Our Courses
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PROGRAMMING IN PYTHON
Course SynopsisThe Python course is designed for novices aiming to develop the ability to conceive and construct purposeful Python code, as well as interpret code composed by others. The curriculum imparts skills in translating conceptual problem descriptions into Python-coded applications/libraries. This foundational course is indispensable for individuals lacking prior programming experience but aspiring to establish themselves as proficient Android engineers in the future. Required TextbooksEric Matthes, “Python Crash Course: A Hands-On, Project-Based Introduction to Programming”, No Starch Press.John Zelle, “Python Programming: An Introduction to Computer Science”, Franklin, Beedle & Associates Inc. Completion CriteriaAfter fulfilling all of the following criteria, the student will be deemed to have finished the Module:Has attended 90% of all classes held.Has received an average grade of 80% on all assignments.Has received an average of 60% in assessments.The tutor believes the student has grasped all of the concepts and is ready to go on to the second module. PrerequisitesFundamental understanding of programming, bits/bytes, procedures, classes, and computer architecture. It's absolutely acceptable if you only have a theoretical understanding of programming, but you should be certain about what programming is and what you intend to gain from this session.Willing and eager to spend at least 10-20 hours (varying from student-to-student) per week outside of the training class to read/write codes in Python (self-study and practice).If you are only interested in theory and have no interest/patience in spending at least 10 hours every week throughout the duration of the course, then this course might not be for you.If you have absolutely no idea about programming or do not see yourself doing programming in the next six -odd months, then this class may not be for you.
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R Programming
R is an open-source programming language and environment dedicated to statistical computing and graphics. Its allure stems from its rich repertoire of statistical techniques and graphical capabilities, making it a preferred tool for statisticians and data miners. With an extensive collection of packages, R equips users with tools for diverse tasks, including data manipulation, visualization, and advanced modeling. From filtering and summarizing data to crafting intricate graphics with libraries like ggplot2, R empowers users to explore, analyze, and present data with precision and flair. Beyond its statistical prowess, R functions as a full-fledged programming language, boasting features like loops, functions, and object-oriented programming. This dual functionality positions R as not only a statistical tool but also a platform for application development. Supported by a vibrant community and a vast repository of packages, R fosters collaboration and innovation, enabling users to tackle diverse data challenges effectively. Its seamless integration with other languages and tools enhances its versatility, while its emphasis on reproducibility ensures that analyses are transparent and easily replicable. In essence, R stands as an indispensable asset in the toolkit of data scientists, statisticians, and researchers, facilitating the extraction of insights and the advancement of knowledge from data.
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Workshop on Master LLMs: Build, Train and Deploy AI-Powered Chatbots
Welcome to our course on building AI-powered chatbots using Large Language Models (LLMs)! In today's digital landscape, chatbots are transforming how businesses interact with customers, providing instant support and enriching user experiences. This course is designed to equip you with the practical skills and hands-on experience needed to create intelligent chatbots that can adapt to various real-world scenarios.By the end of this course, you will be able to:• Understand the fundamentals of chatbots and Large Language Models (LLMs).• Collect, preprocess, and clean conversational data for chatbot training.• Fine-tune LLMs (e.g., GPT-2/3) to build AI-powered chatbots.• Implement intent recognition and entity extraction to build intelligent chatbots.• Develop multi-turn conversational bots that remember user interactions.• Integrate rule-based and AI-generated responses.• Test, evaluate, and deploy chatbots using free tools like Flask/FastAPI, Streamlit,and Heroku.• Build a fully functional chatbot for real-world applications, such as customerservice or virtual assistants.
