Course Content
The course covers various NLP techniques, including text pre-processing, tokenization, stemming, and lemmatization, and introduces students to various NLP libraries and tools such as NLTK, spaCy, and Gensim. It also covers various NLP tasks, including sentiment analysis, text classification, and named entity recognition.
Deep Learning course covers the fundamentals and applications of deep learning, including neural networks, CNNs, RNNs, and popular deep learning frameworks. The course provides hands-on experience in building and evaluating deep learning models through projects and assignments. Suitable for those with a background in computer science or data science interested in a career in deep learning.
In addition, the course explores the applications of deep learning in NLP and covers key concepts such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Students will learn how to implement deep learning models for NLP tasks, such as text generation, machine translation, and question answering
This course is a program that teaches students the concepts and techniques of processing and analyzing digital images. The course covers topics such as image representation, enhancement, restoration, segmentation, and recognition. Students will learn how to use image processing tools and algorithms to solve real-world problems and apply their knowledge to a variety of applications, such as computer vision, medical imaging, and security systems. The course will also cover the latest developments and trends in the field, providing students with the skills and knowledge they need to succeed in the fast-paced world of image processing.