English | Size: 386.6 MB
Genre: eLearning
Video Description
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 video 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.
Style and Approach
This course contains problem solution approach. Each video focuses on a particular task at hand, and is explained in a very simple, easy to understand manner.
What You Will Learn
Introduces statistical terminology and machine learning
Provides an overview of machine learning terminology for model building and validation
Offers practical solutions for simple linear regression and multi-linear regression
Compares logistic regression and random forest using examples
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