README.md

NLP Learning Journey

Topic-Based Learning Path

This workspace is organized by topics rather than courses, allowing for progressive difficulty and focused learning. All files have been renamed with descriptive, progressive naming conventions.

Learning Path (01 → 09)

01_Text_Preprocessing

• Foundation of all NLP tasks
• Files: 01_basic_text_preprocessing.ipynb, 02_corpus_preprocessing.ipynb, 03_tweet_preprocessing_showcase.ipynb
• Start here for any NLP project

02_Basic_NLP_Concepts

• Word frequencies and statistics
• Files: 01_word_frequency_statistics.ipynb, 02_text_parsing_and_tagging.ipynb, 03_numpy_foundations_nlp.ipynb
• NumPy fundamentals for NLP

03_Classification_Models

• Logistic regression and Naive Bayes for text
• Files: 01_logistic_regression_for_text.ipynb, 02_naive_bayes_visualization.ipynb, 03_sentiment_analysis_case_study.ipynb
• Real sentiment analysis applications

04_Word_Embeddings

• Vector representations of words
• Files: 01_manipulating_word_embeddings.ipynb through 06_word_embeddings_step_by_step.ipynb
• CBOW model training and hands-on manipulation

05_Language_Models

• Building vocabulary and statistical models
• Files: 01_building_vocabulary_model.ipynb through 04_out_of_vocabulary_handling.ipynb
• Spell correction and OOV handling

06_Advanced_Techniques

• Mathematical foundations for NLP
• Files: 01_linear_algebra_for_nlp.ipynb through 04_locality_sensitive_hashing.ipynb
• PCA, vector operations, and scalable similarity

07_Neural_Networks

• Deep learning for NLP tasks
• Files: 01_tensorflow_fundamentals.ipynb, 02_siamese_networks_similarity.ipynb, 03_triplet_loss_optimization.ipynb
• TensorFlow and advanced neural architectures

08_Sequence_Models

• Sequential data processing
• Files: 01_hidden_state_activations.ipynb, 02_perplexity_evaluation.ipynb
• RNNs, LSTMs, and sequence evaluation

09_Advanced_Projects

• Complete, integrated applications
• Files: 01_sentiment_classification_project.ipynb through 10_model_evaluation_metrics.ipynb
• Real-world projects combining multiple techniques

Personal Learning Spaces

Personal_Experiments/

Your sandbox for trying new ideas and techniques. Document your experiments!

Progress_Notes/

Track your learning journey. Create notes for each topic you complete.

Project_Gallery/

Showcase your completed projects and portfolio pieces.

Resources & Utils

Utils_and_Resources/

• Utility functions and helper scripts
• Datasets and images
• Unit tests and validation tools
• Reference materials

How to Use This Structure

1. Start with 01_Text_Preprocessing - Essential foundation
2. Progress sequentially through topics 02-09
3. Document your learning in Progress_Notes/
4. Experiment freely in Personal_Experiments/
5. Build projects that combine multiple topics
6. Track your progress and note key insights

Learning Tips

• Practice-first approach: Run code before reading theory
• Build incrementally: Each topic builds on previous ones
• Create your own examples: Don't just follow tutorials
• Connect concepts: Link ideas across different topics
• Document insights: Keep notes on what you learn

Next Steps

1. Create a learning log in Progress_Notes/learning_log.md
2. Set up your first experiment in Personal_Experiments/
3. Begin with text preprocessing fundamentals
4. Build your first NLP project combining multiple topics