Python Certification Program with AI

This program will focus on building foundational Python skills while introducing core AI concepts and applications. Each month covers different aspects of Python, gradually introducing AI topics like machine learning and data handling.

Program Overview:

• Duration: Upto 6 months.
• Mode: Instructor led live classes with assessments and hands-on projects.
• Prerequisites: None (suitable for beginners).
• Goal: To equip learners with foundational Python skills and introduce basic AI concepts, leading to a Python and AI certification From SkillSet Arena.
  

Learning Outcomes:

By the end of this program, learners will:

• Understand Python basics, including syntax, data structures, and object-oriented programming.
• Learn how to work with data using Python libraries.
• Gain foundational knowledge of AI, machine learning, and their applications.
• Be able to write Python programs to solve problems and build simple AI models.

Program Structure:

Month 1: Introduction to Python & Programming Basics:

Module 1:

• What is Python? Installing Python (IDLE, Anaconda).
• Understanding IDEs (Jupyter Notebook, VS Code).
• Python syntax, variables, data types (int, float, str, bool).

Module 2:

• Control structures: If statements, loops (for, while).
• Functions: Defining, arguments, return values.

Module 3:

• Basic input/output.
• Error handling and debugging.

Module 4:

• Project: Build a basic calculator using Python.
• Assessment: Quiz + Assignment (Calculator program).

Month 2: Data Structures and Algorithms in Python

Module 1:

• Lists, tuples, sets, dictionaries: Operations and use cases.
• List comprehensions and lambda functions.
  

Module 2:

• String manipulation and regular expressions.
  

Module 3:

• Basic algorithms: Sorting, searching, recursion.
  

Module 4:

• Project: Implement a simple sorting algorithm and search through a dataset.
• Assessment: Quiz + Assignment (Data manipulation program)

Month 3: Object-Oriented Programming (OOP):

Module 1:

• Introduction to OOP: Classes and objects.
• Attributes, methods, and constructors.
  

Module 2

• Inheritance, polymorphism, encapsulation.
  

Module 3:

• Advanced OOP concepts: Magic methods, method overriding.
  

Module 4:

• Project: Build a class-based system (e.g., a banking system or a school management system).
• Assessment: Quiz + Project (OOP-based project)

Month 4: Introduction to Data Science with Python

Module 1:

• Libraries: NumPy and Pandas for data manipulation.
• Data cleaning, filtering, sorting, and aggregation.
  

Module 2:

• Data visualization with Matplotlib and Seaborn.
• Reading and writing files: CSV, Excel.

Module 3:

• Introduction to Jupiter Notebook for interactive coding.
• Exploratory data analysis (EDA) on a dataset.

Module 4:

• Project: Perform EDA on a public dataset (e.g., COVID-19 data, weather data).
• Assessment: Quiz + EDA Project
  

Month 5: Introduction to AI and Machine Learning:

Module 1:

• What is AI? AI vs. Machine Learning vs. Deep Learning.
• Introduction to supervised learning: Regression, classification using Scikit-learn for ML.
  

Module 2:

• Model training, testing, and evaluation (accuracy, precision, recall).
• Building a linear regression model.

Module 3:

• Unsupervised learning: Clustering and dimensionality reduction.
• Introduction to K-means clustering.

Module 4:

• Project: Build a basic machine learning model (classification or regression).
• Assessment: Quiz + Machine Learning Project
  

Month 6: AI Applications and Final Project:

Module 1:

• Introduction to neural networks and deep learning.
• Introduction to TensorFlow and Keras (basics only).
  

Module 2:

• AI applications: Computer vision, NLP, speech recognition.
• Hands-on: Build a simple neural network using Keras.

Module 3:

• AI ethics and considerations.
• Future of AI and job opportunities.

Module 4:

• Capstone Project: Build a simple AI application (e.g., sentiment analysis, digit recognition).
• Assessment: Final Exam + Capstone Project.
  

Key Features:

• Instructor Led Live Learning: Access to pre-recorded video lessons, reading materials, and exercises.
• Hands-on Projects: Practical projects in every module, applying Python and AI skills.
• Regular Assessments: Weekly quizzes, assignments, and monthly projects.
• Capstone Project: A comprehensive AI-based project to apply everything learned.
• Certification: SkillSet Arena issued upon successful completion of the course and final project.
  

Tools & Technologies:

• Python Libraries: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow/Keras.
• Tools: Jupyter Notebook, VS Code, Anaconda.
• Datasets: Public datasets (Kaggle, UCI Machine Learning Repository).

Support Resources:

• Discussion Forum: A place for students to ask questions and share ideas.
• Mentor Support: Optional mentor support for personalized guidance.

Certificate of Completion:

• Earn a certificate upon completing the course and final project from SkillSet Arena.

This program is designed to be beginner-friendly but also provide enough depth to give students a solid understanding of Python and AI. With hands-on projects and regular assessments, learners will have the opportunity to apply their skills in real-world scenarios.