About the course
Teach your machine to think for itself! Video Course
Supervised machine learning is used in a wide range of industries across sectors such as finance, online advertising, and analytics, and it’s 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. You’ll embark on this journey with a quick course overview and see how supervised machine learning differs from unsupervised learning.
Next, we’ll 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, you’ll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential. Finally, you’ll wrap up with a brief foray into neural networks and transfer learning.
By the end of the video course, you’ll be equipped with hands-on techniques to gain the practical know-how needed to quickly and powerfully apply these algorithms to new problems.
All the codes of the course are uploaded on GitHub: https://bit.ly/2nR4aMU
Style and Approach
This course is a step-by-step guide to help you understand complex mathematical concepts in a practical fashion. Though solutions may exist (i.e., implementations in various other Python libraries), this course adheres to a “learning by doing” pattern. We won’t implement everything there is to learn, and we certainly won’t be able to write everything in its most flexible or efficient form (i.e., no C or C++) in the time we have, but you’ll walk away with a great understanding and foundation of how things work under the hood.
Most algorithms we cover will be introduced first by theory and math slides, then by practical implementation and example. By the end, the hope is that you understand these algorithms in a thorough fashion.
What You Will Learn
- Crack how a machine learns a concept and generalize its understanding to new data
- Uncover the fundamental differences between parametric and non-parametric models. Distinguish why you might opt for one over the other.
- Implement and grok several well-known supervised learning algorithms from scratch; build out your github portfolio and show off what you’re capable of!
- Work with model families like recommender systems, which are immediately applicable in domains such as ecommerce and marketing
- Expand your expertise using various algorithms like regression, decision trees, clustering and many to become a much stronger Python developer
- Build your own models capable of making predictions
- Delve into some of the most popular approaches in deep learning like transfer learning and neural networks
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. LinkedIn and Github profile. https://linkedin.com/in/taylorgsmith https://github.com/tgsmith61591
Hands-on Scikit-learn for Machine Learning [Video]