Introduction to AI

course (3)

Introduction to AI Course outline 

Module 1: Environment Setup & Data Analysis
Preparing the AI development environment and understanding data fundamentals

• Setting up Python development environments (Anaconda & Virtual Environments)

• Advanced Python review for AI applications

• Working with Lists, Dictionaries, Functions, and Lambda Expressions

• Understanding data formats (CSV, JSON, Parquet)

• Data collection and preparation techniques

• Feature engineering and label preparation

• Handling missing values and data cleaning

• Statistical analysis fundamentals

• Data visualization and pattern discovery

• Exploratory Data Analysis (EDA)

• Introduction to data-driven decision making

Technologies: Pandas, NumPy, Matplotlib, Seaborn

Module 2: Machine Learning Fundamentals
Building predictive and intelligent decision-making systems

• Introduction to Machine Learning concepts

• Supervised Learning fundamentals

• Classification techniques and applications

• Regression techniques and applications

• Unsupervised Learning concepts

• Clustering and segmentation methods

• Dimensionality reduction techniques

• Reinforcement Learning fundamentals

• Agents, environments, rewards, and policies

• Model evaluation and performance metrics

• Overfitting and underfitting prevention techniques

• Model deployment fundamentals

Technologies: Scikit-Learn, SciPy, Joblib, OpenAI Gym

Module 3: Deep Learning
Building intelligent systems using neural networks

• Introduction to Artificial Neural Networks

• Neural Network architecture and components

• Activation functions and optimization techniques

• Forward and Backpropagation processes

• Understanding tensors and tensor operations

• PyTorch fundamentals

• Loss functions and optimization algorithms

• Convolutional Neural Networks (CNN)

• Recurrent Neural Networks (RNN)

• Deep Learning model training and evaluation

• GPU acceleration concepts

Technologies: PyTorch, CUDA, TensorBoard

Module 4: Natural Language Processing (NLP)
Enabling computers to understand and process human language

• Introduction to Natural Language Processing

• Text preprocessing and cleaning techniques

• Arabic and English language processing

• Tokenization and text normalization

• Word Embeddings and vector representations

• Transformer architecture fundamentals

• Sentiment Analysis systems

• Question Answering systems

• Text classification techniques

• Introduction to Large Language Models (LLMs)

• Working with Arabic language models

Technologies: Hugging Face Transformers, NLTK, AraBERT

Module 5: Computer Vision
Teaching computers to interpret and understand visual information

• Introduction to Computer Vision

• Digital image processing fundamentals

• Working with images using OpenCV

• Convolutional Neural Networks for image analysis

• Transfer Learning techniques

• Using pre-trained computer vision models

• Object detection fundamentals

• Real-time object detection systems

• Image augmentation techniques

• Image classification applications

• Modern Computer Vision workflows

Technologies: OpenCV, Pillow, Torchvision, YOLO

Module 6: Speech AI
Developing intelligent systems for speech understanding and recognition

• Introduction to Speech Processing

• Audio signal fundamentals

• Sound wave representation and spectrograms

• Feature extraction techniques (MFCC)

• Speech-to-Text systems

• Automatic Speech Recognition (ASR)

• Working with Whisper models

• Advanced speech processing using Wav2Vec2

• Audio analysis and interpretation

• Speech AI applications and use cases

Technologies: Librosa, Torchaudio, Whisper, SoundFile

Module 7: AI Project Development & Deployment
Applying AI concepts through real-world practical projects

• AI project planning and development lifecycle

• Building Reinforcement Learning agents

• Gesture-based control systems

• Computer Vision applications

• Virtual Calculator development

• AI Virtual Keyboard implementation

• Vehicle License Plate Recognition systems

• Image Steganography applications

• AI Virtual Mouse development

• People Detection, Tracking, and Counting

• Speech Recognition projects

• Multi-task NLP systems

• Text Summarization applications

• Model integration and deployment considerations

• Final Capstone Project

Technologies: OpenCV, MediaPipe, YOLO, Transformers, Librosa, Gymnasium