Talks and presentations

Tweet Emotion Recognition with TensorFlow

January 02, 2025

Tweet Emotion Recognition with TensorFlow provides an applied introduction to building deep learning models for understanding emotions expressed in tweets. It focuses on leveraging TensorFlow and Natural Language Processing (NLP) techniques to detect emotions such as happiness, sadness, anger, and more from social media text. Learners explore how emotion recognition can enhance sentiment analysis, improve user engagement, and support applications like mental health monitoring or customer feedback analysis.

Fine Tune BERT for Text Classification with TensorFlow

January 02, 2025

Fine Tune BERT for Text Classification with TensorFlow offers a hands-on introduction to the practical application of transfer learning using BERT (Bidirectional Encoder Representations from Transformers) for natural language processing tasks, specifically text classification. Learners are introduced to the fundamentals of BERT, including how it works, why it is effective for NLP tasks, and its advantages over traditional machine learning approaches for textual data. By the end of the course, students will have a clear understanding of how pretrained models can be fine-tuned for specific tasks, saving time and resources compared to training models from scratch.

Transfer Learning for NLP with TensorFlow Hub

December 19, 2024

The Transfer Learning for NLP with TensorFlow Hub course on Coursera introduces participants to the powerful concept of transfer learning in natural language processing (NLP). Transfer learning allows developers to leverage pre-trained models, reducing the need for massive datasets and extensive computational resources. Participants learn why this technique is particularly effective for NLP, where tasks like sentiment analysis, text classification, or entity recognition require understanding complex linguistic patterns. The course emphasizes how pre-trained models simplify these challenges by encoding linguistic knowledge learned from large-scale datasets.

Natural Language Processing

September 20, 2020

The Natural Language Processing course on Coursera is a comprehensive introduction to the field of NLP, designed to equip participants with the knowledge and tools to work on text and language data. The course begins by explaining the fundamentals of NLP, including tokenization, stemming, lemmatization, and text preprocessing. Learners are introduced to the unique challenges of understanding and processing human language, such as ambiguity, context, and syntax, and how computational methods can address these complexities.

Introduction to Data Science in Python

July 09, 2020

The Introduction to Data Science in Python course on Coursera offers a comprehensive entry point into the world of data science, focusing on Python as the primary tool for data analysis. The course begins by introducing participants to the fundamental concepts of data science, such as data wrangling, exploration, and visualization. Learners are guided through Python’s essential libraries, such as pandas for data manipulation and NumPy for numerical computations, building a solid foundation for handling and processing structured data effectively.

Machine Learning Algorithms: Supervised Learning Tip to Tail

June 28, 2020

The Machine Learning Algorithms: Supervised Learning Tip to Tail course on Coursera provides a deep dive into the foundations and practical applications of supervised learning. It is designed to help participants understand how supervised learning works, its real-world relevance, and how to implement and optimize these algorithms. The course begins by explaining the core concepts of supervised learning, such as labeled data, target prediction, and evaluation metrics. Learners are introduced to common tasks like classification and regression, which form the basis of predictive modeling in a wide range of industries.

SUKAIRAJ HAFIZ IMAM