{"id":4603,"date":"2026-06-13T17:50:24","date_gmt":"2026-06-13T10:50:24","guid":{"rendered":"https:\/\/class-jo.com\/?post_type=courses&#038;p=4603"},"modified":"2026-06-13T14:53:54","modified_gmt":"2026-06-13T14:53:54","slug":"introduction-to-ai","status":"publish","type":"courses","link":"https:\/\/class-jo.com\/ar\/courses\/introduction-to-ai\/","title":{"rendered":"Introduction to AI"},"content":{"rendered":"<p><a href=\"https:\/\/class-jo.com\/wp-content\/uploads\/2026\/06\/course-3.pdf\">course (3)<\/a><\/p>\n<p>Introduction to AI Course outline\u00a0<\/p>\n<p><strong>Module 1: Environment Setup &amp; Data Analysis<\/strong><br \/><em>Preparing the AI development environment and understanding data fundamentals<\/em><\/p>\n<ul>\n<li>\n<p>Setting up Python development environments (Anaconda &amp; Virtual Environments)<\/p>\n<\/li>\n<li>\n<p>Advanced Python review for AI applications<\/p>\n<\/li>\n<li>\n<p>Working with Lists, Dictionaries, Functions, and Lambda Expressions<\/p>\n<\/li>\n<li>\n<p>Understanding data formats (CSV, JSON, Parquet)<\/p>\n<\/li>\n<li>\n<p>Data collection and preparation techniques<\/p>\n<\/li>\n<li>\n<p>Feature engineering and label preparation<\/p>\n<\/li>\n<li>\n<p>Handling missing values and data cleaning<\/p>\n<\/li>\n<li>\n<p>Statistical analysis fundamentals<\/p>\n<\/li>\n<li>\n<p>Data visualization and pattern discovery<\/p>\n<\/li>\n<li>\n<p>Exploratory Data Analysis (EDA)<\/p>\n<\/li>\n<li>\n<p>Introduction to data-driven decision making<\/p>\n<\/li>\n<\/ul>\n<p><strong>Technologies:<\/strong> Pandas, NumPy, Matplotlib, Seaborn<\/p>\n<hr \/>\n<p><strong>Module 2: Machine Learning Fundamentals<\/strong><br \/><em>Building predictive and intelligent decision-making systems<\/em><\/p>\n<ul>\n<li>\n<p>Introduction to Machine Learning concepts<\/p>\n<\/li>\n<li>\n<p>Supervised Learning fundamentals<\/p>\n<\/li>\n<li>\n<p>Classification techniques and applications<\/p>\n<\/li>\n<li>\n<p>Regression techniques and applications<\/p>\n<\/li>\n<li>\n<p>Unsupervised Learning concepts<\/p>\n<\/li>\n<li>\n<p>Clustering and segmentation methods<\/p>\n<\/li>\n<li>\n<p>Dimensionality reduction techniques<\/p>\n<\/li>\n<li>\n<p>Reinforcement Learning fundamentals<\/p>\n<\/li>\n<li>\n<p>Agents, environments, rewards, and policies<\/p>\n<\/li>\n<li>\n<p>Model evaluation and performance metrics<\/p>\n<\/li>\n<li>\n<p>Overfitting and underfitting prevention techniques<\/p>\n<\/li>\n<li>\n<p>Model deployment fundamentals<\/p>\n<\/li>\n<\/ul>\n<p><strong>Technologies:<\/strong> Scikit-Learn, SciPy, Joblib, OpenAI Gym<\/p>\n<hr \/>\n<p><strong>Module 3: Deep Learning<\/strong><br \/><em>Building intelligent systems using neural networks<\/em><\/p>\n<ul>\n<li>\n<p>Introduction to Artificial Neural Networks<\/p>\n<\/li>\n<li>\n<p>Neural Network architecture and components<\/p>\n<\/li>\n<li>\n<p>Activation functions and optimization techniques<\/p>\n<\/li>\n<li>\n<p>Forward and Backpropagation processes<\/p>\n<\/li>\n<li>\n<p>Understanding tensors and tensor operations<\/p>\n<\/li>\n<li>\n<p>PyTorch fundamentals<\/p>\n<\/li>\n<li>\n<p>Loss functions and optimization algorithms<\/p>\n<\/li>\n<li>\n<p>Convolutional Neural Networks (CNN)<\/p>\n<\/li>\n<li>\n<p>Recurrent Neural Networks (RNN)<\/p>\n<\/li>\n<li>\n<p>Deep Learning model training and evaluation<\/p>\n<\/li>\n<li>\n<p>GPU acceleration concepts<\/p>\n<\/li>\n<\/ul>\n<p><strong>Technologies:<\/strong> PyTorch, CUDA, TensorBoard<\/p>\n<hr \/>\n<p><strong>Module 4: Natural Language Processing (NLP)<\/strong><br \/><em>Enabling computers to understand and process human language<\/em><\/p>\n<ul>\n<li>\n<p>Introduction to Natural Language Processing<\/p>\n<\/li>\n<li>\n<p>Text preprocessing and cleaning techniques<\/p>\n<\/li>\n<li>\n<p>Arabic and English language processing<\/p>\n<\/li>\n<li>\n<p>Tokenization and text normalization<\/p>\n<\/li>\n<li>\n<p>Word Embeddings and vector representations<\/p>\n<\/li>\n<li>\n<p>Transformer architecture fundamentals<\/p>\n<\/li>\n<li>\n<p>Sentiment Analysis systems<\/p>\n<\/li>\n<li>\n<p>Question Answering systems<\/p>\n<\/li>\n<li>\n<p>Text classification techniques<\/p>\n<\/li>\n<li>\n<p>Introduction to Large Language Models (LLMs)<\/p>\n<\/li>\n<li>\n<p>Working with Arabic language models<\/p>\n<\/li>\n<\/ul>\n<p><strong>Technologies:<\/strong> Hugging Face Transformers, NLTK, AraBERT<\/p>\n<hr \/>\n<p><strong>Module 5: Computer Vision<\/strong><br \/><em>Teaching computers to interpret and understand visual information<\/em><\/p>\n<ul>\n<li>\n<p>Introduction to Computer Vision<\/p>\n<\/li>\n<li>\n<p>Digital image processing fundamentals<\/p>\n<\/li>\n<li>\n<p>Working with images using OpenCV<\/p>\n<\/li>\n<li>\n<p>Convolutional Neural Networks for image analysis<\/p>\n<\/li>\n<li>\n<p>Transfer Learning techniques<\/p>\n<\/li>\n<li>\n<p>Using pre-trained computer vision models<\/p>\n<\/li>\n<li>\n<p>Object detection fundamentals<\/p>\n<\/li>\n<li>\n<p>Real-time object detection systems<\/p>\n<\/li>\n<li>\n<p>Image augmentation techniques<\/p>\n<\/li>\n<li>\n<p>Image classification applications<\/p>\n<\/li>\n<li>\n<p>Modern Computer Vision workflows<\/p>\n<\/li>\n<\/ul>\n<p><strong>Technologies:<\/strong> OpenCV, Pillow, Torchvision, YOLO<\/p>\n<hr \/>\n<p><strong>Module 6: Speech AI<\/strong><br \/><em>Developing intelligent systems for speech understanding and recognition<\/em><\/p>\n<ul>\n<li>\n<p>Introduction to Speech Processing<\/p>\n<\/li>\n<li>\n<p>Audio signal fundamentals<\/p>\n<\/li>\n<li>\n<p>Sound wave representation and spectrograms<\/p>\n<\/li>\n<li>\n<p>Feature extraction techniques (MFCC)<\/p>\n<\/li>\n<li>\n<p>Speech-to-Text systems<\/p>\n<\/li>\n<li>\n<p>Automatic Speech Recognition (ASR)<\/p>\n<\/li>\n<li>\n<p>Working with Whisper models<\/p>\n<\/li>\n<li>\n<p>Advanced speech processing using Wav2Vec2<\/p>\n<\/li>\n<li>\n<p>Audio analysis and interpretation<\/p>\n<\/li>\n<li>\n<p>Speech AI applications and use cases<\/p>\n<\/li>\n<\/ul>\n<p><strong>Technologies:<\/strong> Librosa, Torchaudio, Whisper, SoundFile<\/p>\n<hr \/>\n<p><strong>Module 7: AI Project Development &amp; Deployment<\/strong><br \/><em>Applying AI concepts through real-world practical projects<\/em><\/p>\n<ul>\n<li>\n<p>AI project planning and development lifecycle<\/p>\n<\/li>\n<li>\n<p>Building Reinforcement Learning agents<\/p>\n<\/li>\n<li>\n<p>Gesture-based control systems<\/p>\n<\/li>\n<li>\n<p>Computer Vision applications<\/p>\n<\/li>\n<li>\n<p>Virtual Calculator development<\/p>\n<\/li>\n<li>\n<p>AI Virtual Keyboard implementation<\/p>\n<\/li>\n<li>\n<p>Vehicle License Plate Recognition systems<\/p>\n<\/li>\n<li>\n<p>Image Steganography applications<\/p>\n<\/li>\n<li>\n<p>AI Virtual Mouse development<\/p>\n<\/li>\n<li>\n<p>People Detection, Tracking, and Counting<\/p>\n<\/li>\n<li>\n<p>Speech Recognition projects<\/p>\n<\/li>\n<li>\n<p>Multi-task NLP systems<\/p>\n<\/li>\n<li>\n<p>Text Summarization applications<\/p>\n<\/li>\n<li>\n<p>Model integration and deployment considerations<\/p>\n<\/li>\n<li>\n<p>Final Capstone Project<\/p>\n<\/li>\n<\/ul>\n<p><strong>Technologies:<\/strong> OpenCV, MediaPipe, YOLO, Transformers, Librosa, Gymnasium<\/p>","protected":false},"excerpt":{"rendered":"<p>course (3) Introduction to AI Course outline\u00a0 Module 1: Environment Setup &amp; Data AnalysisPreparing the AI development environment and understanding 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