Module 0. About the Course (3 min)

Module 1. Introduction to Machine Learning (19 min)

• Humans and Machine Learning
• What Happens In ML
• What Is Machine Learning?
• Type Of Machine Learning
• Learning Styles
• Machine Learning Algorithms
• Machine Learning Process
• Why Now?
• Applications of ML
• ML Example: Price Surging

Module 2. Introduction to Statistical Learning Theory (22 min)

• What Is Statistical Learning Theory?
• Decision Theory
• Machine Learning Action
• Sequence of Events
• Decision & Loss Functions
• Prediction Model Overview
• Continuous Vs. Categorical Data
• Bias Variance Tradeoff
• Typology Of Errors
• Least Square Method

Module 3. Supervised Learning (33 min)

• Supervised Learning
• How Does Supervised Learning Work?
• Training Model
• Everyday Example
• Types of Supervised Learning
• Algorithms
• Linear Regression
• Logistic Regression
• Decision Trees (CART)
• Ensemble Learning
• Random Forest
• Classification: K-Nearest Neighbor
• How to Select Algorithms?
• Finding The Best Algorithm
• Model Performance

Module 4. Unsupervised Learning (34 min)

• Unsupervised Learning
• How Unsupervised Learning Works
• Why Unsupervised Learning
• Applications of Unsupervised learning
• When Should You Choose Supervised Vs. Unsupervised?
• Types of Unsupervised learning

Module 5. Deep Learning (37 min)

• What is Deep Learning?
• History of Deep Learning
• How Does Deep Learning Work?
• Components of Neural Networks
• How Do You Train an Algorithm?
• What Kinds of Neural Networks Exist?
• What Kind of Problems do NN’s Solve?
• When to Use Deep Learning
• Programming Languages Used for Deep Learning
• Top 5 Learning Frameworks
• Deep Learning Uses

Module 6. Business Applications (24 min)

• Machine Learning Review
• Natural Language Processing (NLP)
• Examples
• Soft Clustering
• Clustering Example
• Classification Example
• Regression Example
• ML Across Industries

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