Building various machine learning models using Python and R
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 the complex statistical computations required for Machine Learning. You will gain information on statistics behind unsupervised learning, reinforcement learning, and more. You’ll master real-world examples that discuss the statistical side of Machine Learning.
In this video, you will acquire a deep knowledge of the various models of unsupervised and reinforcement learning, and explore the fundamentals of deep learning with the help of the Keras software. Furthermore, you’ll gain an overview of reinforcement learning with the Python programming language.
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.
- This video is intended for developers with a moderate knowledge in statistics who want to implement Machine Learning in their systems.
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