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Computer Science
Data Science
Data Science
1. Foundations of Data Science
2. Mathematical and Statistical Foundations
3. Computational Foundations and Tools
4. Data Acquisition and Management
5. Exploratory Data Analysis
6. Feature Engineering and Selection
7. Machine Learning Fundamentals
8. Advanced Machine Learning Topics
9. Big Data and Distributed Computing
10. Data Visualization and Communication
11. Model Deployment and MLOps
12. Ethics and Responsible AI
Advanced Machine Learning Topics
Ensemble Methods
Bagging
Bootstrap Aggregating
Random Forest
Extra Trees
Boosting
AdaBoost
Gradient Boosting
XGBoost
LightGBM
CatBoost
Stacking
Meta-learners
Blending
Multi-level Stacking
Voting
Hard Voting
Soft Voting
Weighted Voting
Deep Learning
Neural Network Fundamentals
Perceptron
Multi-layer Perceptron
Universal Approximation Theorem
Training Neural Networks
Backpropagation Algorithm
Gradient Descent Variants
Stochastic Gradient Descent
Mini-batch Gradient Descent
Adam Optimizer
RMSprop
Learning Rate Scheduling
Batch Normalization
Dropout
Activation Functions
Sigmoid
Tanh
ReLU
Leaky ReLU
ELU
Swish
Deep Learning Architectures
Convolutional Neural Networks
Convolution Operation
Pooling Layers
CNN Architectures
Applications in Computer Vision
Recurrent Neural Networks
Vanilla RNN
Long Short-Term Memory
Gated Recurrent Unit
Bidirectional RNNs
Transformer Architecture
Attention Mechanism
Self-Attention
Multi-Head Attention
Positional Encoding
Regularization Techniques
L1 and L2 Regularization
Dropout
Early Stopping
Data Augmentation
Deep Learning Frameworks
TensorFlow
PyTorch
Keras
Natural Language Processing
Text Preprocessing
Tokenization
Normalization
Stop Word Removal
Stemming
Lemmatization
Text Representation
Bag of Words
TF-IDF
Word Embeddings
Word2Vec
GloVe
FastText
Contextual Embeddings
ELMo
BERT
GPT
NLP Tasks
Sentiment Analysis
Named Entity Recognition
Part-of-Speech Tagging
Text Classification
Machine Translation
Question Answering
Topic Modeling
Latent Dirichlet Allocation
Non-negative Matrix Factorization
Latent Semantic Analysis
Computer Vision
Image Processing Fundamentals
Image Representation
Color Spaces
Image Filtering
Edge Detection
Feature Extraction
SIFT
SURF
HOG
Local Binary Patterns
Deep Learning for Computer Vision
CNN Architectures
LeNet
AlexNet
VGG
ResNet
Inception
Object Detection
R-CNN
YOLO
SSD
Image Segmentation
Semantic Segmentation
Instance Segmentation
U-Net
Time Series Analysis and Forecasting
Time Series Components
Trend
Seasonality
Cyclicity
Irregularity
Stationarity
Testing for Stationarity
Differencing
Transformation Methods
Time Series Models
Autoregressive Models
Moving Average Models
ARIMA Models
Seasonal ARIMA
Exponential Smoothing
State Space Models
Machine Learning for Time Series
Feature Engineering for Time Series
Cross-validation for Time Series
Deep Learning Approaches
RNNs for Time Series
LSTM Networks
Transformer Models
Forecasting Evaluation
Mean Absolute Error
Mean Squared Error
Mean Absolute Percentage Error
Symmetric MAPE
Forecast Accuracy Measures
Anomaly Detection
Types of Anomalies
Point Anomalies
Contextual Anomalies
Collective Anomalies
Statistical Methods
Z-score Method
Grubbs' Test
Dixon's Q Test
Machine Learning Methods
Isolation Forest
One-Class SVM
Local Outlier Factor
Autoencoders
Time Series Anomaly Detection
Statistical Process Control
Seasonal Decomposition
LSTM Autoencoders
Recommendation Systems
Collaborative Filtering
User-based Collaborative Filtering
Item-based Collaborative Filtering
Matrix Factorization
Content-based Filtering
Feature Extraction
Similarity Measures
Profile Building
Hybrid Approaches
Weighted Hybrid
Switching Hybrid
Mixed Hybrid
Deep Learning for Recommendations
Neural Collaborative Filtering
Autoencoders
Deep Matrix Factorization
Evaluation Metrics
Precision at K
Recall at K
Mean Average Precision
Normalized Discounted Cumulative Gain
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