This course will introduce applied machine learning, involving more on the techniques, methods, and their implementation. The course shall also cover Python based coding of machine learning algorithms applied to set of bench-marked generic data.
The popular Deep Learning techniques and their uses to work on industry-level projects will also be covered. You will learn how to use a set of data to discover potentially predictive relationships. By the end of this course, the learner will get good hold in supervised (classification) and unsupervised (clustering) technique with good exposure to real-life AI problems such as MNIST hand-written digits, IRIS dataset, and IMDB dataset.
Participants will learn the subject in following steps that will prepare them to tak e up more challenging problems at the research level:
The course will introduce the state-of-the-art IoT technologies and applications. Programming with IoT devices such as Arduino board, and NodeMCU will be covered in detail. This course includes both theory and hands-on exercises. The participants will be taught interfacing different sensors and actuators using IoT hardware like NodeMCU. There will be hands-on sessions on connecting these devices to cloud services and controlling them remotely via smart phone and web browser.
The interplay between IoT and machine learning will be covered which is vital for applications such as decision making, predictive maintenance and forecasting.