About This Course

• Understand the Statistical fundamentals and terminology for model building and validation

• Handle simple linear regression using wine quality data

• Execute Ridge/Lasso regression model

• Perform grid search on Random Forest

• Implement Logistic Regression using credit data

Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This course will teach you all it takes to perform complex statistical computations required for Machine Learning. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it.

We will discuss the application of frequently used algorithms on various domain problems, using both Python and R programming. We will use libraries such as scikit-learn, NumPy, random Forest and so on.

By the end of the course, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem.