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Professional Diploma in Artificial Intelligence

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Professional Diploma in Artificial Intelligence

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About Course

The Professional Diploma in Artificial Intelligence, offered by French College, is a comprehensive six-month program designed to equip learners with the skills to master AI technologies and apply them to real-world challenges. Spanning three integrated levels—Fundamentals, Practical Applications, and Advanced Projects—this diploma blends theoretical knowledge with hands-on training to prepare individuals for careers as data analysts, AI developers, or technology researchers.

Program Overview

  • Duration: 4.5 to 6 months (270 training hours across 18 weeks).

  • Levels:

    • Level 1 – Beginner (2 weeks, 30 hours): Introduces AI fundamentals, machine learning basics, and ethical considerations, laying a strong foundation for beginners.

    • Level 2 – Intermediate (10 weeks, 150 hours): Focuses on Python programming, machine learning, deep learning, and practical applications using TensorFlow and PyTorch.

    • Level 3 – Advanced (6 weeks, 90 hours): Covers advanced techniques like reinforcement learning and generative AI, culminating in a supervised capstone project.

Key Objectives

  • Understand core AI and machine learning concepts.

  • Master Python programming and data analysis with tools like Pandas and NumPy.

  • Build and deploy machine learning and deep learning models.

  • Explore AI applications in sectors like healthcare and education.

  • Develop and implement real-world AI solutions, addressing ethical and deployment challenges.

Learning Experience

The program features live online instruction, interactive workshops, and hands-on projects, supported by expert supervision. Trainees progress from foundational skills to advanced applications, completing a capstone project that demonstrates practical expertise. Upon completion, graduates earn an accredited diploma, qualifying them as AI professionals ready to drive digital transformation.

Why Choose This Diploma?

Ideal for beginners and professionals alike, this course combines theoretical insights with practical skills, aligning with market demands. With a focus on innovation and real-world problem-solving, it prepares learners for a future where AI shapes industries and societies.

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Course Content

Week 1: Introduction to Artificial Intelligence
Overview of AI, its history, and its significance in the digital era. Differences between human intelligence and artificial intelligence. Distinguishing AI, machine learning, and deep learning. Practical AI applications in daily life and popular platforms such as Manus AI and ChatGPT. Total Hours: 15 hours (including 4 hours of interactive online workshops)

Week 2: Introduction to Machine Learning, Deep Learning, and AI Ethics
Fundamental principles and types of machine learning: Supervised Learning Unsupervised Learning Reinforcement Learning Deep learning and artificial neural networks: How they mimic the human brain Key applications AI Ethics, including: Data bias Privacy considerations Social responsibility Total Hours: 15 hours (including 4 hours of interactive online workshops)

Weeks 1–2: Python Fundamentals
Introduction and setup of the development environment Variables, operators, and strings Lists, dictionaries, and sets Conditional and iterative loops, functions Workshop: Develop your first Python script (e.g., a simple calculator)

Weeks 3–4: Basic Machine Learning Project
Apply machine learning concepts to a real dataset Build and test a simple classification or regression model Data cleaning and preparation Analyze results and evaluate performance ???? Example: Predicting customer churn for a telecommunications company

Week 5: Evaluating Machine Learning Models + Simple Neural Networks
Performance metrics: Accuracy, Precision, Recall Cross-validation, Confusion Matrix, ROC Curves Introduction to neural networks: Perceptron, Multi-layer Networks Backpropagation, Activation Functions ???? Workshop: Build a simple neural network (e.g., predicting diabetes)

Week 6: Convolutional Neural Networks (CNNs)
Fundamentals of computer vision Image classification and object detection Architectures: LeNet and AlexNet ???? Example: Classifying images and analyzing neural network accuracy

Week 7: Recurrent Neural Networks (RNNs) + Transfer Learning
LSTM and GRU networks Natural Language Processing (NLP): sentiment analysis, machine translation Using pre-trained models Fine-tuning & feature extraction ???? Workshop: Small NLP project (e.g., customer review classification)

Weeks 8–10: Practical Projects and Frameworks
Introduction to TensorFlow and PyTorch Implementation of advanced practical models Comprehensive project applying learned skills Handling big data challenges and performance optimization ???? Example: Building a personalized recommendation system for an e-commerce store

Week 1: Introduction to Large Language Models (LLMs) + GPT-3.5 Turbo
Exploring API interfaces Hands-on application: Analyzing customer feedback using GPT

Week 2: Using BERT and LLaMA2
Applying BERT to Arabic datasets Utilizing the LLaMA2 model Workshop: Comparing different models

Week 3: Advanced Applications
Explainable AI techniques (LIME, SHAP) Deploying models in production environments using Docker and Kubernetes Introduction to MLOps for managing the model lifecycle

Weeks 4–6: Capstone Project
Three full weeks dedicated to the final project Includes development, testing, presentation, and evaluation Direct supervision by an academic advisor

Duration: 6 weeks (90 hours)
Divided into 3 weeks of advanced learning + 3 weeks for the capstone project

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