Advanced Statistics for Machine Learning [Video]

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Advanced Statistics for Machine Learning [Video]

Pratap Dangeti

Building various machine learning models using Python and R
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Video Details

ISBN 139781788994989
Course Length2 hours 10 minutes

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 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.

Style and Approach

This course applies the problem/solution approach. Each video focuses on a particular task at hand, and is explained in a very simple, easy to understand manner.

Table of Contents

Neural Networks
The Course Overview
Artificial Neural Networks
Forward Propagation and Back Propagation
Optimization of Neural Networks
ANN Classifier Applied on Handwritten Digits
Introduction to Deep Learning
Unsupervised Learning
K-means Clustering
Principal Component Analysis
Singular Value Decomposition
Deep Autoencoders
Deep Autoencoders Applied on Handwritten Digits
Reinforcement Learning
Introduction to Reinforcement Learning
Reinforcement Learning Basics
Markov Decision Process and Bellman Equations
Dynamic Programming
Monte Carlo Methods
Temporal Difference Learning

What You Will Learn

  • Understand artificial neural network concepts 
  • Introduce different types of Unsupervised Learning
  • Execute various models of Reinforcement Learning

Authors

Table of Contents

Neural Networks
The Course Overview
Artificial Neural Networks
Forward Propagation and Back Propagation
Optimization of Neural Networks
ANN Classifier Applied on Handwritten Digits
Introduction to Deep Learning
Unsupervised Learning
K-means Clustering
Principal Component Analysis
Singular Value Decomposition
Deep Autoencoders
Deep Autoencoders Applied on Handwritten Digits
Reinforcement Learning
Introduction to Reinforcement Learning
Reinforcement Learning Basics
Markov Decision Process and Bellman Equations
Dynamic Programming
Monte Carlo Methods
Temporal Difference Learning

Video Details

ISBN 139781788994989
Course Length2 hours 10 minutes
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