Python 3 Course: Artificial Intelligence

COURSE DESCRIPTION

This advanced Python Level 3 course at ETC Lebanon is focused on Artificial Intelligence system development. Building on your previous knowledge of Python and object-oriented programming, you will explore real-world applications of AI using libraries such as Scikit-learn, TensorFlow, Keras, NLTK, and OpenCV. The course includes hands-on projects in machine learning, natural language processing (NLP), and neural networks, equipping you to build intelligent systems such as recommendation engines, chatbots, and image classifiers. You will also learn how to prepare datasets, train and evaluate models, and deploy your AI solutions as standalone applications. This course is ideal for learners ready to move from theory to building and deploying complete AI projects in Python.

LEARNING OBJECTIVES

What's covered in this course:

1. AI in the Real World

• 1.1 What is Artificial Intelligence?
• 1.2 Real-world AI use cases in healthcare, finance, and daily life
• 1.3 The role of Python in AI development
• 1.4 Overview of projects covered in the course

2. Data Handling for AI

• 2.1 Working with CSV, JSON, and API-based datasets
• 2.2 Data manipulation using Pandas
• 2.3 Numerical computation with NumPy
• 2.4 Mini Project: Analyze global COVID-19 trends

3. Data Visualization

• 3.1 Introduction to Matplotlib and Seaborn
• 3.2 Visualizations: line, bar, scatter, heatmap
• 3.3 Styling and customizing plots
• 3.4 Mini Project: Build a COVID-19 dashboard

4. Introduction to Machine Learning

• 4.1 What is Machine Learning?
• 4.2 Supervised vs. Unsupervised Learning
• 4.3 Building your first ML model with scikit-learn
• 4.4 Project: Predict exam scores from study time

5. Classification Models

• 5.1 k-Nearest Neighbors (k-NN)
• 5.2 Logistic Regression
• 5.3 Decision Trees
• 5.4 Model evaluation: accuracy, precision, recall
• 5.5 Confusion matrix interpretation
• 5.6 Project: Spam or Not Spam email classifier

6. Regression Models

• 6.1 Simple Linear Regression
• 6.2 Multiple Linear Regression
• 6.3 Polynomial Regression
• 6.4 Project: Predict real estate prices

7. Clustering & Dimensionality Reduction

• 7.1 k-Means Clustering
• 7.2 DBSCAN algorithm
• 7.3 Dimensionality reduction using PCA
• 7.4 Project: Customer segmentation model for marketing

8. Natural Language Processing (NLP)

• 8.1 Tokenization, stemming, and stop word removal
• 8.2 Text preprocessing with NLTK and spaCy
• 8.3 Project: Twitter sentiment analyzer

9. Neural Networks

• 9.1 Understanding how neural networks work
• 9.2 Creating neural networks with TensorFlow/Keras
• 9.3 Project: Handwritten digit recognition using MNIST

10. AI in Computer Vision

• 10.1 Introduction to image processing with OpenCV
• 10.2 Using CNNs for image classification
• 10.3 Project: Real-time face mask detector

11. Building an AI Chatbot

• 11.1 Rule-based vs. ML-powered chatbots
• 11.2 Integrating NLP for chatbot interaction
• 11.3 Project: Customer support chatbot with memory

12. Deploying AI Projects

• 12.1 Saving and loading models using Pickle
• 12.2 Creating interactive web interfaces with Streamlit
• 12.3 Project: Deploy a trained model (e.g., chatbot or spam filter)

13. Capstone Project

• 13.1 Brainstorming and selecting a project idea
• 13.2 Step-by-step project planning
• 13.3 Final Project Ideas:
  • AI-based recommendation system
  • Disease prediction tool
  • Facial emotion recognition app
  • Custom AI project of student's choice

AUDIENCE

This course is ideal for Python programmers who have completed Python Level 1 and Level 2 or have equivalent experience, and who want to specialize in Artificial Intelligence and machine learning projects.

PREREQUISITES

Completion of Python 1 - Fundamentals and Python 2 - Data Science, OOP, and UI courses, or equivalent programming experience with Python.