Name: Full-Stack AI Engineer 2026: ML, Deep Learning, GenerativeAI
Availability: InStock

What You'll Learn

Master Python programming for AI, including data types, control flow, functions, and file handling to build strong foundations for machine learning.
Apply data science techniques using NumPy, Pandas, Matplotlib, and Seaborn to clean, visualize, and analyze datasets for actionable AI insights.
Build and evaluate machine learning models using Scikit-learn, covering regression, classification, ensemble methods, and model optimization.
Design and train deep learning models using TensorFlow and PyTorch, including CNNs, RNNs, and LSTMs for vision and sequence-based tasks.
Implement MLOps pipelines with Git, DVC, Docker, MLflow, and CI/CD to automate model deployment and management on AWS, GCP, and Azure.
Create Generative AI and LLM-based applications using OpenAI GPT, Claude, and Gemini APIs with RAG pipelines and custom fine-tuned models.

Requirements

No prior experience in AI or machine learning is required — this course starts from scratch and builds up to advanced, industry-ready concepts.
Basic computer literacy and a curiosity to learn Python programming will help you follow along and complete the hands-on coding exercises.
A laptop or desktop computer (Windows, macOS, or Linux) with at least 8GB of RAM and a stable internet connection for online tools and labs.
Access to Google Colab or a local Python setup (Anaconda or VS Code) is recommended for running Jupyter notebooks and training models.
Familiarity with high school-level math and statistics is helpful but not mandatory — all key concepts are explained from first principles.
A growth mindset, persistence, and passion for building real-world AI and Generative AI projects will ensure your success in this program.

Aspiring AI Engineers, Machine Learning Developers, and Data Scientists who want a complete, end-to-end learning path from Python to Generative AI.
Beginners in programming who want to break into Artificial Intelligence with a structured, guided roadmap of practical projects and real-world examples.
Software Engineers and Developers looking to upgrade their skills and transition into Machine Learning, Deep Learning, or AI Infrastructure roles.
Students, researchers, and tech enthusiasts eager to understand how modern AI systems like GPT, Claude, and Gemini are built and deployed.
Professionals in IT, analytics, or data-driven industries aiming to automate workflows using AI models, MLOps, and cloud deployment tools.
Anyone who wants to build and deploy AI applications — not just study them — and become a Full-Stack AI Engineer ready for enterprise-level challenges.

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