Overview:

Welcome to "NLP Essentials: From Text to Insights," a comprehensive guide to Natural Language Processing for beginners and aspiring professionals. This course bridges the gap between theoretical foundations and practical applications in NLP. You'll embark on a journey from basic text manipulation to state-of-the-art language models, gaining hands-on experience with real-world NLP projects along the way.

Throughout this course, we'll explore both classical and modern NLP techniques, providing you with a well-rounded understanding of the field's evolution and current best practices. You'll learn how to process and analyze text data, implement machine learning algorithms for language tasks, and leverage powerful pre-trained models. By the end of the course, you'll be equipped with the knowledge and skills to tackle complex NLP challenges in professional settings.

Who this course is for:

This course is designed for:

- Beginners in NLP looking to build a strong foundation

- Software developers interested in adding language processing capabilities to their applications

- Data scientists and analysts seeking to expand their skillset into text-based data

- Students and researchers in computer science, linguistics, or related fields who want practical NLP skills

- Professionals in industries like finance, healthcare, or marketing who work with text data and want to extract meaningful insights

While we start from the basics, this course quickly progresses to intermediate and advanced topics, making it ideal for those with some programming experience who are ready to dive deep into NLP.

Prerequisites:

To get the most out of this course, you should have:

- Basic understanding of Python programming (variables, data structures, functions, loops)

- Familiarity with fundamental machine learning concepts (supervised vs unsupervised learning, basic model evaluation)

- Basic knowledge of statistics and probability

- Comfort with mathematical notation and basic linear algebra

- Experience with data manipulation libraries like NumPy and Pandas is helpful but not required

Skills that you will learn:

By completing this course, you will:

- Gain proficiency in processing and manipulating text data using Python

- Understand and implement classical NLP algorithms for tasks like text classification and named entity recognition

- Learn to create and use word embeddings for improved language representation

- Master the fundamentals of deep learning for NLP, including working with transformers and attention mechanisms

- Develop skills in fine-tuning and deploying pre-trained language models

- Learn best practices for evaluating NLP models and interpreting results

- Gain hands-on experience with popular NLP libraries and frameworks

- Develop the ability to approach and solve real-world NLP problems systematically

Course contents:

1. Introduction to Natural Language Processing for Professionals

  - Overview of NLP and its applications

  - Key challenges in processing natural language

  - Introduction to the NLP pipeline

2. History and Development of NLP

  - Evolution of NLP techniques

  - Milestones in NLP research

  - Current state-of-the-art and future trends

3. String Processing in Python

  - Basic text manipulation with Python

  - Regular expressions for pattern matching

  - String processing in pandas

4. Classical Methods for Supervised Learning in NLP

  - Bag-of-words and TF-IDF representations

  - Naive Bayes and SVM for text classification

  - Hidden Markov Models for sequence labeling

5. Word Embeddings in NLP

  - Introduction to distributional semantics

  - Word2Vec, GloVe, and FastText

  - Using pre-trained embeddings in NLP tasks

6. Evaluation Methods for NLP Models

  - Metrics for classification, regression, and ranking tasks

  - Cross-validation and holdout sets

  - Challenges in NLP evaluation

7. Transformers - An Overview

  - Attention mechanisms and self-attention

  - Architecture of transformer models

  - Understanding BERT and its variants

8. Pre-trained Models in NLP

  - Overview of popular pre-trained models (BERT, GPT, T5)

  - Transfer learning in NLP

  - Fine-tuning strategies for specific tasks

9. Model Training for NLP Tasks

  - Data preparation and preprocessing

  - Hyperparameter tuning and optimization

  - Handling class imbalance and long texts

10. Deployment of NLP Models

   - Model serialization and export

   - API development for NLP services

   - Scalability and performance considerations

Each chapter will include hands-on coding exercises, real-world examples, and mini-projects to reinforce learning and build practical skills.