Udemy Machine Learning A-Z
Thinking about data
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Data Processing
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Regression:
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Support Vector Regression (SVR)
- Decision Tree Regression
- Random Forest Regression
- Evaluating Regression Models Performance
- Regularization Methods
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Classification:
- Logistic Regression
- K-Nearest Neighbors (K-NN)
- Support Vector Machine (SVM)
- Kernel SVM
- Naive Bayes
- Decision Tree Classification
- Random Forest Classification
- Evaluating Classification Models Performance
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Clustering:
- K-Means Clustering
- Hierarchical Clustering
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Association Rule Learning:
- Apriori
- Eclat
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Reinforcement Learning:
- Upper Confidence Bound (UCB)
- Thompson Sampling
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Natural Language Processing:
- Natural Language Processing Algorithms
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Deep Learning:
- Artificial Neural Networks (ANN)
- Convolutional Neural Networks (CNN)
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Dimensionality Reduction:
- Principal Component Analysis (PCA)
- Linear Discriminant Analysis (LDA)
- Kernel PCA
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Model Selection:
- Model Selection
- XGBoost