My name is Peter Chen and I am the instructor for this course. I want to introduce you to the wonderful world of Machine Learning through practical examples and code. The course will cover Supervised Learning algorithms and models in machine learning. More importantly, it will get you up and running quickly with a practical and at times funny applications of Supervised Learning algorithms. The course has code & sample data for you to run and learn from. It also encourages you to explore your own datasets using Supervised Learning algorithms. Prerequisites: Beginner knowledge of Python and R. It's used mostly for expository reasons. You do NOT need to be a Python or R expert to understand this course. Basic math and comfortable with basic probability and statistics.
What am I going to get from this course?
* Know when to apply a prediction machine learning algorithm
* Know when to apply a classification machine learning algorithm
* Gained an intuition behind the math of the underlying algorithms and be able to explain it
* Learned how to use Python scikit-learn library and R libraries to build supervised machine learning models and algorithms
* Apply Python & R code to their data sets to solve prediction and classification problems
* Evaluate the effectiveness of their machine learning models
* Develop a taste for tinkering with the model to improve results