What You'll Learn
- Build and deploy 100 practical AI and ML projects from scratch
- Understand core concepts in NLP, computer vision, and agents
- Use libraries like PyTorch, TensorFlow, HuggingFace, and LangChain
- Create AI apps with Streamlit, FastAPI, and Gradio
- Fine-tune LLMs and build RAG and agentic systems locally
- Apply AI in real-world domains: health, finance, education, etc.
- Integrate speech, image, and text models into full-stack apps
- Evaluate and test LLMs for safety, alignment, and accuracy
- Use tools like ChromaDB, Ollama, and LangGraph offline
- Develop ethical, aligned, and human-centered AI systems
Requirements
- A basic understanding of Python programming (variables, functions, loops)
- Familiarity with using Jupyter Notebooks or any Python IDE
- An interest in exploring how AI works through real-world projects
- A computer with at least 8GB of RAM and a stable internet connection
- (Optional) Basic knowledge of machine learning or data science concepts
- No prior AI/ML experience is required. This course will guide you from foundational topics to advanced projects step by step.
Who This Course is For
- Aspiring AI engineers who want to build real-world projects and gain hands-on experience
- Developers and software engineers looking to transition into AI and machine learning
- Students and self-learners who want a structured, project-based way to master AI
- Startup founders and tech entrepreneurs seeking to prototype AI-powered applications
- Educators and tech mentors who want ready-to-use projects to teach AI in practical settings
- Anyone curious about AI and motivated to learn by building instead of just reading theory
Your Instructor
Gourav J. Shah
Premium Instructor | 200,000+ Students | DevOps/AIOps/MLOps
4.4 Instructor Rating
5,018 Reviews
202,458 Students
27 Courses
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