Introduction to AI

Total Hours: 48 hours total (6 hours per week)
Total Duration: 2 Months Totlal Sessions: 16
2 Sessions per week
Session Duration: 3 Hours

What is the Course?

This course is designed to introduce beginners to the world of Artificial Intelligence. It starts with a strong foundation in Python programming and gradually advances into key AI concepts, machine learning fundamentals, and practical applications. Divided into two months, the first focuses on Python for AI while the second explores core AI and ML topics.

Course Objectives

  • Build a solid foundation in Python programming and data handling
  • Introduce the essential concepts and techniques of Artificial Intelligence
  • Develop practical skills in machine learning through hands-on exercises
  • Provide insights into real-world applications and AI-driven decision making
  • Prepare students for further study and entry-level roles in AI and data science

What Will You Learn?

  • Python fundamentals including syntax, data types, control structures, and data structures
  • Working with essential libraries such as NumPy, Pandas, and Matplotlib
  • Techniques for data visualization and basic machine learning model building
  • Introduction to TensorFlow, NLP, and computer vision concepts
  • Foundations of AI including supervised and unsupervised learning models

What Will the Course Prepare You For?

  • Entry-level roles in AI, data science, and machine learning
  • Advanced studies and certifications in AI and related fields
  • Practical experience to work on real-world AI projects
  • Understanding how to apply AI techniques to solve business challenges

Course Curriculum

Month 1: Python for AI (8 Sessions)
Total Sessions: 8 | Sessions per Week: 2 | Duration: 4 Weeks

Week 1

Session 1: Python Environment & Basic Syntax

  • Installing Python, IDEs, Virtual Environments
  • Python Syntax (variables, data types, operators)
  • Basic Input/Output and String Manipulation

Session 2: Control Structures & Data Structures

  • Conditional Statements (if/elif/else)
  • Loops (for, while)
  • Built-in Data Structures (lists, tuples, dictionaries, sets)

Week 2

Session 3: Working with Libraries (NumPy & Pandas)

  • NumPy Arrays, Basic Math Operations
  • Pandas for Data Loading, Cleaning, and Manipulation
  • Practical Exercises with Real Datasets

Session 4: Data Visualization in Python

  • Introduction to Matplotlib & Seaborn
  • Creating Line, Bar, Scatter, and Histogram Plots
  • Hands-On Visualization Exercises

Week 3

Session 5: Advanced Python Concepts

  • Functions, Lambda Expressions
  • Object-Oriented Programming (Classes, Objects)
  • Error Handling and Modules

Session 6: Project Environment Setup & Version Control

  • Git & GitHub Basics
  • Virtual Environments and Requirements Files
  • Structuring a Python Project for Collaboration

Week 4

Session 7: Mini-Project (Data Analysis)

  • End-to-End Data Analysis (Cleaning, Exploration, Visualization)
  • Applying Pandas & Visualization Libraries
  • Presentation of Findings

Session 8: Python Review & Next Steps

  • Review of Key Python Concepts
  • Q&A on Python Best Practices
  • Preview of AI Topics Coming in Month 2

Month 2: Introduction to AI (8 Sessions)
Total Sessions: 8 | Sessions per Week: 2 | Duration: 4 Weeks

Week 5

Session 9: AI Overview & Machine Learning Fundamentals

  • Definitions: AI, Machine Learning, Deep Learning
  • Supervised vs. Unsupervised Learning
  • Real-World Applications (Healthcare, Education, etc.)
  • Building a Simple ML Model (e.g., Linear Regression) in Python

Session 10: Predictive Modeling & scikit-learn

  • Data Preprocessing & Feature Engineering
  • Training, Validation, and Evaluation Metrics
  • Hands-On Exercise: Classification or Regression Task

Week 6

Session 11: Data Analysis & Introduction to TensorFlow

  • Revisiting Data Analysis Workflows for ML
  • Setting Up TensorFlow; Tensors & Computational Graphs
  • Hands-On: Simple Neural Network in TensorFlow

Session 12: Introduction to NLP (Natural Language Processing)

  • Text Preprocessing (Tokenization, Stopwords, Stemming/Lemmatization)
  • Basic NLP Tools (NLTK, SpaCy)
  • Building a Simple Text Classification or Sentiment Analysis Model

Week 7

Session 13: Introduction to Computer Vision

  • What is Computer Vision? (Image Processing, Object Detection)
  • OpenCV Basics (Loading, Displaying, Basic Transformations)
  • Hands-On: Simple Image Classification (High-Level TensorFlow/Keras Example)

Session 14: Deep Learning Essentials

  • Neural Networks vs. Traditional Machine Learning
  • Convolutional Neural Networks (CNN) Overview
  • Hands-On: Building/Exploring a Basic CNN Model

Week 8

Session 15: AI Specializations & Career Paths

  • Differences Between Main AI Fields
  • Deep Learning: advanced neural networks, large datasets
    Data Science: data wrangling, exploratory analysis, big data
    Computer Vision: image/video processing, object detection
    NLP: text processing, language models, chatbots
  • Skills & Resources Needed for Each Track
  • Industry Use Cases & Opportunities

Session 16: Final Project & Wrap-Up

  • Students Choose a Mini-Project Based on Their Interests (CV, NLP, or General ML)
  • Presentation & Feedback Session
  • Q&A, Future Learning Paths (Competitions, Certifications, Advanced Courses)
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