Fundamentals of Statistical Modeling and Machine Learning Techniques [Video]

More Information
  • 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

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.

  • 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
Course Length 2 hours
ISBN 9781788833981
Date Of Publication 30 Oct 2017


Pratap Dangeti

Pratap Dangeti is currently working as a Senior Data Scientist at Bidgely Technologies Bangalore. He has a vast experience in analytics and data science. He received his master's degree from IIT Bombay in its industrial engineering and operations research program. Pratap is an artificial intelligence enthusiast. When not working, he likes to read about next-gen technologies and innovative methodologies.