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Instructor
Explico , Instructor - Machine Learning for Executives

Explico

Explico is a management consultancy that leverages depth and breadth of business and financial expertise to define, analyze, and solve business needs. They have the ability to solve business needs no matter how complex. Explico's approach draws on business and financial concepts, academic knowledge, and real-world business experience.
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Instructor: Explico

An introductory course on Supervised, Unsupervised, Reinforcement learning, and machine learning applications.

Get introduced to Machine Learning and gain a good basic understanding of its applications.

Course Description

This course covers Machine Learning Challenges, Learning Types, Supervised, Unsupervised, Reinforcement, and Applications.

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What am I going to get from this course?

Be conversant on topics in Machine Learning and identify further areas of interest in AI.

Prerequisites and Target Audience

What will students need to know or do before starting this course?

There is no prerequisite knowledge.

Who should take this course? Who should not?

 Those who are looking to get introduced to Machine Learning and gain a good basic understanding of Supervised, Unsupervised, and Reinforcement learning.

Curriculum

Module 1: Machine Learning

Lecture 1 Introduction

Machine learning methods based on data Models and Statistical learning approaches including Supervised and Unsupervised learning types. An explanation of machine learning process.

Lecture 2 History

History of Machine Learning and how it ties to growth of data and computing power.

Lecture 3 Challenges

Challenges as they relate to data collection, types, and modeling.

Lecture 4 Learning Types

Supervised and Unsupervised learning techniques.

Lecture 5 Supervised Learning

Supervised Learning including regression, rule based, k-Nearest, Decision Tree, support vector matrix and Naïve Bayes.

Lecture 6 Unsupervised Learnings

Unsupervised learning inkling Factorial Analysis, Principal Component Analysis, Cluster Analysis, and K-means.

Lecture 7 Reinforcement

An introduction to Reinforcement Learning.

Lecture 8 Application Part 1

Applications Online advertisement with supervised and supervised learning. Supervised for recommendation: Classification, Decision Trees, Random Forest, k nearest neighbors, and SVM. Unsupervised, Cluster analysis and Association.

Lecture 9 Application Part 2

Applications including cluster analysis, association, neural networks.

Lecture 10 Conclusion

Course summary.