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01 - Introduction to Course
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02 - OPTIONAL_ Python Crash Course
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03 - Machine Learning Pathway Overview
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04 - NumPy
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05 - Pandas
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06 - Matplotlib
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07 - Seaborn Data Visualizations
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08 - Data Analysis and Visualization Capstone Project Exercise
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09 - Machine Learning Concepts Overview
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10 - Linear Regression
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11 - Feature Engineering and Data Preparation
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12 - Cross Validation , Grid Search, and the Linear Regression Project
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13 - Logistic Regression
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14 - KNN - K Nearest Neighbors
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15 - Support Vector Machines
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16 - Tree Based Methods_ Decision Tree Learning
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17 - Random Forests
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18 - Boosting Methods
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19 - Supervised Learning Capstone Project
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20 - Naive Bayes Classification and Natural Language Processing
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21 - Unsupervised Learning
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22 - K-Means Clustering
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23 - Hierarchical Clustering
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24 - DBSCAN - Density-based spatial clustering of applications with noise
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25 - PCA - Principal Component Analysis and Manifold Learning
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26 - Model Deployment
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