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Computer Science
Artificial Intelligence
Machine Learning
Machine Learning for Developers
1. Introduction to Machine Learning for Developers
2. Machine Learning Project Lifecycle
3. Supervised Learning Fundamentals
4. Unsupervised Learning Fundamentals
5. Python Machine Learning Ecosystem
6. Data Engineering for Machine Learning
7. Pre-trained Models and Transfer Learning
8. Model Deployment and MLOps
9. Production Monitoring and Maintenance
10. Natural Language Processing for Developers
11. Computer Vision for Developers
12. Responsible AI and Ethics
13. Advanced Topics and Specializations
Natural Language Processing for Developers
Text Preprocessing
Text Cleaning
Noise Removal
Normalization
Tokenization
Linguistic Processing
Stemming and Lemmatization
Part-of-Speech Tagging
Named Entity Recognition
Text Representation
Traditional Methods
Bag-of-Words
TF-IDF
N-gram Models
Word Embeddings
Word2Vec
GloVe
FastText
Contextual Embeddings
ELMo
BERT
GPT Models
Common NLP Tasks
Text Classification
Sentiment Analysis
Topic Classification
Spam Detection
Information Extraction
Named Entity Recognition
Relation Extraction
Event Extraction
Text Generation
Language Modeling
Text Summarization
Machine Translation
Question Answering
Extractive QA
Generative QA
Conversational AI
Transformer Architecture
Attention Mechanisms
Self-Attention
Multi-Head Attention
Positional Encoding
Pre-trained Models
BERT Family
GPT Family
T5 and Variants
Fine-Tuning Strategies
Task-Specific Fine-Tuning
Few-Shot Learning
Prompt Engineering
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11. Computer Vision for Developers