Python & Applied AI Engineering Training Course in Ahmedabad

Module 1: Programming Fundamentals and Python Setup

• • Introduction to programming and the Python ecosystem.
• • Installing Python, VS Code, and Jupyter Notebook.
• • Running scripts and using the Python interpreter.
• • Variables, data types, operators, and type conversion.
• • User input, output, and formatted strings.
• • Basic debugging and reading error messages.
• • Practice: calculator, unit converter, and simple billing program.

Module 2: Python Logic and Data Structures

• • Conditional statements and nested conditions.
• • For loops, while loops, and iteration.
• • Lists, tuples, sets, and dictionaries.
• • String operations and common built-in functions.
• • List and dictionary comprehensions.
• • Functions, parameters, return values, and scope.
• • Mini-project: student result processing and report generation.

Module 3: Object-Oriented Programming and Python Modules

• • Classes, objects, attributes, and methods.
• • Constructors, inheritance, encapsulation, and polymorphism.
• • Modules, packages, imports, and virtual environments.
• • File handling and working with CSV, JSON, and text.
• • Exception handling and custom exceptions.
• • Iterators, generators, and decorators introduction.
• • Mini-project: object-oriented inventory or employee management system.

Module 4: Professional Python Development

• • Clean code, naming conventions, and reusable functions.
• • Type hints and documentation with docstrings.
• • Dependency management and environment variables.
• • Logging, debugging, and structured error handling.
• • Unit testing with pytest.
• • Git, GitHub, branches, commits, and pull requests.
• • Mini-project: package a tested Python utility and publish its source repository.

Module 5: SQL, Databases, and Python Integration

• • Relational database concepts and schema design.
• • SQL queries, filtering, joins, grouping, and aggregation.
• • Insert, update, delete, and transaction fundamentals.
• • Connecting Python to PostgreSQL.
• • CRUD operations and parameterized queries.
• • Data validation, constraints, and database security basics.
• • Mini-project: Python-based customer or product management application.

Module 6: Backend Development and REST APIs

• • HTTP, REST, requests, responses, and status codes.
• • Building APIs with FastAPI.
• • Routes, path parameters, query parameters, and request bodies.
• • Request validation using Pydantic.
• • Database integration and CRUD endpoints.
• • Authentication concepts and API documentation.
• • Mini-project: develop a task management or student management API.

Module 7: Data Analysis with NumPy and Pandas

• • Python numerical computing fundamentals.
• • NumPy arrays, indexing, and vectorized operations.
• • Pandas Series and DataFrames.
• • Reading CSV, Excel, and JSON datasets.
• • Cleaning missing, duplicate, and inconsistent data.
• • Grouping, filtering, merging, and transforming datasets.
• • Mini-project: analyze sales or customer data and generate a business report.

Module 8: Mathematics and Machine Learning Foundations

• • AI, machine learning, deep learning, and generative AI.
• • Features, labels, datasets, and model training.
• • Essential statistics, probability, vectors, and matrices.
• • Supervised versus unsupervised learning.
• • Training, validation, and test datasets.
• • Data leakage, overfitting, and underfitting.
• • Mini-project: explore a dataset and prepare it for model training.

Module 9: Practical Machine Learning with scikit-learn

• • Data preprocessing and feature engineering.
• • Linear and logistic regression.
• • Decision trees, random forests, and classification.
• • Clustering and introductory unsupervised learning.
• • Pipelines and reproducible preprocessing.
• • Evaluation metrics, cross-validation, and model comparison.
• • Mini-project: develop a basic prediction or classification model and document its limitations.

Module 10: Deep Learning and Pretrained Models

• • Neural network concepts and activation functions.
• • Loss functions, optimization, and gradient descent.
• • Introduction to PyTorch tensors and model training.
• • Training loop fundamentals and model evaluation.
• • Using pretrained models from Hugging Face.
• • Transfer learning concepts and inference pipelines.
• • Mini-project: use a pretrained text classification or image classification model.

Module 11: Generative AI and Large Language Models

• • How large language models work at a conceptual level.
• • Tokens, context windows, model parameters, and inference.
• • Prompt engineering and structured output generation.
• • Using hosted model APIs and supported local models.
• • Text generation, summarization, extraction, and classification.
• • Temperature, latency, cost, and output quality trade-offs.
• • Mini-project: build a structured text-processing assistant using Python.

Module 12: Embeddings, Vector Search, and RAG

• • Embeddings and semantic similarity.
• • Document ingestion, chunking, and metadata.
• • Vector search and retrieval fundamentals.
• • Retrieval-augmented generation (RAG) architecture.
• • Building a document question-answering pipeline.
• • Source citations, relevance evaluation, and hallucination reduction.
• • Mini-project: create a searchable knowledge assistant for a collection of documents.

Module 13: AI Testing, Evaluation, and Deployment

• • Unit and integration testing for AI applications.
• • Testing APIs, database operations, and model outputs.
• • Building repeatable evaluation datasets.
• • Measuring retrieval quality, response relevance, latency, and cost.
• • Secrets management and data privacy fundamentals.
• • Docker basics, application configuration, and deployment concepts.
• • Mini-project: containerize or prepare the AI application for deployment and document its setup.

Module 14: Portfolio and Career Readiness

• • Final project presentation and code review.
• • Python programming exercises and debugging practice.
• • SQL, REST API, and database interview preparation.
• • Machine learning and generative AI fundamentals revision.
• • GitHub portfolio, README, and project documentation.
• • Resume preparation and mock technical interviews.
• • Career planning for junior Python and applied AI roles.

Is Python difficult to learn for someone who has never programmed before?

Python is widely considered beginner-friendly because its syntax is relatively easy to read and understand. With structured training and regular practice, beginners can progress from basic programming concepts to building applications and working with AI technologies.

What is the difference between learning Python programming and Applied AI Engineering?

Python programming teaches you how to write code, work with data, develop applications and solve problems. Applied AI Engineering builds on those foundations to help you integrate AI models, use APIs, process data and develop practical AI-powered applications.

Will I learn to build AI applications instead of just studying theory?

The training should include hands-on projects such as AI chatbots, document analysis tools, intelligent assistants, data-processing applications and AI-powered automation. You will learn how to connect application components, test results and build solutions for practical use cases.

Can I use Python and AI tools to develop projects even if I am a beginner?

Yes. AI coding assistants can help explain code, suggest solutions and identify potential errors while you learn. However, you should also practise writing and debugging code independently so you can understand, test and maintain the applications you build.

What should I learn first: Python, Machine Learning or Generative AI?

For most beginners, the recommended order is Python fundamentals, programming logic, data structures, libraries and APIs, followed by applied AI development. You can then progress to machine learning, generative AI, LLM applications and more advanced AI engineering topics.