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Natural language processing (NLP)




Understanding of fundamental NLP concepts

Skills to be Learned:

Proficiency in using spaCy for NER model development.

Custom NER (Named Entity Recognition)

Embark on a comprehensive journey to master Named Entity Recognition (NER) using Python and spaCy. Tailor your own NER model to extract specific entities from text data across various fields.

The course is an immersive journey into the world of natural language processing and custom NER model development. In this project-based course, participants will learn to create a custom NER model capable of recognizing specific entities in unstructured text data. The course provides hands-on experience using Python, the spaCy library, and transformer-based language models to develop, train, and test a custom NER model. By the end of the course, students will have the skills needed to tackle real-world NER challenges in various domains, including medicine, finance, and more.

Learning Outcomes:

Upon completing this course, participants will:

- Gain a deep understanding of Named Entity Recognition (NER) and its applications in information extraction from text data.

- Acquire proficiency in using spaCy, a popular NLP library, for custom NER model development.

- Learn how to preprocess text data and format it for training NER models.

- Develop a custom NER model capable of recognizing user-defined entities in text.

- Evaluate and fine-tune the performance of the NER model using real-world data.

- Apply NER techniques to practical use cases, such as extracting medical entities from medical text.


- Basic knowledge of programming in Python.

- Familiarity with fundamental NLP concepts is advantageous but not mandatory.

- Access to a Python environment with the required libraries, as demonstrated in the provided code.

Libraries and Programming Language Used:

- Python for coding and scripting.

- spaCy, an open-source NLP library, for NER model development.

- Transformer-based language models, specifically "en_core_web_trf," for contextual word embeddings.

Course Syllabus:

Introduction to Named Entity Recognition (NER)

   - Understanding the significance of NER in information extraction.

   - Real-world applications of NER across various domains.

Setting Up the Environment

   - Installing and configuring the necessary libraries and resources.

   - Preparing the Python environment for NER model development.

Data Preparation

   - Loading and preprocessing text data.

   - Annotating and formatting data for NER model training.

Custom NER Model Development

   - Utilizing spaCy and transformer-based language models for NER.

   - Defining entity labels and creating training data.

Training the NER Model

   - Training the custom NER model on annotated data.

   - Monitoring and evaluating model performance.

Practical Applications

   - Applying the custom NER model to real-world text data.

   - Extracting medical entities from medical text as a practical example.

Delve into the intricate world of natural language processing (NLP) and custom Named Entity Recognition (NER) model development with our project-based course. Designed for enthusiasts keen to explore NLP, this course guides you through creating a custom NER model using Python and the spaCy library, coupled with transformer-based language models. You'll gain hands-on experience in developing, training, and testing your model, preparing you to address real-world NER challenges in diverse domains such as medicine and finance. By the end of your journey, you'll have a deep understanding of NER applications and the proficiency to develop models that can intelligently process and analyze unstructured text data.

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