skilldevelopment.cpe@lpu.co.in

What you'll learn

This short-term course introduces the foundational concepts of Natural Language Processing (NLP) and Machine Learning (ML), with practical implementation using Python. It covers text processing, feature extraction, classification, and model building using popular libraries such as NLTK and scikit-learn. By the end of the course, participants will be able to: 1. Understand key NLP concepts like tokenization, stemming, and sentiment analysis. 2. Apply machine learning algorithms in various tasks. 3. Build and evaluate predictive models using Python. 4. Develop simple NLP applications such as text classifiers.

  • Overview of Natural Language Processing and Machine Learning and their applications.
  • Fundamentals of Python including data types, control structures and functions.
  • Using NumPy for numerical operations and Pandas for data manipulation and analysis.
  • Cleaning and preparing raw text (tokenization, lowercasing, stopword removal, stemming, lemmatization etc. using nltk, re packages).
  • Converting text to numeric form using Bag of Words, TF-IDF, and n-grams (using package sklearn).
  • Building models for classification tasks using labeled data (using sklearn e.g., LogisticRegression, SVC, DecisionTreeClassifier).
  • Creating and evaluating models for categorizing text (e.g., spam detection, news categorization using sklearn, nltk etc.).
  • Using metrics and tuning techniques for model performance improvement and visualisation (using sklearn, matplotlib etc. package)
  • Building sentiment classifiers using labeled datasets (e.g., movie reviews, tweets using nltk, sklearn etc.).

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Dr. Ajay Rastogi
Assistant Professor

Dr. Ajay Rastogi received the master’s and Ph.D. degrees in computer science from Jamia Millia Islamia, New Delhi, India, in 2013, 2021, respectively. Currently, he is an Assistant Professor with Lovely Professional University, Phagwara, India. His research interests include data mining, machine learning, graph mining, complex networks, and social computing.