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.

Summary

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!

What's inside

Collect and clean data for training models

Use popular Python tools, including NumPy, Scikit-Learn, and TensorFlow

Deploy ML models to a production-ready environment

About the reader

Python programming skills assumed. No previous machine learning knowledge is required.

About the author

Alexey Grigorev

is a principal data scientist at OLX Group. He runs DataTalks.Club, a community of people who love data.

Table of Contents

1 Introduction to machine learning

2 Machine learning for regression

3 Machine learning for classification

4 Evaluation metrics for classification

5 Deploying machine learning models

6 Decision trees and ensemble learning

7 Neural networks and deep learning

8 Serverless deep learning

9 Serving models with Kubernetes and Kubeflow

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