Google Offers Free Online Course on Machine Learning, Check Important Details

Google has offered a free online course on machine learning that can be taken by interested participants. The course called “Machine Learning Crash Course with TensorFlow APIs” is Google's fast-paced, practical introduction to machine learning that can be completed in 15 hours. The Google free online machine learning crash course features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises.

The course requires interested participants to have a basic knowledge of machine learning, NumPy, pandas, algebra, trigonometry, calculus, and so on. If participants are new to machine learning, they can also take an Introduction to Machine Learning Problem Framing being provided by Google. The one-hour self-study course teaches participants how to identify appropriate problems for machine learning.

Prerequisites to Take Google Free Online Course on Machine Learning

The course does not mandate for candidates to have any prior knowledge in machine learning. However, to understand the concepts presented and complete the exercises, students will need to meet the following prerequisites:

• Participants must be comfortable with variables, linear equations, graphs of functions, histograms, and statistical means.
• Participants should be good programmers, and ideally, have some experience programming in Python because the programming exercises are in Python. However, experienced programmers without Python experience can usually complete the programming exercises anyway, says Google.

What the Google Free Online Course on Machine Learning Will Cover?

After completing the course, participants will be able to recognize the practical benefits of mastering machine learning, and understand the philosophy behind machine learning. Some of the topics that will be covered are as follows:

• Introduction to machine learning.
• Framing.
• Descending into machine learning.
• Reducing loss.
• Define common machine learning terms.
• Describe examples of products that use machine learning and general methods of machine learning problem-solving used in each.
• Identify whether to solve a problem with machine learning.
• Compare and contrast machine learning to other programming methods.
• Apply hypothesis testing and the scientific method to machine learning problems.
• Have conversations about machine learning problem-solving methods and so on.

Those who wish to know more and take the Google free online course on machine learning are advised to visit the official website for details.