Machine Learning Bookcamp: Build a Portfolio of Real-Life Projects
- 8h 48m 50s
- Alexey Grigorev
- Manning Publications
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.
In Machine Learning Bookcamp you will:
- Collect and clean data for training models
- Use popular Python tools, including NumPy, Scikit-Learn, and TensorFlow
- Apply ML to complex datasets with images
- Deploy ML models to a production-ready environment
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 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.
about the technology
Master 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 book
Machine 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!
About the Author
Alexey Grigorev is a principal data scientist at OLX Group. He runs DataTalks.Club, a community of people who love data.
In this Audiobook
Chapter 1 - Introduction to machine learning
Chapter 2 - Machine learning for regression
Chapter 3 - Machine learning for classification
Chapter 4 - Evaluation metrics for classification
Chapter 5 - Deploying machine learning models
Chapter 6 - Decision trees and ensemble learning
Chapter 7 - Neural networks and deep learning
Chapter 8 - Serverless deep learning
Chapter 9 - Serving models with Kubernetes and Kubeflow