DCT Artificial Intelligence Associate Course

Course Description

This associate-level workshop provides a comprehensive foundation in Artificial Intelligence (AI) — combining theory, practical lab work, and real-world applications. Participants will learn how AI systems are built, trained, and deployed using both open-source and cloud tools. By the end of the course, learners will be able to design small-scale AI solutions, evaluate models, and understand AI’s ethical, business, and operational impacts.

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Learning Outcomes

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

• Explain the fundamental principles, history, and categories of Artificial Intelligence.

• Prepare and process data for AI and ML applications.

• Implement machine learning and deep learning models for basic use cases.

• Understand how generative AI and large language models (LLMs) function.

• Deploy and manage AI models in both on-premise and cloud environments.

• Identify ethical and responsible AI considerations.

• Integrate AI into IT and business processes.

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Target Audience

• IT Professionals, System Administrators, and Developers transitioning into AI

• Database Administrators and Data Engineers expanding into data-driven applications

• Cloud professionals seeking to integrate AI services into workflows

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Course Outline

Module 1: Introduction to Artificial Intelligence and Data Foundations

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AI Overview and Evolution

• What is AI? Historical context and modern developments

• Key domains: Machine Learning, Deep Learning, Generative AI, Expert Systems

• AI use cases across industries (healthcare, finance, IT operations, etc.)

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Understanding AI Ecosystems

• AI frameworks: TensorFlow, PyTorch, Scikit-learn, Hugging Face

• Open-source vs. cloud AI platforms (AWS, Azure, GCP)

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Data and the AI Lifecycle

• Role of data in AI projects

• Types of data (structured, unstructured, semi-structured)

• Basics of data engineering and data pipelines

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AI Project Lifecycle

• Defining the problem and use case selection

• Data collection and cleaning

• Model selection, training, testing, and deployment

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Hands-on Labs

• Setting up Python and Jupyter Notebook environment

• Exploring datasets using Pandas and NumPy

• Simple classification using a pre-trained model

 

Module 2: Machine Learning and Model Evaluation

Core Machine Learning Concepts

• Supervised, Unsupervised, and Reinforcement Learning

• Algorithms overview: Linear Regression, Decision Trees, KNN, Clustering

• Model training and validation workflow

Feature Engineering and Data Preparation

• Data normalization and encoding

• Handling missing and imbalanced data

• Feature selection techniques

Model Evaluation

• Confusion matrix, ROC curve, Precision/Recall/F1-score

• Avoiding overfitting and underfitting

Model Optimization

• Hyperparameter tuning

• Cross-validation and grid search

Hands-on Labs

• Building a supervised ML model from scratch

• Comparing algorithm performance

• Visualizing results using Matplotlib and Seaborn

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Module 3: Deep Learning, Generative AI, and Neural Networks

Deep Learning Fundamentals

• Artificial Neural Networks (ANN) structure

• Convolutional and Recurrent Neural Networks (CNNs and RNNs)

• Transfer learning and pre-trained models

Generative AI

• What is Generative AI?

• Large Language Models (LLMs): GPT, BERT, Claude, Gemini

• Prompt engineering basics

• Use cases: text generation, summarization, image synthesis

Applied AI

• Computer vision, NLP, and speech recognition basics

• AI in automation (AIOps, Chatbots, Intelligent Assistants)

Hands-on Labs

• Training a neural network on image data

• Text classification and summarization using pre-trained NLP models

• Experimenting with generative AI APIs (optional if cloud resources available)

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Module 4: AI Deployment, Responsible AI, and Cloud Integration

AI Deployment and MLOps

• Model packaging and versioning

• REST APIs for inference

• Introduction to MLOps and CI/CD pipelines for AI

Edge and Cloud AI

• Edge AI overview (TinyML, IoT inference)

• Cloud deployment using AWS SageMaker, Azure ML, or Vertex AI

• Cost and scaling considerations

Ethics, Governance, and Responsible AI

• Bias, fairness, and transparency

• Explainable AI (XAI)

• AI regulation and governance frameworks

Career and Industry Pathways

• AI job roles (AI Analyst, ML Engineer, Data Scientist Assistant)

• Continuing education and certifications roadmap

Hands-on Labs

• Deploying an ML model as an API endpoint

• Model explainability exercise (SHAP/LIME demo)

• Group mini-project: design an AI-based solution for a business challenge

 

Module 5 (Capstone Project)

Capstone Project

• End-to-end AI workflow:

1. Define a use case

2. Collect and preprocess data

3. Train and evaluate model

4. Deploy locally or via a lightweight cloud environment

• Group presentations and feedback session

Assessment and Certification

• Knowledge Check: Short quizzes and discussions at the end of each module

• Practical Evaluation: Hands-on lab submissions or in-class demos

• Capstone Project: Team-based AI solution (optional for shorter version)

 

Tools & Platforms used

• Languages: Python

• Frameworks: TensorFlow, Scikit-learn, PyTorch, Keras

• Libraries: Pandas, NumPy, Matplotlib, Seaborn

• Cloud Options (optional): Azure AI Studio, AWS SageMaker, or Google Vertex AI

• IDE: JupyterLab / VS Code