English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 2h 10m | 420 MB
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.
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.
What You Will Learn
- Understand artificial neural network concepts
- Introduce different types of Unsupervised Learning
- Execute various models of Reinforcement Learning
Table of Contents
01 The Course Overview
02 Artificial Neural Networks
03 Forward Propagation and Back Propagation
04 Optimization of Neural Networks
05 ANN Classifier Applied on Handwritten Digits
06 Introduction to Deep Learning
07 K-means Clustering
08 Principal Component Analysis
09 Singular Value Decomposition
10 Deep Autoencoders
11 Introduction to Reinforcement Learning
12 Reinforcement Learning Basics
13 Markov Decision Process and Bellman Equations
14 Dynamic Programming
15 Monte Carlo Methods
16 Temporal Difference Learning
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