Most of us have heard about the term Machine Learning, but surprisingly the question frequently asked by developers across the Globe is, "How do I get started in Machine Learning?" One reason could be the vastness of the subject area because people often get overwhelmed by the abstractness of ML and terms such as regression, supervised learning, probability density function, and so on. This systematic guide will teach you various Machine Learning techniques.

You start with the very basics of data and mathematical models in easy-to-follow language that you are familiar with; you will feel at home while implementing the examples. The course introduces you to various libraries and frameworks used in the world of Machine Learning, and then, without wasting any time, you will get to the point and implement regression, clustering, classification, and more, all with fun examples.

Style and Approach

This course gives you a glimpse into Machine Learning models and the application of models at scale using clustering, classification, regression, and reinforcement learning, all with fun examples. Hands-on examples will be presented to understand the power of problem-solving with Machine Learning and advanced architectures, software installation, and configuration.