Skip to main content
Back to top
Ctrl
+
K
Introduction to Machine Learning
Mathematical Foundations
Linear Algebra
Vectors and Matrices
Eigenvalues and Eigenvectors
Calculus
Derivatives
Integrals
Gradients
Hessian
Probability and Statistics
Probability Distributions
Bayes’ Theorem
Descriptive Statistics
Inferential Statistics
Machine Learning Algorithms
Supervised Learning
Linear Regression
Theoretical Foundations
Types of Linear Regression
Model Training
Evaluating the Model
Assumptions of Linear Regression
Dealing with Violations of Assumptions
Practical Considerations
Implementation
Advanced Topics
Case Studies
Conclusion
Logistic Regression
Theoretical Foundations
Types of Logistic Regression
Model Training
Model Evaluation
Assumptions of Logistic Regression
Dealing with Violations of Assumptions
Practical Considerations
Implementation
Advanced Topics
Case Studies
Conclusion
Decision Trees and Random Forests
Support Vector Machines
K-Nearest Neighbors
Neural Networks and Deep Learning
Unsupervised Learning
Clustering
K-Means
Hierarchical
DBSCAN
Dimensionality Reduction
Principal Component Analysis (PCA)
t-SNE
Association Rules
Apriori
Eclat
Reinforcement Learning
Markov Decision Processes (MDP)
Q-Learning
Deep Q-Networks (DQN)
Model Evaluation and Validation
Train/Test Split
Cross-Validation
Evaluation Metrics
Accuracy
Precision
Recall
F1 Score
ROC-AUC
Confusion Matrix
Feature Engineering
Feature Selection
Feature Scaling and Normalization
Handling Missing Values
Encoding Categorical Variables
Data Preprocessing
Data Cleaning
Data Transformation
Data Augmentation
Overfitting and Underfitting
Bias-Variance Tradeoff
Regularization Techniques
L1
L2
Dropout
Cross-Validation
Neural Networks and Deep Learning
Perceptrons
Multi-Layer Perceptrons (MLP)
Backpropagation
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN)
Transfer Learning
Advanced Topics
Natural Language Processing
Text Preprocessing
Word Embeddings
Sequence Models
Computer Vision
Image Preprocessing
Object Detection
Generative Models
Time Series Analysis
Time Series Decomposition
ARIMA
LSTM Networks
Tools and Libraries
Programming Languages
Libraries and Frameworks
Scikit-Learn
TensorFlow
Keras
PyTorch
Pandas
NumPy
Jupyter Notebooks
Projects and Case Studies
Ethics and Best Practices
Resources for Further Learning
Repository
Open issue
.ipynb
.pdf
Principal Component Analysis (PCA)
Principal Component Analysis (PCA)
#
