AI Foundations and Development is a practical training program designed to provide participants with a strong understanding of artificial intelligence, machine learning, intelligent systems, and AI development. The course combines fundamental concepts with practical development principles to help learners understand how AI systems work, how they process data, and how intelligent solutions are designed and implemented.
AI Duration 5 days – 35 hrs
Overview
The AI Foundations and Development Training is a hands-on 5-day program designed for professionals who understand the basic concepts of Artificial Intelligence (AI) but want to develop a deeper understanding of how AI systems are built, trained, evaluated, and deployed. The course bridges the gap between AI concepts and practical development, giving participants a clear introduction to the technologies and processes behind intelligent applications.
Participants will explore the relationship between Artificial Intelligence, Machine Learning, and Deep Learning, while learning how data, algorithms, models, and development tools work together to create intelligent systems. The training introduces the AI development lifecycle, from identifying a problem and collecting and preparing data to selecting algorithms, training models, evaluating results, and deploying AI solutions.
A practical focus is placed on Python for AI development, allowing participants to work with commonly used tools and frameworks such as Jupyter Notebook, Scikit-learn, and TensorFlow. Through guided exercises, learners will gain hands-on experience with data preprocessing, supervised and unsupervised learning, regression, classification, and the development of basic machine learning models.
The course also introduces the backend functionality of AI applications, helping participants understand how trained models can be integrated into simple applications and services. Learners will explore basic deployment approaches using tools such as Flask or Streamlit, providing practical insight into how AI models can move from development environments into usable applications.
Beyond technical development, the training emphasizes model evaluation, responsible AI, data quality, and ethical considerations. Participants will learn why accuracy, reliability, bias, privacy, and responsible use are important when developing and deploying intelligent systems.
Through hands-on activities, demonstrations, and a practical group project, participants will apply their knowledge to a real-world AI use case. By the end of the AI Foundations and Development Training, learners will have a clearer understanding of how AI works and the practical skills needed to begin developing, testing, and deploying simple intelligent systems using modern AI development tools.
Learning Objectives
- Understand Microsoft Copilot’s core features, AI capabilities, and integration with Office apps.
- Create and refine documents, reports, and summaries in Word using AI prompts.
- Analyze datasets, create charts, and automate tasks in Excel with natural language.
- Draft and manage emails, schedules, and follow-ups effectively in Outlook.
- Build dynamic presentations and improve visual storytelling in PowerPoint.
- Leverage Copilot in Teams for meeting notes, action points, and enhanced collaboration.
- Apply AI productivity principles in different roles such as HR, Sales, and Operations.
- Practice AI prompt engineering and evaluate content for accuracy and relevance.
Audience
- Developers and IT professionals with limited AI development experience
- Technical team members looking to build AI capabilities from the ground up
- Business professionals with a general understanding of AI concepts seeking deeper technical skills
Prerequisites
- General understanding of AI and IT systems
- Basic programming knowledge (preferably Python) is helpful but not required
- No prior experience with AI development necessary
Course Content
Foundations of Artificial Intelligence
- Introduction to AI, ML, and Deep Learning
- Key AI applications across industries
- Understanding data’s role in AI
- Overview of AI development workflow
- Ethical AI and responsible development
AI Development Concepts and Tools
- Introduction to Python for AI
- Data collection and preprocessing
- Supervised vs. Unsupervised Learning
- Introduction to machine learning algorithms (Regression, Classification)
- Tools and frameworks: Jupyter Notebook, Scikit-learn, TensorFlow basics
Building and Testing AI Models
- Hands-on: building a basic AI model (e.g., prediction or classification)
- Training and evaluating models
- Understanding AI backend architecture
- Deploying a simple model using Flask or Streamlit
- Group activity: ideating an AI use case in your organization
