Practical Machine Learning with Scikit-Learn – Free Udemy Courses
Learn the most powerful machine learning algorithms in under an hour
What you’ll learn
Practical Machine Learning with Scikit-Learn – Free Udemy Courses
- How to implement regression, classification, and boosting algorithms
- Which algorithms work best for a given dataset
- Data preprocessing
Requirements
-
Basic python knowledge
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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 its 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 sci-kit-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
