Introduction
This roadmap gives clear steps for students and beginners who want to learn NLP. The goal is simple learning with practical actions. Next, you move from Python basics to transformers then to real projects.
هاد ال roadmap غادي تعاونك تبدأ ف NLP ب خطوات واضحين و بلا تعقيد. غادي تمشي من Python حتى ال transformers.
1. Learn Python Basics
NLP depends on Python. Work with strings. Write simple scripts. Process text files.
What to Learn
- Data types
- Functions
- File reading and writing
- Regex basics
تعلم Python basics. خدم ب strings و regex ودير سكريبتات كيقراو و يكتبو النصوص.
2. Study Core Math
NLP needs light math. Focus on basic linear algebra, probability, and statistics.
Important Points
- Vectors and matrices
- Distributions
- Mean and variance
- Conditional probability
الرياضيات هنا بسيطة. تعلم vectors و distributions و الاحصائيات الأساسية.
3. Learn NLP Fundamentals
Start with basic text processing. Clean text. Remove noise. Tokenize. Normalize. Extract features.
Key Concepts
- Tokenization
- Stemming
- Lemmatization
- Stopword filtering
- N grams
فهم أساسيات NLP. دير tokenization. حدف stopwords. دير normalization.
4. Learn Traditional NLP Models
Before transformers, learn traditional models. They help build intuition.
Algorithms to Study
- Bag of Words
- TF IDF
- Naive Bayes
- Logistic Regression
- SVM for text
جرب TF IDF و Naive Bayes و logistic regression ف نصوص صغار.
5. Learn Word Embeddings
Embeddings give meaning to words. Study distributed representations.
What to Cover
- Word2Vec
- GloVe
- FastText
- Cosine similarity
تعلم Word2Vec و GloVe و FastText و استعمل cosine similarity.
6. Learn Neural NLP
Deep learning gives stronger NLP models. Learn networks that handle sequences.
Core Models
- Feedforward networks for text
- RNN
- LSTM
- GRU
تعلم RNN و LSTM و GRU وكيفاش كيخدمو مع النصوص.
7. Learn Attention and Transformers
Transformers drive modern NLP. Study attention. Study encoder and decoder blocks. Learn fine tuning.
Focus Areas
- Self attention
- Positional encoding
- Encoder blocks
- Decoder blocks
- Fine tuning transformer models
تعلم attention و positional encoding و encoder o decoder. هادو الأساس د transformers.
8. Work With NLP Frameworks
Use real NLP libraries. They improve workflow and speed.
Useful Tools
- PyTorch
- TensorFlow
- Hugging Face Transformers
- spaCy
- NLTK
استعمل Hugging Face و spaCy و PyTorch ف مشاريعك.
9. Build Real Projects
Apply skills with real datasets. Train models. Debug code. Improve accuracy.
Project Ideas
- Sentiment analysis
- Spam detection
- Named entity recognition
- Machine translation
- Question answering
دير sentiment analysis ولا spam detection باش تطبق المفاهيم.
10. Learn Evaluation and Deployment
Study evaluation. Export models. Build APIs. Deploy NLP systems.
Key Metrics
- Accuracy
- Precision
- Recall
- F1 score
- BLEU score for translation
قيم الموديل ب accuracy و recall و F1. و تعلم BLEU للترجمة.
Syntax or Model Structure Example
Below is a simple example showing how to tokenize text with NLTK.
import nltk
from nltk.tokenize import word_tokenize
text = "Natural Language Processing is important"
tokens = word_tokenize(text)
print(tokens)
هادا مثال بسيط باش دير tokenization باستعمال NLTK.
Exercises
- Write a Python script that loads and prints a text file.
- Create a regex that finds all email addresses in text.
- Tokenize a paragraph and count word frequency.
- Train a TF IDF model on a small dataset.
- Train a Naive Bayes classifier for sentiment.
- Generate word embeddings with Word2Vec.
- Build a simple RNN for text.
- Fine tune a transformer for classification.
- Evaluate a translation model with BLEU.
- Deploy an NLP model with a small API.
Conclusion
Follow the roadmap step by step. Train models. Test ideas. Build strong NLP projects.
تبع الخطوات و خدم بكثرة باش تطور مهاراتك ف NLP.