Python AI & Machine Learning

What you'll learn

• Understand the fundamentals of Artificial Intelligence and Machine Learning
• Differentiate between rule-based programming and learning-based systems
• Train AI models using real data and examples
• Use cloud-based AI tools (Google Teachable Machine)
• Build machine learning models using Scikit-learn
• Understand concepts like training data, prediction, and accuracy
• Work with datasets and visualize results
• Build a real AI application using text classification

This course includes:

• 2 AI Projects (Cloud + Python ML)
• 6 Hours Live Classes (4 Sessions)
• Online / Onsite (Physical)
• Practice Datasets & Code Files
• Mini Project (Emoji Mood Predictor)
• Certificate of Completion

Course Content

Session 1 — What is AI & Machine Learning?

Duration: 90 Minutes

Topics Covered:
• Introduction to Artificial Intelligence
• Rule-Based vs Learning-Based Systems
• Key Concepts: Training Data, Predictions, Confidence
• Real-World Applications of AI
• Understanding AI through Analogies

Key Learning Objectives:
• Understand how AI systems learn from examples
• Recognize AI applications in daily life
• Build foundational knowledge of machine learning
• Develop critical thinking about AI

Activities:
• AI Hunt Activity (identify AI in daily life)
• Examples: recommendations, face unlock, spam filters
• Analogy discussion (teaching a baby vs coding rules)
• “Is this AI?” quiz game

Session 2 — Google Teachable Machine (Cloud AI)

Duration: 90 Minutes

Topics Covered:
• Introduction to Cloud-Based AI Tools
• Using Google Teachable Machine
• Understanding Classes and Training Samples
• Training Image Classification Models
• Confidence Scores and Model Accuracy

Key Learning Objectives:
• Train AI models without coding
• Understand classification systems
• Improve model accuracy with better data
• Experiment with real-time predictions

Activities:
• Train 3-class model (thumbs up/down/open hand)
• Capture 30+ images per class
• Test live predictions
• Challenge: classify school bag items
• Discussion on data quality and accuracy

Session 3 — Scikit-learn: Python Machine Learning

Duration: 90 Minutes

Topics Covered:
• Installing Scikit-learn
• Machine Learning Workflow
(Load Data → Train → Predict → Evaluate)
• Decision Tree Classifier
• Working with Datasets (Iris Dataset)
• Model Accuracy and Evaluation
• Visualizing Decision Trees

Key Learning Objectives:
• Understand ML workflow
• Train models using Python
• Evaluate model performance
• Interpret results visually

Activities:
• Load Iris dataset
• Explore data using pandas
• Split into training/testing sets
• Train Decision Tree model
• Print accuracy score
• Visualize tree using plot_tree()

Session 4 — Emoji Mood Predictor (Mini AI Project)

Duration: 90 Minutes

Topics Covered:
• Text Classification using Machine Learning
• Naive Bayes Classifier
• Training AI with Text Data
• Predicting Sentiment/Mood
• Building Complete AI Application

Key Learning Objectives:
• Apply AI to text-based problems
• Build end-to-end ML applications
• Understand limitations of AI models
• Improve model predictions

Activities:
• Create dataset of labeled sentences
• Train model (happy, sad, angry, excited)
• Input new sentence for prediction
• Display matching emoji
• Test with creative sentences
• Analyze incorrect predictions

Practice Projects for Real-World Skills

• AI Recognition Activity
• Image Classifier (Teachable Machine)
• Decision Tree Model (Iris Dataset)
• Final Project: Emoji Mood Predictor

Requirements

• Completion of Python Intermediate Level
• Basic understanding of Python and logic
• Laptop/PC with internet access
• Curiosity about AI and machine learning

Description

This advanced module introduces students to Artificial Intelligence and Machine Learning through hands-on tools and real-world examples. Students will explore both no-code AI platforms and Python-based machine learning using Scikit-learn.

From training image classifiers to building a text-based AI application, learners will gain practical experience in how intelligent systems are built and evaluated.

Why Choose This Course?

• Beginner-Friendly Introduction to AI
• Hands-On Machine Learning Projects
• Cloud + Python-Based Learning
• Real-World Applications
• Strong Foundation for Future AI Studies

Activities During Class

• Identifying AI in real life
• Training image classification models
• Building machine learning models
• Testing and improving predictions
• Creating AI-powered applications

Who Is This Course For?

• Students who completed Python Intermediate Level
• Learners interested in AI and data science
• School students exploring future tech
• Beginners in machine learning

Course Highlights

• AI + Machine Learning Fundamentals
• Real Projects with Live Data
• Cloud AI + Python Integration
• Interactive Learning Experience
• Certificate of Completion

Enroll Today!

Step into the future of technology with Artificial Intelligence and Machine Learning. Learn how machines think, learn, and make decisions—and build your own AI-powered applications.