Do you wish to learn how companies and non-profit organizations use Machine Learning? Are you interested in the practical coding aspects of building a Machine Learning model? If so, this is the perfect course for you. This course wraps up Ken Jee and Jeff Li’s series on Machine Learning. First, they showed you how the ML Process works in practice; then, they explained the fundamentals of the most popular Machine Learning algorithms used in the data science world. Now it’s time to consider the practical application of ML models and learn how to build them independently. In this course, Ken and Jeff will help you understand how big tech firms and small businesses can use different ML algorithms to boost their results. They’ll consider more straightforward methods which don’t require much data and computational power, such as linear regression, logistic regression, and SVMs. Still, they’ll also discuss several advanced use cases of neural networks and collaborative filtering. The coding walkthroughs section is a rare opportunity to gain practical intuition and see how an experienced data scientist builds an algorithm from scratch. This allows you to better grasp the theoretical concepts and logic studied earlier. You’ll learn to tackle real-world challenges when working on ML problems and develop essential debugging and problem-solving techniques.