AI Engineer Program

Course Description

Intensive training in artificial intelligence covering the fundamentals of machine learning and deep learning, neural networks (CNN, RNN), generative AI, as well as deploying and optimizing AI models in production with tools such as TensorFlow, Keras, and PyTorch.

Learning Objectives

By the end of this course, participants will be able to:

• Master the fundamentals of artificial intelligence, including supervised, unsupervised, and deep learning.
• Build, train, and deploy AI models using modern frameworks and tools.
• Implement optimized AI solutions for concrete use cases across various industries.
• Understand ethical considerations, biases, and regulations in AI.
• Integrate AI pipelines into production systems while ensuring scalability.

Who Is This Course For?

This course is intended for a wide audience, including:

• Developers and software engineers: seeking specialized skills in developing and implementing AI solutions.
• Evolving data scientists: looking to deepen their knowledge in advanced AI and integrate AI pipelines into production environments.
• Cloud and infrastructure architects: wanting to understand AI implications to design optimized, scalable architectures.
• Students and AI researchers: aiming to turn theoretical knowledge into concrete, impactful applications.
• IT professionals transitioning careers: aspiring to enter the high-demand field of artificial intelligence to boost their careers.

Prerequisites

No specific prerequisites are required.

Course Syllabus

Module 1: Fundamentals of AI and Machine Learning (Days 1–4)

• Goal: Understand the basics and principles of learning algorithms.
• Content:
  Key AI concepts and how they differ from Data Science.
  Types of learning: supervised, unsupervised, semi-supervised, and reinforcement.
  Introduction to Python for AI.
  Data manipulation with libraries such as NumPy and Pandas.

Module 2: Supervised and Unsupervised Learning Models (Days 5–7)

• Goal: Develop high-performing models using a variety of algorithms.
• Content:
  Linear and logistic regression.
  Decision trees, random forests, and boosting.
  Clustering (K-Means, DBSCAN) and dimensionality reduction (PCA).
  Model evaluation and validation (metrics and cross-validation).

Module 3: Introduction to Deep Learning (Days 8–10)

• Goal: Understand the basics of neural networks and build deep models.
• Content:
  How neural networks work and backpropagation.
  Popular frameworks: TensorFlow and PyTorch.
  Convolutional Neural Networks (CNNs) for image analysis.
  Recurrent Neural Networks (RNNs) for time series and Natural Language Processing (NLP).

Module 4: Model Deployment and MLOps (Days 11–13)

• Goal: Automate and manage the lifecycle of AI models in production.
• Content:
  Production environments for AI.
  Introduction to MLOps and automated pipelines.
  Integration with tools such as Docker, Kubernetes, and CI/CD.
  Monitoring and maintenance of deployed models.

Module 5: Hands-on Projects (Days 14–16)

• Goal: Apply acquired knowledge to real-world problems.
• Content:
  Case study 1: Demand forecasting.
  Case study 2: Image classification.
  Case study 3: Sentiment analysis with NLP.

Module 6: Ethics, Regulation, and the Future of AI (Days 17–18)

• Goal: Explore the social and regulatory aspects of AI.
• Content:
  Bias and discrimination in AI models.
  Local and international AI regulations.
  Emerging trends: Generative AI, ethical AI, green AI.
  Final project presentations and feedback.

Training Highlights

• Modular, hands-on approach: Alternating theory and practice for better concept retention.
• Cloud integration: Strong focus on cloud and distributed solutions.
• Qualified instructors: Trainers with real-world, hands-on experience.
• Tools and learning resources: Access to online resources, live demos, and real case studies.
• Accessibility: Open to all, no advanced technical prerequisites.
• Practical application: A complete project at the end of the modules to consolidate learning.
• Industry readiness: Focus on certifications and standard tools used in the workplace.

Teaching Methods and Tools

• Live demonstrations with data science services.
• Hands-on workshops and real case studies across sectors (industry, retail, healthcare).
• Lessons learned: Sharing best practices and common pitfalls in companies.
• Simulations and tools: Use of simulators for interactive workshops.

Assessment

• End-of-course quiz to test understanding of the concepts covered.
• Practical case studies or group discussions to apply acquired knowledge.
• Continuous assessment during practical sessions.
• Practical application: A complete project at the end of the modules to consolidate learning.

Standards & References

• Well-Architected Cloud Framework.
• GDPR (General Data Protection Regulation).
• ISO 27001, SOC 2 (Service Organization Control).
• NIST Cybersecurity Framework.