Master AI Foundations, Machine Learning, Deep Learning, LLMs, Agents, Deployment, and Responsible AI in 12 Weeks
Sub Category
- Data Science
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Objectives
- Understand the core concepts of Artificial Intelligence, including how AI systems work, where they are used, and how they create real-world impact.
- Build practical AI projects using Python, NumPy, Pandas, and common data processing techniques.
- Clean, analyze, visualize, and prepare data for Machine Learning and AI applications.
- Train, evaluate, and improve Machine Learning models for regression, classification, clustering, and predictive tasks.
- Understand the fundamentals of Deep Learning, neural networks, activation functions, backpropagation, and model training.
- Build beginner-friendly applications in Natural Language Processing, Computer Vision, and Large Language Models.
- Apply Prompt Engineering techniques to create better outputs from LLMs and AI assistants.
- Design and build basic AI Agents using tools, memory, vector databases, and automation workflows.
- Deploy AI applications using modern production concepts such as APIs, FastAPI, monitoring, logging, and optimization.
- Understand key principles of Responsible AI, including ethics, bias, governance, privacy, security, and risk management.
- Complete hands-on labs and a final portfolio-ready capstone project that demonstrates practical AI skills.
Pre Requisites
- No advanced AI or machine learning experience is required.
- Basic computer skills are helpful, such as installing software, using folders, and working with files.
- Basic programming knowledge is helpful, but the course starts with beginner-friendly Python concepts.
- A laptop or desktop computer is required to complete the hands-on labs and projects.
- Internet access is recommended for downloading tools, libraries, datasets, and course resources.
- Learners should be willing to practice coding, experiment with AI tools, and build projects step by step.
- Familiarity with basic math concepts such as averages, percentages, and simple graphs is helpful but not required.
- No prior experience with Machine Learning, Deep Learning, NLP, Computer Vision, LLMs, or AI Agents is required.
- The course is designed for beginners, professionals, students, developers, analysts, and anyone who wants to build practical AI skills.
- Most importantly, learners should come with curiosity, consistency, and a willingness to learn by building.
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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