Python for AI – Complete Beginner-to-Deployment Course

Learn Python programming, data analysis, machine learning, deep learning, generative AI, AI agents, and deployment through a structured, project-focused syllabus. Designed for learners who want to build practical AI applications step by step.

Course highlights

• 12 structured learning modules, from Python foundations to deploying an AI application.
• Practical projects and real-world examples throughout the syllabus.
• Learn data preparation, machine learning models, NLP, LLM concepts, RAG, and AI workflows.
• Final capstone project covering application deployment and presentation.

Full Course Syllabus

1. Python Foundations I: Installation and setup, variables, data types, operators, input/output, conditional statements. Project: Number-guessing game.
2. Python Foundations II: Loops, functions, lists, tuples, dictionaries, sets, and error handling. Project: To-do list manager.
3. Python for Data: File handling, CSV and JSON, virtual environments, Git, and NumPy fundamentals. Project: Sales-data summary.
4. Data Analysis: Pandas, data cleaning, exploratory data analysis (EDA), Matplotlib, and Seaborn. Project: Exploratory analysis report.
5. Math for AI: Statistics, probability, distributions, correlation, and feature scaling. Project: Dataset insight dashboard.
6. Machine Learning I: Supervised learning, regression, train/test splits, and model evaluation metrics. Project: House-price predictor.
7. Machine Learning II: Decision trees, random forests, K-nearest neighbors (KNN), support vector machines (SVM), cross-validation, and hyperparameter tuning. Project: Customer-churn classifier.
8. Unsupervised Learning: K-means, hierarchical clustering, PCA, and anomaly detection. Project: Customer segmentation.
9. Deep Learning & NLP: Neural networks, activation functions, backpropagation, PyTorch introduction, tokenization, and embeddings. Project: Sentiment classifier.
10. Generative AI: Large language models (LLMs), prompt engineering, API usage, embeddings, vector databases, and retrieval-augmented generation (RAG). Project: Document Q&A chatbot.
11. AI Agents & Applications: Tool use, function calling, memory, workflows, and safety considerations. Project: Task-automation agent.
12. Deployment & Capstone: Flask or FastAPI, Docker fundamentals, testing, logging, monitoring, and project presentation. Project: Deploy an AI application.

Who should enroll? Beginners, students, aspiring Python developers, and learners interested in data science and AI. Basic programming experience is helpful but not required.

Price: ₹150 (one-time course price).

Note: Course access, lessons, and any completion certificate are subject to the features provided on the course website. This product description does not promise a certificate unless one is explicitly included.