Python Programming | Enablers

Now loading...

Python Programming

Sessions Venue
Online & On Campus Apply Now

Benefits to join Boot Camp

Comprehensive 3-Month Training Program

State-of-the-Art Lab Facilities

Practical Learning Approach

Dedicated Student Support

Career Advancement Opportunities

Business support for dedicated students

What you'll learn

The Python Programming Certification covers the fundamentals of Python, empowering students to build web applications using Python libraries and Django. Ideal for those with basic programming knowledge, it opens doors to high-paying roles and in-demand freelance opportunities.

At the end of this program, you will be able to:

  • Master Python from Scratch: Learn Python 2 and 3, building a strong foundation for both personal projects and professional tasks.
  • Create Engaging Projects: Build interactive games like Tic Tac Toe and Blackjack, and develop real-world applications using Python libraries.
  • Level Up with Advanced Features: Explore advanced Python topics, including object-oriented programming, decorators, and working with the collections module.
  • Build a Portfolio: Showcase your skills by creating a portfolio of Python-based projects you can proudly share.

Module Breakdown

Now…here’s something SUPER EXCITING that we have to share with you…

This is a complete overview of the actions you will take while building your business with the Enablers.

  • What is Python, and how is it used in modern applications?
  • How to set up Python using Anaconda, VS Code, and virtual environments.
  • How to write Python code with proper syntax, indentation, and comments.
  • What are variables and data types? (int, float, str, bool, list, tuple, dict, set)
  • How to perform type conversion and take user input/output.
  • How to use conditional statements (if, elif, else) to control program flow.
  • How to use loops (for, while, break, continue) to execute repetitive tasks.
  • What are list comprehensions, and how do they improve efficiency?
  • How to define and use functions (parameters, return values, lambda functions).
  • How scope works in Python (global, local, nonlocal) and when to use built-in functions.
  • What is Object-Oriented Programming (OOP), and how does it help in structuring code?
  • How to create and use classes, objects, methods, and attributes.
  • How Encapsulation, Inheritance, and Polymorphism work in Python.
  • How to use special (dunder) methods (__init__, __str__, etc.).
  • How to handle exceptions (try-except-finally) to prevent program crashes.
  • How to read and write files (TXT, CSV, JSON, Pickle) for data storage.
  • How to interact with databases using SQLite3 & PostgreSQL.
  • How to perform CRUD (Create, Read, Update, Delete) operations using SQLAlchemy.
  • How to fetch and process external data using APIs (Requests library).
  • What is Flask, and how is it used for web development?
  • How to set up a Flask application.
  • How to define and manage routes in Flask.
  • How to handle GET & POST requests for dynamic data exchange.
  • How to use Jinja2 templating to create dynamic web pages.
  • What are RESTful APIs, and how do they work?
  • How to create API endpoints and structure JSON responses.
  • How to extend Flask using Flask-RESTful and Flask-CORS.
  • How to secure APIs using authentication (JWT tokens).
  • What is FastAPI, and how does it compare to Flask?
  • How to set up a FastAPI application.
  • How to build API routes and validate data using Pydantic models.
  • How to handle errors and implement asynchronous programming in FastAPI.
  • How to connect Flask/FastAPI with databases.
  • How to use SQLAlchemy ORM for managing database models.
  • How to perform CRUD operations efficiently.
  • How to implement API security with OAuth2 and JWT authentication.
  • How to deploy APIs using Docker for scalability.
  • What is ML, and how does it fit into AI and Data Science?
  • How to set up an ML environment (NumPy, Pandas, Scikit-learn).
  • How to preprocess data (handle missing values, encode categorical data).
  • How to apply basic feature engineering techniques to improve model performance.
  • How to train an ML model (Logistic Regression, Random Forest).
  • How to evaluate model performance using accuracy, precision, and recall.
  • How to save and load ML models using joblib or pickle.
  • How to expose an ML model as an API using Flask.
  • How to deploy a trained ML model with FastAPI.
  • How to build an inference pipeline for serving predictions.
  • How to test API endpoints using Postman.
  • How to host FastAPI applications on cloud platforms (Render, AWS, or Azure).
  • How to develop a full AI-powered web application (Flask/FastAPI + ML).
  • How to implement CI/CD pipelines for automated deployment.
  • How to deploy applications using Docker, Heroku, or AWS.
  • How to finalize and present the project effectively.

MOMENTS TO BE REMEMBERED