The search engine giant has come up with a new course ‘Learn with Google AI’ that acts as a practical introduction to machine learning to all users for free.
Whether you you need guidance on learning to code or you are a seasoned machine learning practitioner, Google is here to help. The search engine giant has come up with a new course -- ‘Learn with Google AI’ -- that acts as a practical introduction to machine learning to all users for free.
Machine learning (ML) is a branch of artificial intelligence (AI) and pertains to a computer's ability to execute certain tasks on its own without being programmed by a human being. Some examples of ML include self-driving cars, speech recognition, language translators, etc.
Google’s new machine learning crash course is designed to provide a fast-paced self-study guide for aspiring machine learning practitioners using high-level TensorFlow (TF) APIs. It features a series of video lessons with lectures from ML experts, real-world case studies and hands-on practice exercises to help users learning about key ML algorithms and frameworks.
How to enroll for Google’s Machine Learning Crash Course
The ML crash course is available for users for free. Those who are interested can access the ‘Learn with Google AI’ at ai.google/education/ and click on ‘Machine Leaning Crash Course with TensorFlow APIs’ or directly access it at developers.google.com/machine-learning/crash-course/.
The next step is to click ‘Start Crash Course’ to start learning how to apply fundamental ML concepts with the available training resources.
Prerequisites to start ML Crash Course
The ML course does not presume or require any prior knowledge in machine learning. However, to understand the concepts presented and complete the exercises, the students must have knowledge of introductory level of algebra. This includes understanding of variables and coefficients, linear equations, logarithms, etc.
The users also need to have a knowledge of programming basics, and some experience coding in Python as the programming exercises in the course are coded in the same programming language.
However, if a user has no knowledge of the mentioned prerequisites, linked resources are also available in the course structure.
