We are excited to announce the launch of our AI & Machine Learning Bootcamp. This intensive, hands-on program is designed to help technical professionals break into the world of applied AI.
The first course in this series is titled Applied AI: From Deep Learning Fundamentals to Agentic Systems, an 8-week journey into building real-world AI capabilities.
This Applied AI course is tailored for industry professionals with a tech background but no direct AI experience. It spans 8 weeks, with three 1.5-hour sessions per week, totaling 36 hours of learning. Each session combines theory and hands-on practice, using tools like Python, PyTorch, and Hugging Face libraries, ensuring you can apply AI concepts in real-world scenarios.
The course covers these topics:
What to Expect in Each Session
Each session starts with a 45-minute theoretical lecture, covering concepts like neural network basics or LLM capabilities, followed by a 45-minute hands-on activity, such as building a simple CNN or fine-tuning a pre-trained model. Expect guided coding exercises, with materials like slides and notebooks provided, and instructor support for questions.
Unexpected Detail
You’ll also explore how Agentic AI can integrate with external tools, like web search, to create more autonomous systems, which is a cutting-edge application not typically covered in beginner AI courses.
The course is structured over 8 weeks, with a total of 24 sessions, each 1.5 hours long, equating to 36 hours of learning. This duration was chosen to thoroughly cover the broad topics listed—NLP, CV, GANs & Diffusion, and Agentic AI—while allowing for a smooth introduction and progression to advanced concepts. The decision for 8 weeks, rather than the minimum 6, ensures sufficient time for hands-on projects and deep dives, given the participants’ lack of direct AI experience but strong tech background.
The Course Learning Outcomes (CLOs) are designed to ensure participants achieve a robust understanding and practical skill set:
These outcomes ensure participants can integrate AI into their work, driving innovation and efficiency.
The course is divided into thematic weeks, each with three sessions, balancing theory and hands-on practice. Below is a detailed breakdown, including content, hands-on activities, and expectations for each session.
This week ensures a smooth entry, starting with basics and introducing essential tools.
Given the breadth of NLP, it is split into two weeks for comprehensive coverage.
Week 2 (NLP Part 1):
Week 3 (NLP Part 2):
Similar to NLP, CV is split for depth, covering basics and advanced topics.
Week 4 (CV Part 1):
Week 5 (CV Part 2):
This ensures participants can design and adapt CV models, with practical to solidify learning.
This week focuses on creating new content, covering both GANs and Diffusion Models.
This week introduces cutting-edge generative techniques, with hands-on creation of new data.
Given the user’s clarification, Agentic AI focuses on 2024-2025 trends, using tools and LLMs to create AI agents and assistants.
This week aligns with current trends, ensuring participants can create autonomous systems, a key area in AI development.
This final week addresses ethical considerations and consolidates learning.
This ensures participants reflect on AI’s societal impact and connect learning to their work.
Hands-On and Theoretical Balance.
Each session is split into approximately 45 minutes of theory and 45 minutes of hands-on practice, ensuring a balanced approach. Hands-on activities include coding exercises like building neural networks, fine-tuning models, and creating agents, with materials provided (slides, notebooks, datasets). This structure supports the course’s theoretical + hands-on nature, catering to the participants’ need for practical skills.
Tools and Prerequisites.
The course uses Python, PyTorch for deep learning, and Hugging Face for NLP and generative tasks, with additional libraries for Agentic AI (e.g., LangChain for agent frameworks). Prerequisites include familiarity with Python and basic tech concepts, but no prior AI experience, aligning with the target audience’s profile.
Additional Considerations
This detailed design ensures a comprehensive, engaging, and practical AI learning experience, ready for implementation in professional settings.