Module Zero: Course Introduction
Module One: Introduction to Data Science and the Data Scientist role
Module Two: Introduction to Python
Module Three: Statistics for Data Science
Module Four: Building Machine Learning (ML) Models
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Introduction to Machine Learning (10:28)
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Environment Setup - Jupyter Notebook (1:33)
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Linear Regression - Theory (8:06)
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Linear Regression - Hands-on Exercise (15:11)
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Logistic Regression - Theory (8:03)
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Logistic Regression - Hands-on Exercise (11:40)
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K-Nearest Neighbors - Theory (9:32)
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K-Nearest Neighbors - Hands-on Exercise (19:30)
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Decision Tree & Random Forest - Theory (9:02)
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Decision Tree & Random Forest - Hands-on Exercise (12:01)
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Support Vector Machine - Theory (5:11)
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Support Vector Machine - Hands-on Exercise (8:14)
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K-Means Clustering - Theory (5:59)
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K-Means Clustering - Hands-on Exercise (13:04)
Module Five: Introduction to Deep Learning (DL) Models
Module Six: Machine Learning & Deep Learning on AWS SageMaker Fundamentals
Bonus Section - Career Mentoring Module
