Hands-on Supervised Machine Learning with Python

Course details

Supervised machine learning is used in a wide range of industries across sectors such as finance, online advertising, and analytics, and its here to stay. Supervised learning allows you to train your system to make pricing predictions, campaign adjustments, customer recommendations, and much more, while allowing the system to self-adjust and make decisions on its own. This makes it crucial to know how a machine learns under the hood.

This course will guide you through the implementation and nuances of many popular supervised machine learning algorithms while facilitating a deep understanding along the way. Youll embark on this journey with a quick course overview and see how supervised machine learning differs from unsupervised learning.

Next, well explore parametric models such as linear and logistic regression, non-parametric methods such as decision trees, and various clustering techniques to facilitate decision-making and predictions. As we proceed, youll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential. Finally, youll wrap up with a brief foray into neural networks and transfer learning.

By the end of the video course, youll be equipped with hands-on techniques to gain the practical know-how needed to quickly and powerfully apply these algorithms to new problems.

About the Author

Taylor Smith is a machine learning enthusiast with over five years of experience who loves to apply interesting computational solutions to challenging business problems. Currently working as Principal Data Scientist, Taylor is also an active open source contributor and staunch Pythonista.