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Resolving Machine Learning with Scikit-Learn in Python
#00 | Machine Learning & the Scikit-Learning Library
1. What will you learn in this course?
2. Install & Learn About Jupyter Lab
3. Download the Materials and Upload Your Work to GitHub
⚠️ 4. IMPORTANT: How to Work Out the Materials
5. Another Way to Download the Materials
#01 | The Linear Regression & Supervised Regression Models
1. Read Article
2. Fit the Model
3. Calculate Predictions & Visualize the Model
4. Score | Regression Model Evaluation
5. Linear Regression Model Interpretation
6. Other Regression Models: Random Forest & Support Vector Machines
7. Visualize All Models Together
8. Practical Exercise 1
#02 | The Decision Tree & Supervised Classification Models
1. Read Article
2. Fit the Model
3. Data Preprocessing
4. Model Visualization & Predictions
5. Model Interpretation
6. Model's Score
7. The Confusion Matrix
8. ROC Curve
9. Other Classification Models: Random Forest & Support Vector Machines
10. Practical Exercise 2
#03 | Train Test Split for Model Selection
1. Read Article
2. Load & Preprocess the Data
3. Build Machine Learning Models: Decision Tree, Support Vector Machines & Logistic Regression
4. Thinking Process to Create a Function
5. Understand the Need for Train Test Split
6. Reautomate Process with Train Test Split
7. Compare All Machine Learning Models in a DataFrame
8. Practical Exercise 3
#04 | Overfitting & Hyperparameter Tuning with Cross Validation
1. Read Article
2. Load & Preprocess the Data
3. The Overfitting Problem
4. Trying Different Hyperparameter Configurations
5. Cross Validation
6. Cross Validation with Support Vector Machines & K Nearest Neighbors
7. Compare All Machine Learning Models in a DataFrame
8. Practical Exercise 4
#05 | The k-Means & Unsupervised Clustering Models
1. Read the Article
2. Load the Data & Compute k-Means
3. Understand the Need for Scaling the Data
4. Preprocess the Data with MinMaxScaler
5. k-Means Comparison: Scaled Data vs Non-Scaled Data
6. Other Clustering Models
7. Practical Use Case Conclusion
8. Practical Exercise 5
#06 | Principal Component Analysis (PCA) for Dimensionality Reduction
1. Read the Article
2. k-Means Model Visualization Problem
3. Transform Original Data to Principal Components
4. Explained Variance Ratio
5. Relationship between Original Data & Principal Components
6. The Mathematical Equation of PCA
7. PCA & Clustering Interpretation
8. Practical Exercise 6
5. Understand the Need for Train Test Split
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