, including NumPy, Scikit-Learn, and TensorFlowApply ML to complex datasets with imagesDeploy ML models to a production-ready environmentThe 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 analysis, each new project builds on what you've learned in previous chapters. You'll build a portfolio of business-relevant machine learning projects that hiring managers will be excited to see.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the technologyMaster key machine learning concepts as you build actual projects! Machine learning is what you need for analyzing customer behavior, predicting price trends, evaluating risk, and much more. To master ML, you need great examples, clear explanations, and lots of practice. This book delivers all three!About the bookMachine Learning Bookcamp presents realistic, practical machine learning scenarios, along with crystal-clear coverage of key concepts. In it, you'll complete engaging projects, such as creating a car price predictor using linear regression and deploying a churn prediction service. You'll go beyond the algorithms and explore important techniques like deploying ML applications on serverless systems and serving models with Kubernetes and Kubeflow. Dig in, get your hands dirty, and have fun building your ML skills!What's insideCollect and clean data for training modelsUse popular Python tools, including NumPy, Scikit-Learn, and TensorFlowDeploy ML models to a production-ready environmentAbout the readerPython programming skills assumed. No previous machine learning knowledge is required.About the authorAlexey Grigorev is a principal data scientist at OLX Group. He runs DataTalks.Club, a community of people who love data.Table of Contents1 Introduction to machine learning2 Machine learning for regression3 Machine learning for classification4 Evaluation metrics for classification5 Deploying machine learning models6 Decision trees and ensemble learning7 Neural networks and deep learning8 Serverless deep learning9 Serving models with Kubernetes and Kubeflow