Building Text Classification Models in NLP

  • IntermediateLevel

  • 1988+Students Enrolled

  • 1 Hr 10 MinsDuration

  • 4.8Average Rating

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About this Course

  • This course introduces text classification in NLP, covering what is text classification, core concepts, and hands-on projects so you can build practical NLP classification models.
  • Learn key NLP techniques, text cleaning, feature engineering, and model-building to create accurate NLP models for text classification and text summarization in NLP.
  • Explore NLP fundamentals, embeddings, and feature engineering, then build deep learning NLP classification models and deploy text classification in NLP projects end to end.

Learning Outcomes

Learn NLP Fundamentals

Understand the basics of NLP and its applications.

Master Text Preprocessing

Use tokenization, stemming, and vectorization for text processing.

Build NLP Models

Train and evaluate machine learning models for text classification.

Who Should Enroll

  • Aspiring data scientists and machine learning enthusiasts eager to explore NLP and build text classification models.
  • Students and professionals in AI and data science looking to enhance their skills in natural language processing.
  • Developers and researchers interested in applying NLP techniques for automating text analysis and classification tasks.

Course Curriculum

Dive into text classification in NLP from basics to deployment. Learn preprocessing, feature engineering, ML and deep learning NLP classification models, evaluation, and real-world projects.

tools

  1. 1. What is NLP?

  2. 2. Common tasks in a NLP Project

  3. 3. NLP Libraries

  4. 4. Methods of Text Preprocessing - Part 1

  5. 5. Methods of Text Preprocessing - Part 2

  6. 6. Methods of Text Preprocessing - Part 3

  1. 1. Introduction to dataset and problem statement

  2. 2. Creating a Basic Review Classification Model

  3. 3. Understanding TF-IDF and its implementations

  4. 4. Understanding N-grams

  5. 5. Advanced Preprocessing Techniques

  6. 6. Building an basic ANN model

  7. 7. Limitations of ANN

Meet the instructor

Our instructor and mentors carry years of experience in data industry

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Apoorv Vishnoi

Head of Training

Apoorv is a seasoned AI professional with over 14 years of experience. He has founded companies, worked at start-ups and mentored start-ups at incubation cells.

Get this Course Now

With this course you’ll get

  • 1 Hour 10 Mins

    Duration

  • Apoorv Vishnoi

    Instructor

  • Intermediate

    Level

Certificate of completion

Earn a professional certificate upon course completion

  • Industry-Recognized Credential
  • Career Advancement Credential
  • Shareable Achievement
certificate

Frequently Asked Questions

Looking for answers to other questions?

Text classification in NLP is the task of assigning labels to text, such as spam vs non-spam or topic categories. It powers real applications like email filtering, sentiment analysis, ticket routing, and content moderation across industries.

We start with classical models like Logistic Regression, Naive Bayes, and SVM, then move to deep learning and transformer-based NLP models for text classification, such as BERT-style architectures for more advanced and accurate predictions.

You’ll primarily use Python with libraries like scikit-learn, Pandas, NumPy, and Hugging Face Transformers. We also cover experiment tracking, evaluation, and simple deployment workflows so you can run your text classification models end to end.

Preprocessing—like cleaning text, removing noise, handling stopwords, and normalizing tokens—helps models focus on meaningful patterns. Good preprocessing improves model accuracy, stability, and interpretability, especially when working with noisy real-world data.

Not always. Deep learning and transformer models often outperform traditional algorithms on large datasets, but simpler models can work better on small data, be easier to train, cheaper to run, and more interpretable for many business use cases.

Use text summarization in NLP when your goal is to condense content into a shorter version while preserving meaning. Use text classification when you need discrete labels, such as intent, sentiment, topic, or priority for incoming text.

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