Machine learning has been around for decades, but its usage was limited to specialized applications such as Optical Character Recognition (OCR) due to constraints in computing resources. With advancements in computing and communication technology, powerful computing resources are becoming more affordable, and it is becoming possible to implement and use machine learning to solve complex problems in various application domains effectively. Whether you are in healthcare, banking, or manufacturing domains, machine learning may suit your needs.

This course assumes you know close to nothing about Machine Learning. You will learn the concepts and tools needed to implement programs that learn from data by using production-ready Python frameworks. The course comprises of two parts: 1) Fundamentals of Machine Learning and 2) Building and implementing machine learning models with Jupyter Notebook. The first part introduces the types of machine learning techniques, the typical workflow of a machine learning project, and going through an example project using some datasets. The second part covers the basics of Jupyter Notebook, implementation of the Machine Learning Models in both non-distributed and distributed computing environment. At the end of the course, you will have the ability to utilize machine learning models to solve some real problems.