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Computer Science
Artificial Intelligence
Machine Learning
Machine Learning with Python
1. Foundations of Machine Learning and Python
2. Core Python Libraries for Data Science
3. Machine Learning Workflow with Scikit-Learn
4. Supervised Learning Algorithms
5. Unsupervised Learning Algorithms
6. Introduction to Deep Learning
7. Deep Learning with Python Frameworks
8. Advanced Topics and Applications
9. Model Deployment and MLOps
Advanced Topics and Applications
Natural Language Processing
Text Preprocessing
Text Cleaning
Noise Removal
Case Normalization
Special Character Handling
Tokenization
Word Tokenization
Sentence Tokenization
Subword Tokenization
Text Normalization
Stop Word Removal
Stemming
Lemmatization
Text Representation
Traditional Methods
Bag of Words
TF-IDF
N-grams
Word Embeddings
Word2Vec
Skip-gram Model
CBOW Model
GloVe
Global Vectors
Co-occurrence Matrix
FastText
Subword Information
Out-of-vocabulary Handling
Advanced NLP Models
Transformer Architecture
Self-Attention Mechanism
Multi-Head Attention
Positional Encoding
BERT Models
Bidirectional Encoding
Masked Language Modeling
Next Sentence Prediction
GPT Models
Generative Pre-training
Autoregressive Generation
Fine-tuning Applications
NLP Tasks
Text Classification
Sentiment Analysis
Topic Classification
Spam Detection
Named Entity Recognition
Entity Types
Sequence Labeling
Text Generation
Language Modeling
Creative Writing
Code Generation
Computer Vision
Image Processing Fundamentals
Image Representation
Color Spaces
Image Transformations
Object Detection
Bounding Box Prediction
Region-based Methods
Single-shot Methods
Image Segmentation
Semantic Segmentation
Instance Segmentation
Panoptic Segmentation
Generative Models
Variational Autoencoders
Generative Adversarial Networks
Diffusion Models
Time Series Analysis
Time Series Components
Trend Analysis
Seasonality Detection
Cyclical Patterns
Irregular Components
Stationarity
Stationarity Tests
Augmented Dickey-Fuller Test
KPSS Test
Differencing
First Differencing
Seasonal Differencing
Time Series Decomposition
Additive Decomposition
Multiplicative Decomposition
STL Decomposition
Autocorrelation Analysis
Autocorrelation Function
Partial Autocorrelation Function
Lag Selection
Traditional Forecasting Models
ARIMA Models
Model Identification
Parameter Estimation
Diagnostic Checking
Seasonal ARIMA
Seasonal Parameters
Model Selection
Exponential Smoothing
Simple Exponential Smoothing
Holt's Method
Holt-Winters Method
Machine Learning for Time Series
Feature Engineering
Lag Features
Rolling Statistics
Date-time Features
Cross-Validation Strategies
Time Series Split
Walk-forward Validation
Deep Learning for Time Series
RNN-based Models
CNN-based Models
Transformer Models
Reinforcement Learning
RL Fundamentals
Agent-Environment Interaction
States, Actions, and Rewards
Markov Decision Processes
Policy and Value Functions
Value-Based Methods
Q-Learning
Q-Table
Temporal Difference Learning
Exploration Strategies
Deep Q-Networks
Function Approximation
Experience Replay
Target Networks
Policy-Based Methods
Policy Gradient Methods
Actor-Critic Methods
Proximal Policy Optimization
RL Applications
Game Playing
Robotics
Resource Allocation
Trading Strategies
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