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Classical Machine Learning | Incognition
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Classical Machine Learning
Master the algorithms and techniques used in real-world tabular machine learning.
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41 lessons
Chapter 1: Decision Trees & Ensemble Learning
0 / 11 lessons
1
Decision Trees
2
Splitting Data
3
Entropy
4
Information Gain
5
Gini Index
6
Tree Depth & Pruning
7
Random Forest
8
Bagging
9
Boosting
10
XGBoost
11
CatBoost & LightGBM
Chapter 2: Feature Engineering & Selection
0 / 9 lessons
1
Why Features Matter
2
Creating Better Features
3
Feature Interactions
4
Feature Extraction vs Feature Selection
5
Filter Methods
6
Wrapper Methods
7
Embedded Methods
8
Feature Importance
9
Best Practices
Chapter 3: Model Evaluation & Selection
0 / 13 lessons
1
Why Evaluate Models?
2
Training vs Testing Performance
3
Cross Validation
4
Accuracy
5
Precision
6
Recall
7
F1 Score
8
Confusion Matrix
9
ROC Curve
10
AUC Score
11
Regression Metrics
12
Choosing the Right Metric
13
Error Analysis
Chapter 4: Hyperparameter Optimization
0 / 8 lessons
1
Parameters vs Hyperparameters
2
Why Hyperparameter Tuning Matters
3
Grid Search
4
Random Search
5
Bayesian Optimization
6
Learning Curves
7
Validation Curve
8
Selecting the Best Model
