
AI, ML, DL, Data Science explained Real-world applications Career roadmap

Basics: variables, loops, functions Data structures, file handling Libraries: NumPy, Pandas, Matplotlib

SQL queries, joins, aggregations Power BI dashboards & data viz

Data cleaning, preprocessing, feature selection ML algorithms: Regression, Trees, SVM EDA with Pandas & Seaborn

Neural Networks & CNNs Object Detection with YOLO OpenCV projects

Tokenization, embeddings, sentiment analysis Transformers, BERT, GPT Chatbot building

Diffusion models, Stable Diffusion, DALL·E LangChain & LlamaIndex AI Agents & AutoGPT

Model deployment (FastAPI, Docker) CI/CD pipelines Hosting on GCP / Hugging Face
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