Master Python, Machine Learning, DL, MLOps, and Gen AI through hands-on projects to become a Full-Stack AI Engineer
β±οΈ Length: 32.2 total hours
π₯ 14 students
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- Course Overview
- Embark on a transformative journey to become a proficient Full-Stack AI Engineer, mastering the end-to-end lifecycle of AI development from foundational programming to cutting-edge generative models.
- This intensive, hands-on program is meticulously crafted to bridge the gap between theoretical AI concepts and practical, deployable solutions, preparing you for the dynamic demands of the AI industry.
- With a focus on practical application, you will evolve from writing your first lines of Python code to architecting and deploying sophisticated AI systems.
- The curriculum emphasizes building a robust understanding of how different AI components integrate, enabling you to conceptualize, build, and manage AI projects holistically.
- You will gain the confidence to tackle complex data challenges, develop intelligent algorithms, and leverage the power of large language models to create innovative applications.
- The course is designed to foster a deep appreciation for the engineering aspects of AI, ensuring your solutions are not just functional but also scalable, reliable, and maintainable.
- By the end of this program, you will be equipped with a comprehensive skill set and a portfolio of projects that demonstrate your readiness for advanced AI roles.
- Requirements / Prerequisites
- A solid understanding of fundamental programming concepts, ideally with prior exposure to Python.
- Familiarity with basic mathematical principles, particularly linear algebra and calculus, will be beneficial but not strictly mandatory, as foundational concepts will be reinforced.
- An inquisitive mind and a passion for problem-solving are paramount.
- Access to a reliable internet connection and a computer capable of running development environments.
- Willingness to engage actively in coding exercises, project work, and collaborative learning.
- Skills Covered / Tools Used
- Core Programming & Data Handling: Python (advanced constructs), NumPy, Pandas, Matplotlib, Seaborn.
- Machine Learning Fundamentals: Scikit-learn (supervised and unsupervised algorithms, hyperparameter tuning, cross-validation), feature engineering.
- Deep Learning Architectures: TensorFlow, PyTorch (convolutional neural networks, recurrent neural networks, transformer models).
- MLOps & Deployment: Git (version control), DVC (data version control), Docker (containerization), MLflow (experiment tracking and model registry), CI/CD pipelines.
- Cloud Platforms: Hands-on experience with AWS, GCP, or Azure for AI model deployment and infrastructure management.
- Generative AI & LLMs: OpenAI API (GPT series), Claude API, Gemini API, Retrieval Augmented Generation (RAG), fine-tuning techniques.
- Software Engineering Practices: Best practices for writing clean, modular, and efficient code.
- Problem-Solving & Algorithm Design: Developing effective solutions for real-world AI challenges.
- Benefits / Outcomes
- Become a highly sought-after Full-Stack AI Engineer capable of managing the entire AI project lifecycle.
- Develop a robust portfolio showcasing practical expertise in Python, ML, DL, MLOps, and GenAI.
- Gain the ability to translate business problems into AI solutions and implement them from concept to production.
- Acquire the skills to build, train, evaluate, and deploy machine learning and deep learning models efficiently.
- Master the art of leveraging large language models and generative AI to create innovative and impactful applications.
- Understand and implement best practices for model management, versioning, and continuous integration/deployment.
- Be prepared to contribute effectively to cutting-edge AI projects in various industries.
- Enhance your career prospects with in-demand skills that are shaping the future of technology.
- PROS
- Comprehensive Curriculum: Covers the entire AI engineering spectrum, from foundational programming to advanced generative AI.
- Project-Centric Learning: Emphasizes hands-on application, building a practical portfolio.
- Industry-Relevant Tools: Utilizes popular and essential tools and platforms in the AI landscape.
- Full-Stack Capability: Equips learners with skills to handle AI projects end-to-end.
- Future-Proof Skills: Focus on GenAI and LLMs ensures relevance in the evolving AI market.
- CONS
- Intensive Pace: The breadth of topics may require significant dedication and time commitment from students.
Learning Tracks: English,Development,Data Science
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