Machine Learning Concepts
Machine Learning
Learning Paradigms
Supervised Learning
Regression
Linear Regression
Lasso (L1)
Ridge (L2)
ElasticNet
Classification
Logistic Regression
Decision Trees
Random Forest
SVM
Naive Bayes
Unsupervised Learning
Clustering
K-Means
Hierarchical Clustering
Dimensionality Reduction
PCA
t-SNE
Semi-supervised Learning
Reinforcement Learning
Transfer Learning
Neural Networks
Architecture
Perceptron
MLP (Multi-layer Perceptron)
Deep Learning Models
Activation Functions
ReLU
Sigmoid
Tanh
Softmax
Optimization
Gradient Descent
Stochastic Gradient Descent
Mini-batch Gradient Descent
Adam / RMSprop
Regularization Techniques
Dropout
L1 (Lasso)
L2 (Ridge)
Early Stopping
Data Processing
Data Cleaning
Feature Engineering
Feature Selection
Normalization / Scaling
Dimensionality Reduction (PCA)
Model Evaluation
Cross-Validation
Confusion Matrix
Precision / Recall / F1 Score
ROC Curve / AUC
Bias-Variance Tradeoff
Ensemble Methods
Bagging
Random Forest
Boosting
AdaBoost
Gradient Boosting
Stacking
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