- Job Type:
Domain Specific,Linguists - Opportunity:
Less Job Circular
Natural Language Processing (NLP) is a specific field of Artificial Intelligence (AI) focused on enabling machines to understand, interpret, and respond to human language meaningfully. NLP bridges the gap between human communication and machine understanding, making it possible for computers to process and analyze large amounts of natural language data.
- Develop, fine-tune, and deploy NLP models for language understanding and generation.
- Work on translation, sentiment analysis, chatbots, and summarization tasks.
- Collaborate with data scientists and software engineers to integrate NLP systems into products.
- Preprocessing text data (tokenization, stemming, lemmatization).
- Build and optimize NLP models for specific tasks.
- Deploying NLP solutions and integrating them into applications.
- Researching and applying cutting-edge advancements in NLP.
- Python has robust libraries for text processing, NLP, and machine learning.
- Python Basics:
- Variables, data types, loops, conditionals, functions, and OOPs.
- Libraries:
- Pandas/Polars: DataFrame library.
- NLTK & SpaCy: For text preprocessing.
- Understanding foundational concepts is critical for building advanced models.
- Tokenization, Stemming, Lemmatization.
- Stopwords removal, Part-of-Speech tagging, Named Entity Recognition (NER).
- Bag of Words, TF-IDF.
- Word Embeddings (Word2Vec, GloVe, FastText).
- Classical ML techniques are the basis for many NLP tasks.
- Text Classification (Naive Bayes, SVM).
- Sentiment Analysis, Topic Modeling (Latent Dirichlet Allocation).
- Feature Engineering for Text Data.
- NLP Videos - Machine Learning Playlist
- NLP Module
- Libraries:
- NLTK & SpaCy: For text preprocessing.
- Scikit-learn: For classical machine learning tasks.
- Powers advanced NLP models for understanding and generating text.
- Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), GRU.
- Transformer Architectures (BERT, GPT, T5).
- Sequence-to-Sequence Models (Seq2Seq, Attention Mechanisms).
- Fine-tuning Pre-trained Models for Custom Tasks.
- Deep Learning Playlist (ANN, RNN, LSTM, GRU, Transformers)
- Hugging Face Tutorials
- Basic to Advanced DL & GenAI
- Libraries:
- NLTK & SpaCy: For text preprocessing.
- Hugging Face Transformers: For state-of-the-art NLP models.
- TensorFlow/PyTorch: For custom deep learning-based NLP solutions.
- Generative models drive content creation in text, audio, and more.
- Variational Autoencoders (VAEs):
- Applications in text generation and compression.
- Transformers:
- GPT, DALL-E, T5.
- Fine-Tuning and Custom Training:
- Domain-specific adaptations of pre-trained models.
- GitHub is a crucial platform for version control and collaboration.
- Enables you to showcase your projects and build a portfolio.
- Facilitates teamwork on data science projects.
- Git Basics:
- Version control concepts, repositories, branches, commits, pull requests.
- GitHub Skills:
- Hosting projects, collaboration workflows, managing issues.
- Best Practices:
- Writing READMEs, structuring repositories, using
.gitignorefiles.
- Writing READMEs, structuring repositories, using
- Complete GitHub for NLP Engineers
- Use GitHub to practice hosting Python, SQL, and machine learning projects.
- Essential for querying, extracting, and joining data from relational databases.
- Used to preprocess and prepare data before modeling.
- Basics: SELECT, INSERT, UPDATE, DELETE.
- Intermediate: Joins (INNER, LEFT, RIGHT, FULL), subqueries.
- Advanced: Window functions, CTEs (Common Table Expressions), and query optimization.
- SQL Learning Playlist
- Programming with Mosh - SQL Playlist
- Tools like MySQL Workbench, SQLite, or PostgreSQL.
- Projects showcase your ability to apply NLP techniques in real-world scenarios.
- Build a sentiment analysis tool for customer reviews.
- Create a chatbot using Transformer models.
- Design an automatic summarizer for news articles.
- Fine-tune BERT for a domain-specific NER task.
- Preprocess text data using tools like NLTK or SpaCy.
- Train models using Scikit-learn, TensorFlow, or PyTorch.
- Fine-tune Transformer models for advanced NLP tasks.
- Deploy and integrate NLP models into applications.
By following this roadmap, you’ll develop the skills needed to become a successful NLP Engineer.
Note: We suggest these premium courses because they are well-organized for absolute beginners and will guide you step by step, from basic to advanced levels. Always remember that T-shaped skills are better than i-shaped skill. However, for those who cannot afford these courses, don't worry! Search on YouTube using the topic names mentioned in the roadmap. You will find plenty of free tutorials that are also great for learning. Best of luck!
Hazrat Ali
- 🌐 LinkedIn Profile
- 🎓 Programmer || Software Engineering