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Data Science Certificate Course
Module Zero: Course Introduction
Welcome (2:58)
Meet our Coaches & Mentors
Important Information
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Module One: Introduction to Data Science and the Data Scientist role
Introduction to Data Science and the Data Scientist role (12:31)
Module Two: Introduction to Python
Environment Setup (1:33)
Python Fundamentals (22:02)
Numpy - Python for Data Analysis (13:32)
Pandas - Python for Data Analysis (15:02)
Matplotlib - Python for Data Visualisation (13:36)
Seaborn - Python for Data Visualisation (12:29)
Python summary
Module Three: Statistics for Data Science
Statistics for Data Science (11:56)
Module Four: Building Machine Learning (ML) Models
Introduction to Machine Learning (10:28)
Environment Setup - Jupyter Notebook (1:33)
Linear Regression - Theory (8:06)
Linear Regression - Hands-on Exercise (15:11)
Logistic Regression - Theory (8:03)
Logistic Regression - Hands-on Exercise (11:40)
K-Nearest Neighbors - Theory (9:32)
K-Nearest Neighbors - Hands-on Exercise (19:30)
Decision Tree & Random Forest - Theory (9:02)
Decision Tree & Random Forest - Hands-on Exercise (12:01)
Support Vector Machine - Theory (5:11)
Support Vector Machine - Hands-on Exercise (8:14)
K-Means Clustering - Theory (5:59)
K-Means Clustering - Hands-on Exercise (13:04)
Module Five: Introduction to Deep Learning (DL) Models
Introduction to Deep Learning (8:50)
Artificial Neural Network (ANN) - Theory (11:43)
Artificial Neural Network (ANN) - Hands-on Exercise (20:26)
Module Six: Machine Learning & Deep Learning on AWS SageMaker Fundamentals
Introduction to AWS (10:05)
Introduction to Machine Learning on AWS SageMaker (8:22)
Bonus Section - Career Mentoring Module
Introduction (13:51)
Data & AI Roles (6:58)
Career Growth Advice - Cvs, Interviews, LinkedIn (20:06)
Career Growth Advice - Insider Tips (10:05)
Career Growth Advice - Experience (10:03)
Career Growth Advice - Your Network (8:49)
Career Growth Advice - Coaching (7:52)
Conclusion & Links (6:15)
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Logistic Regression - Hands-on Exercise
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