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1. Introduction to Artificial Intelligence and Machine Learning

13

What is Artificial Intelligence?
Why is Artificial Intelligence Important?
Artificial Intelligence Technologies
Artificial Intelligence and Related Fields
Benefits of Machine Learning
Machine Learning Use Cases
Types of Machine Learning
How Machine Learning Works
Traditional Software vs Machine Learning
When to Apply Machine Learning

2. Machine Learning Success Factors

5

Key Roles in Machine Learning
Machine Learning Process
Data
Enterprise Machine Learning Platforms
Machine Learning Success Factors: Key Takeaways

3. Build and Use a Quick Machine Learning Model

3

Build and Use a Quick ML Model: Introduction
Build a Quick ML Model: Regression
Build and Use a Quick ML Model: Classification

4. Defining the Business Problem

4

Defining the Business Problem: Introduction
Defining the Business Problem: Framework 1
Defining the Business Problem: Framework 2
Defining the Business Problem: Framework 3

5. Data Understanding

8

Data Understanding: Introduction
Data Sources
Structure of a Dataset
Types of Data
Data Structures
Basic Statistics
Exploratory Data Analysis
Exploratory Data Analysis Hands-on Lab

6. Data Preparation

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Data Preparation: Introduction
Data Blending
Data Cleaning
Dimensionality Reduction
Feature Transformation
Feature Engineering
Data Leakage (or Leakage)
Data Preparation Hands-on Lab

7. Modeling

1

Modeling: Introduction

8. Predictive Modeling

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Predictive Modeling (Supervised Learning): Introduction
Overfitting and Underfitting
Model Evaluation
Choice of Algorithm
Performance Metrics
Model Selection
Hyperparameter Optimization
Model Interpretation
Predictive Modeling Hands-on Lab

9. Model Validation

1

Model Validation

10. Model Deployment

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Model Deployment: Introduction
Key Model Deployment Questions
Common Ways of Using Model Predictions
Model Deployment Options
Model Monitoring
Model Deployment Hands-on Lab

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