Artificial Intelligence

Introduction to AI and Machine Learning

What is AI?

  • AI is the ability of machines to mimic intelligent human behavior.

  • It includes tasks like image classification, language understanding, planning, robotics, etc.

Classic Algorithm vs. AI Algorithm

  • Classic: Explicit step-by-step instructions.

  • AI: Learns patterns from data (e.g., image recognition).

5 Big Ideas of AI 

  • Perception, Representation & Reasoning, Learning, Natural Interaction, Societal Impact.

Machine Learning Concepts

What is Machine Learning?

  • A subfield of AI focused on algorithms that improve with experience/data.

Machine Learning Workflow:

  1. Get Data – from sensors, files, databases.

  2. Analyze Data – preprocessing, wrangling, feature engineering.

  3. Develop Model – train with algorithms (classification, regression).

  4. Deploy Model – monitor performance in real-world applications.

Types of Machine Learning:

  • Supervised Learning – with labeled data (e.g., classification, regression).

  • Unsupervised Learning – with unlabeled data (e.g., clustering, PCA).

Algorithms Covered:

  • Supervised: Logistic Regression, SVM, KNN, Naive Bayes, Decision Trees, Random Forest.

  • Unsupervised: K-Means, Hierarchical, PCA, Anomaly Detection.

Model Evaluation:

  • Accuracy, Confusion Matrix, Precision, Recall, ROC & AUC.

Deep Learning & Convolutional Neural Networks (CNN)

Neural Networks Overview

  • Learns from data rather than explicit programming.

  • Inspired by the biological neuron.

Key Concepts:

  • Perceptron: Input → Weights → Sum → Activation Function

  • Activation Functions: ReLU, Sigmoid, Tanh, Softmax

  • Loss Functions: MSE, Cross-Entropy, Hinge Loss

  • Training Process: Forward Propagation + Backpropagation

CNN Components:

  • Convolutional Layer – feature extraction with kernels

  • Padding & Stride – control feature map size

  • Pooling Layer – reduces spatial size (Max, Avg pooling)

  • Fully Connected Layer – final classification

  • Popular CNN Architectures: AlexNet, VGG, GoogleNet, ResNet

Improving CNN Performance:

  • Data Augmentation – rotating, flipping, zooming, etc.

  • Tuning – learning rate, batch size, regularization (dropout, early stopping)

  • Grid Search, Ensemble Models



What is IDE?

An IDE (Integrated Development Environment) is a software that helps you write, test, and fix code easily — all in one place.

Simple Explanation:

Think of an IDE like a kitchen for cooking code. Just like a kitchen has all the tools you need (stove, fridge, knife), an IDE has all the tools you need for coding:

  • A text editor to write your code (like a knife to cut ingredients)

  • A compiler or interpreter to run your code (like a stove to cook)

  • A debugger to find and fix errors (like a taste test to see if it's good)

Simple Example:

Imagine you're learning Python. If you use Pyzo IDE:

  • You type your Python code (e.g., print("Hello, world!"))

  • Click a button to run it

  • See the output immediately on the screen

Without an IDE, you'd have to use separate tools: one to write, another to run, and another to find errors.


Introduction to Pyzo IDE and Python Installation

What is Pyzo?

Pyzo is a free and lightweight Integrated Development Environment (IDE) for Python programming. It is designed to be simple and beginner-friendly, making it ideal for students, hobbyists, and educators. Pyzo uses an editor + shell interface, where users can write Python code on the left pane and view outputs or errors on the right pane in real-time.

Key features:

  • Simple interface, ideal for teaching and learning

  • Supports syntax highlighting and auto-indentation

  • No need for complex configurations

  • Runs on Windows, MacOS, and Linux

Why Pyzo?

  • Beginner-friendly: You don’t need to manage multiple configurations or extensions.

  • Portable: It can work with any installed Python interpreter.

  • Free & Open Source: Great for schools, students, and educators.

Steps to Install Pyzo IDE

1. Download Pyzo Installer

  1. Open your browser and go to the official website: https://www.pyzo.org

  2. Click the Download button.

  3. Choose the installer for your operating system. (e.g., Windows Installer for .exe)

  4. The installer will begin downloading and usually be saved in your Downloads folder.

2. Locate the Installer

  • Open File Explorer (Windows) or Finder (Mac).

  • Go to the Downloads folder.

  • Look for a file like: pyzo-<version>.exe (e.g., pyzo-2024a-win64.exe)

3. Run the Installer

  • Double-click the .exe file.

  • If prompted by User Account Control (UAC), click Yes.

  • Follow the installation steps:

    • Click Next

    • Accept License Agreement

    • Choose installation location (default is fine)

    • Click Install

    • Finally, click Finish

4. Launch Pyzo

  • Find the Pyzo shortcut on your desktop or in the Start Menu.

  • Open Pyzo. The interface will show:

    • Left side: Code editor

    • Right side: Shell/Console



CNN Architecture

A simple CNN model is built using Keras' Sequential API. The model consists of multiple convolutional layers for feature extraction, pooling layers to reduce dimensionality, and dense layers for classification. The architecture is designed to be beginner-friendly but effective enough for recognizing patterns in image data.

Compilation and Training

The model is compiled with the Adam optimizer, sparse categorical crossentropy as the loss function, and accuracy as the evaluation metric. Training is performed over several epochs using the training set, and validation accuracy is monitored using the test set.

Evaluation and Visualization

After training, the model's performance is evaluated on the test data. The tutorial also demonstrates how to plot training and validation accuracy over epochs, allowing learners to visually assess model performance and detect signs of overfitting or underfitting.




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