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Statistics
Machine Learning
1. Introduction to Machine Learning
2. Mathematical and Statistical Foundations
3. Data Preprocessing and Feature Engineering
4. Supervised Learning
5. Unsupervised Learning
6. Model Evaluation and Validation
7. Ensemble Methods and Advanced Techniques
8. Deep Learning and Neural Networks
9. Reinforcement Learning
10. Advanced Topics and Specialized Areas
11. Machine Learning Operations and Deployment
Advanced Topics and Specialized Areas
Natural Language Processing
Text Preprocessing
Tokenization
Word Tokenization
Sentence Tokenization
Subword Tokenization
Text Normalization
Lowercasing
Punctuation Removal
Unicode Normalization
Stopword Removal
Language-Specific Stopwords
Custom Stopword Lists
Stemming and Lemmatization
Porter Stemmer
Snowball Stemmer
WordNet Lemmatizer
Text Representation
Bag of Words
Term Frequency
Document-Term Matrix
Limitations
TF-IDF
Term Frequency-Inverse Document Frequency
Normalization
Variants
N-grams
Bigrams and Trigrams
Character N-grams
Skip-grams
Word Embeddings
Word2Vec
Skip-gram Model
Continuous Bag of Words
Hierarchical Softmax
Negative Sampling
GloVe
Global Vectors
Co-occurrence Matrix
Factorization
FastText
Subword Information
Out-of-Vocabulary Handling
Language Models
N-gram Language Models
Neural Language Models
Transformer-Based Models
BERT
GPT
T5
RoBERTa
NLP Tasks
Text Classification
Named Entity Recognition
Part-of-Speech Tagging
Sentiment Analysis
Machine Translation
Question Answering
Text Summarization
Computer Vision
Image Preprocessing
Image Formats and Color Spaces
Resizing and Cropping
Normalization
Data Augmentation
Rotation
Flipping
Scaling
Color Jittering
Feature Extraction
Traditional Methods
SIFT
SURF
HOG
LBP
Deep Learning Features
Convolutional Features
Pretrained CNN Features
Computer Vision Tasks
Image Classification
Object Detection
R-CNN Family
YOLO
SSD
Semantic Segmentation
U-Net
DeepLab
Instance Segmentation
Face Recognition
Optical Character Recognition
Time Series Analysis
Time Series Components
Trend
Seasonality
Cyclical Patterns
Irregular Components
Time Series Decomposition
Additive Decomposition
Multiplicative Decomposition
STL Decomposition
Stationarity
Tests for Stationarity
Differencing
Transformation Methods
Traditional Methods
ARIMA Models
Exponential Smoothing
Seasonal Decomposition
Machine Learning for Time Series
Feature Engineering
Cross-Validation Strategies
Forecasting Evaluation
Deep Learning for Time Series
RNNs for Sequences
LSTM and GRU
Attention Mechanisms
Transformer Models
Anomaly Detection
Types of Anomalies
Point Anomalies
Contextual Anomalies
Collective Anomalies
Statistical Methods
Z-Score Method
Grubbs' Test
Dixon's Q Test
Machine Learning Approaches
Isolation Forest
One-Class SVM
Local Outlier Factor
DBSCAN for Anomaly Detection
Deep Learning Methods
Autoencoders
Variational Autoencoders
Generative Adversarial Networks
Evaluation Challenges
Imbalanced Data
Lack of Labeled Anomalies
Evaluation Metrics
Recommender Systems
Collaborative Filtering
User-Based Collaborative Filtering
User Similarity Measures
Neighborhood Selection
Rating Prediction
Item-Based Collaborative Filtering
Item Similarity Measures
Recommendation Generation
Matrix Factorization
Singular Value Decomposition
Non-Negative Matrix Factorization
Alternating Least Squares
Content-Based Filtering
Item Profiles
User Profiles
Similarity Computation
Recommendation Generation
Hybrid Approaches
Weighted Hybrid
Switching Hybrid
Mixed Hybrid
Feature Combination
Deep Learning for Recommendations
Neural Collaborative Filtering
Autoencoders for Recommendations
Recurrent Neural Networks
Evaluation Metrics
Accuracy Metrics
Ranking Metrics
Diversity and Novelty
Coverage
Generative Models
Generative Adversarial Networks
Generator Architecture
Discriminator Architecture
Training Dynamics
Loss Functions
Mode Collapse
Training Stabilization
GAN Variants
DCGAN
WGAN
StyleGAN
CycleGAN
Variational Autoencoders
Encoder Network
Decoder Network
Latent Space
Reparameterization Trick
KL Divergence
Evidence Lower Bound
Autoregressive Models
PixelRNN
PixelCNN
WaveNet
Flow-Based Models
Normalizing Flows
Invertible Transformations
Real NVP
Glow
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11. Machine Learning Operations and Deployment