[100% Off] Full-Stack Ai Engineer: Python, Ml, Deep Learning &Amp; Genai
Master Python, Machine Learning, DL, MLOps, and Gen AI through hands-on projects to become a Full-Stack AI Engineer
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.
Description
This course contains the use of artificial intelligence(AI).
Welcome to Full-Stack AI Engineer: Python, ML, Deep Learning & GenAI, the ultimate end-to-end program designed to turn you into a production-ready Artificial Intelligence Engineer. In this comprehensive AI course, you will master every layer of the AI engineering pipeline, from Python programming and data science foundations to machine learning, deep learning, MLOps, and Generative AI with Large Language Models (LLMs).
This course is your complete roadmap to becoming a Full-Stack AI Engineer, capable of designing, building, training, deploying, and scaling AI models across real-world environments. You’ll gain hands-on experience through real projects using NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Docker, Git, MLflow, LangChain, and FastAPI, ensuring you learn the same AI tools used by leading tech companies.
You’ll begin your journey by learning Python for Data Science, mastering control flow, functions, data structures, and file handling. Next, you’ll dive into data analysis and data visualization with Matplotlib, Seaborn, and Pandas, developing a strong foundation in data cleaning, feature engineering, and statistical modeling. These essential data skills will empower you to manipulate large datasets and prepare them for machine learning workflows.
The next phase of the course focuses on Machine Learning (ML). You’ll explore supervised learning, unsupervised learning, classification, regression, ensemble methods, and model evaluation techniques. You’ll implement algorithms such as linear regression, logistic regression, decision trees, random forests, XGBoost, LightGBM, and CatBoost. Each topic is reinforced with hands-on ML projects that help you apply theory in real scenarios.
After mastering ML, you’ll advance to Deep Learning (DL) — building and training neural networks using TensorFlow and PyTorch. You’ll understand forward propagation, backpropagation, activation functions, loss functions, and gradient descent optimization. You’ll construct Convolutional Neural Networks (CNNs) for image classification and Recurrent Neural Networks (RNNs), LSTMs, and GRUs for sequence modeling. By the end of this module, you’ll have built and deployed multiple deep learning models on real datasets.
Next, you’ll step into the world of MLOps (Machine Learning Operations) — the essential skill for deploying and managing AI systems in production. You’ll learn version control with Git and DVC, model packaging with ONNX and TorchScript, API serving using Flask and FastAPI, and cloud deployment on AWS, GCP, and Azure. You’ll automate model pipelines using CI/CD tools, ensuring that your models are reliable, scalable, and ready for enterprise use.
Finally, you’ll dive into Generative AI (GenAI) and Large Language Models (LLMs). You’ll master prompt engineering, tokenization, fine-tuning, retrieval-augmented generation (RAG), and AI agent frameworks like LangChain and CrewAI. You’ll build real LLM applications using OpenAI GPT, Claude, and Gemini APIs, culminating in a capstone project where you develop your own AI chatbot or content generator.
By the end of this course, you’ll have the full technical stack to become a Full-Stack AI Engineer — a professional who understands data science, machine learning, deep learning, MLOps, and Generative AI end-to-end. Whether you’re starting your AI career or scaling into advanced engineering roles, this course equips you with the skills, tools, and portfolio to build the future of Artificial Intelligence.
Author(s): School of AI
