Course Description

Understand Machine Learning Engineering by Building Projects Machine learning is one of the most rapidly growing fields in computer science, and it has revolutionized the way we think about artificial intelligence. Machine learning engineering is a critical part of building effective machine learning systems, and it's important to understand how to approach this field. This course is designed to help you build practical machine learning projects that will help you to gain a solid understanding of machine learning engineering. The course begins with an introduction to machine learning and its applications. You'll learn the basic concepts of machine learning, including supervised and unsupervised learning, and you'll understand the difference between the two. You'll also learn about the different types of machine learning algorithms and their strengths and weaknesses. The course will then dive into the process of building a machine learning project. You'll learn how to collect and preprocess data, build a machine learning model, and evaluate the performance of the model. You'll also learn how to improve the performance of your model by tuning hyperparameters and using ensemble methods. Throughout the course, you'll work on several hands-on machine learning projects. These projects will help you to apply the concepts you've learned and gain practical experience in building machine learning systems. You'll build projects such as a sentiment analysis system, a recommendation engine, and a fraud detection system. In addition to building projects, the course will also cover best practices in machine learning engineering. You'll learn about version control, testing, and deployment of machine learning models. You'll also learn about the ethical considerations involved in building machine learning systems. By the end of this course "Understand Machine Learning Engineering by Building Projects", you'll have a solid understanding of machine learning engineering and be able to build practical machine learning systems. You'll also have a portfolio of machine learning projects to showcase your skills to potential employers. Whether you're just getting started with machine learning or looking to expand your skills, this course is an excellent way to gain practical experience in machine learning engineering. Author: Alexey Grigorev