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.
About the Author
Pratap
Dangeti
develops machine learning and deep learning solutions for
structured, image, and text data at TCS, in its research and innovation
lab in Bangalore. He has acquired a lot of experience in both 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. He is also the
author of the book Statistics for Machine Learning by Packt.
