About This Course

Learn about this course and its prerequisites.

We'll cover the following

The techniques you learn in this course go a long way toward building solid machine learning skills.

Intended audience and prerequisites

This course aims to build industry-standard machine learning models and provides opportunities to implement what you've learned to solve real-world problems via course exercises and projects.

If you are a beginner in machine learning, this is a course for you, though it would be great to have a basic knowledge of:

  • Python
  • NumPy
  • pandas
  • Matplotlib
  • seaborn
  • Scikit-learn

Note: If you want to review concepts regarding these libraries you can take the "Introduction to Data Science with Python" course before moving forward.

Course layout

This course provides an in-depth explanation of concepts, working principles, and the relevant code of different machine learning models. Each chapter starts with an introductory lesson followed by suitable exercises, solutions, quizzes, and projects to practice.

This course emphasizes practicing code and trying different things out since Educative's platform facilitates you with live coding environments inside your browser without any hassle to set them up. Moreover, at the end of each chapter, there's a short set of assignments for you to practice what you have learned during that chapter. The solutions to the tasks follow each exercise so that you can check your work.

  • Try to do hands-on coding along with the lessons.

  • Try to accomplish as much as you can on your own before moving toward the solution lessons.

  • Consider using the platform’s “Highlight” and “Add a note” capabilities to mark and save relevant thoughts.

  • Complete all the lessons, coding exercises, projects, and quizzes.