Undergraduate Certificate in Mastering Python NLP: Text Classification Projects

This course is designed for undergraduate students and professionals with a basic understanding of Python programming who wish to advance their skills in natural language processing (NLP). Participants will learn to apply Python for text classification tasks, including sentiment analysis, topic modeling, and categorization. By the end, students will have developed a project portfolio showcasing their ability to handle real-world NLP challenges.

Students will gain hands-on experience with popular NLP libraries such as NLTK, spaCy, and scikit-learn. They will also understand the preprocessing steps, model training, evaluation, and deployment of NLP solutions. Practical assignments and a capstone project will ensure they can implement these skills effectively in various applications.

1. 1. Introduction to Python and NLP: Learners will study the basics of Python programming and foundational Natural Language Processing (NLP) concepts, gaining skills in setting up development environments and processing text data.
2. 2. Text Preprocessing and Cleaning: This module covers text normalization techniques such as tokenization, stemming, and lemmatization, enabling learners to clean and prepare text data for analysis.
3. 3. Feature Extraction for Text Classification: Learners will explore various methods for extracting features from text, including bag-of-words, TF-IDF, and n-grams, to build effective text classification models.
4. 4. Supervised Learning for Text Classification: This module introduces learners to supervised learning techniques and algorithms specifically applied to text classification tasks, such as Naive Bayes, SVM, and logistic regression.
5. 5. Unsupervised Learning Approaches: Learners will study unsupervised learning methods for text classification, including topic modeling using LDA and clustering techniques like K-means, enhancing their ability to handle unlabeled data.
6. 6. Deep Learning Models for Text Classification: This module covers advanced deep learning techniques, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), to build robust text classification models.
7. 7. Evaluating and Optimizing Text Classification Models: Learners will learn how to evaluate the performance of text classification models using metrics like accuracy, precision, recall, and F1-score, and techniques for model optimization and tuning.
8. 8. Real-World Text Classification Projects: In this practical module, learners will apply their knowledge to real-world text classification projects, gaining hands-on experience in tackling complex NLP tasks and interpreting results.
9. 9. Advanced Topics in Text Classification: This module delves into advanced topics and recent developments in text classification, such as transfer learning, large language models, and handling imbalanced datasets.
10. 10. Final Project and Presentation: Learners will complete an original text classification project, applying all the skills and knowledge gained throughout the programme, and present their findings in a comprehensive final project.