Guide to Machine Learning Engineering: Empowering Engineers to Solve Real-World Problems

Machine learning (ML) is a rapidly growing field that is transforming industries and businesses across the globe. From powering self-driving cars to improving medical diagnoses, ML is having a profound impact on our world.

Building Intelligent Systems: A Guide to Machine Learning Engineering

by Geoff Hulten

4.3 out of 5

Language	:	English
File size	:	1197 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	370 pages
Paperback	:	148 pages
Item Weight	:	9.4 ounces
Dimensions	:	7.5 x 0.32 x 9.25 inches

FREE

As the demand for ML solutions grows, so does the need for skilled machine learning engineers. However, becoming a successful ML engineer requires more than just a strong foundation in ML algorithms. It also requires a deep understanding of software engineering principles, data science concepts, and cloud computing platforms.

This comprehensive guide has been written to provide engineers with the knowledge and skills they need to succeed in the field of machine learning engineering. It covers the fundamentals of ML, best practices for software engineering, and the latest trends in cloud computing. Whether you're a seasoned engineer or just starting your journey in ML, this book will provide you with the insights and guidance you need to succeed.

Chapter 1: to Machine Learning

This chapter provides a gentle to the field of ML. It covers the basic concepts of ML, such as supervised learning, unsupervised learning, and reinforcement learning. It also discusses the different types of ML algorithms and how they are used to solve real-world problems.

Chapter 2: Software Engineering for Machine Learning

This chapter covers the software engineering principles that are essential for developing and deploying ML solutions. It discusses topics such as software design, testing, and version control. It also provides guidance on how to choose the right cloud computing platform for your ML projects.

Chapter 3: Data Science Concepts for Machine Learning

This chapter covers the data science concepts that are essential for understanding and working with ML data. It discusses topics such as data preprocessing, feature engineering, and data visualization. It also provides guidance on how to use data science tools and libraries to analyze and prepare data for ML models.

Chapter 4: Machine Learning Algorithms

This chapter covers the most common ML algorithms and how they are used to solve real-world problems. It discusses topics such as linear regression, logistic regression, decision trees, and neural networks. It also provides guidance on how to choose the right ML algorithm for your project and how to tune the parameters of the algorithm to achieve the best results.

Chapter 5: Deploying Machine Learning Models

This chapter covers the process of deploying ML models to production. It discusses topics such as model serving, monitoring, and retraining. It also provides guidance on how to use cloud computing platforms to deploy and manage ML models.

This guide has provided a comprehensive overview of the field of machine learning engineering. It has covered the fundamentals of ML, best practices for software engineering, and the latest trends in cloud computing. Whether you're a seasoned engineer or just starting your journey in ML, this book will provide you with the knowledge and skills you need to succeed.

About the Author

Your Name is a machine learning engineer with over 10 years of experience in the field. He has worked on a wide range of ML projects, from developing self-driving cars to improving medical diagnoses. He is passionate about sharing his knowledge and experience with others, and he has written this book to help engineers succeed in the field of machine learning engineering.

Building Intelligent Systems: A Guide to Machine Learning Engineering

by Geoff Hulten

4.3 out of 5

Language	:	English
File size	:	1197 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	370 pages
Paperback	:	148 pages
Item Weight	:	9.4 ounces
Dimensions	:	7.5 x 0.32 x 9.25 inches

FREE