Practical Machine Learning with Scikit-Learn
Requirements
- Basic python knowledge
- Google Colab account
Description
Machine learning is a rapidly growing field. However, a lot of courses on the internet today do not go over some of it's most powerful algorithms. In this course, we will learn multiple machine learning algorithms, along with data preprocessing, all in under an hour. We will go over regression, classification, component analysis and boosting all in scikit-learn, one of the most popular machine learning libraries for python.
Algorithms we'll go over (in order):
Linear Regression
Polynomial Regression
Multiple Linear Regression
Logistic Regression
Support Vector Machines
Decision Trees
Random Forest
Principle Component Analysis
Gradient Boosting
XGBoost
Who this course is for:
- People looking to get into AI but don't know where to start
- People who want to build accurate models as quickly as possible
Instructor
- 4.1 Instructor Rating
- 11,111 Reviews
- 234,782 Students
- 7 Courses
I am a self taught programmer and learning enthusiast. My expertise is mainly in Artificial Intelligence, Ruby on Rails web development, Python and Linux. I hope that my courses will help students learn things that I had difficulty with in an easier and more fun way. These courses are meant to be short, sweet and quick to the point.