{"id":13485,"date":"2026-08-10T10:59:56","date_gmt":"2026-08-10T10:59:56","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/udemy-full-stack-ai-engineer-python-ml-deep-learning-gen-ai-2025-10\/"},"modified":"2026-08-10T10:59:56","modified_gmt":"2026-08-10T10:59:56","slug":"udemy-full-stack-ai-engineer-python-ml-deep-learning-gen-ai-2025-10","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/udemy-full-stack-ai-engineer-python-ml-deep-learning-gen-ai-2025-10\/","title":{"rendered":"Udemy \u2013 Full-Stack AI Engineer: Python, ML, Deep Learning & Gen AI 2025-10"},"content":{"rendered":"
\n

Descriptions<\/h2>\n

Full-Stack AI Engineer: Python, ML, Deep Learning & Gen AI, 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\u2019ll 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\u2019ll begin your journey by learning Python for Data Science, mastering control flow, functions, data structures, and file handling. Next, you\u2019ll dive into data analysis and data visualization with Matplotlib, Seaborn, and Pandas, developing a strong foundation in data cleaning, feature engineering, and statistical modeling. The next phase of the course focuses on Machine Learning (ML). You\u2019ll explore supervised learning, unsupervised learning, classification, regression, ensemble methods, and model evaluation techniques. You\u2019ll 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\u2019ll advance to Deep Learning (DL) \u2014 building and training neural networks using TensorFlow and PyTorch. You\u2019ll understand forward propagation, backpropagation, activation functions, loss functions, and gradient descent optimization.<\/p>\n

What you\u2019ll learn<\/h3>\n