GenAI Essentials for Applied AI Solution Architects

• Scope:
  Introduce the fundamental definitions, historical evolution, and importance of AI, ML, and DL.
• Objective:
  Familiarize you with what AI is, how ML and DL fit into the broader AI landscape, and why these technologies are critical for transforming the future of work.
• Audience:
  Beginners with no prior background in AI.
• What You’ll Learn:
• Definitions and key differences between AI, ML, and DL.
• A brief history of AI developments from symbolic AI to deep learning breakthroughs.
• How these technologies are applied in modern business and everyday life.

• Scope:
  Introduce essential AI/ML/DL terms such as datasets, features, training, models, loss functions, and evaluation metrics.
• Objective:
  Establish a solid vocabulary and conceptual framework for understanding advanced AI topics.
• Audience:
  Beginners seeking to build foundational knowledge in AI terminology.
• What You’ll Learn:
• Definitions and significance of core terms used in AI and ML.
• The role of data, features, training/testing, and evaluation in model development.
• An overview of loss functions and optimization methods.

• Scope:
  Dive into the three primary types of machine learning and explain their typical applications.
• Objective:
  Explain the differences between supervised, unsupervised, and reinforcement learning with examples and use cases.
• Audience:
  Learners with basic AI knowledge seeking to understand various ML paradigms.
• What You’ll Learn:
• Characteristics and examples of supervised learning (e.g., classification, regression).
• Key concepts and applications of unsupervised learning (e.g., clustering, dimensionality reduction).
• An introduction to reinforcement learning and its applications in decision-making tasks.

• Scope:
  Explore the process of preparing data, training models, and evaluating their performance in machine learning.
• Objective:
  Provide detailed, step-by-step guidance on data preprocessing, model training using popular libraries, and evaluating model performance with standard metrics.
• Audience:
  Intermediate users with basic Python skills looking to apply ML techniques practically.
• What You’ll Learn:
• How to perform feature engineering and data preprocessing.
• Step-by-step training of ML models using scikit‑learn.
• Methods for model evaluation, including cross-validation and confusion matrices.

• Scope:
  Introduce the fundamentals of neural networks and explore popular deep learning architectures such as CNNs, RNNs, and LSTMs.
• Objective:
  Explain how neural networks function, how deep learning architectures are built, and why they excel in tasks like image and language processing.
• Audience:
  Intermediate learners interested in the technical aspects of deep learning.
• What You’ll Learn:
• Basic structure of a neural network (neurons, layers, activation functions).
• Training neural networks with backpropagation and gradient descent.
• Overview of Convolutional Neural Networks (CNNs) for image recognition.
• Introduction to Recurrent Neural Networks (RNNs) and LSTMs for sequential data.

• Scope:
  Explore advanced deep learning models with a focus on transformer architectures and generative AI models that have revolutionized NLP and other domains.
• Objective:
  Explain the inner workings of transformers, highlight popular models like BERT and GPT, and discuss their significance in generative AI.
• Audience:
  Advanced learners and those interested in state‑of‑the‑art AI models.
• What You’ll Learn:
• Key components of the transformer model: self‑attention, multi-head attention, and positional encoding.
• Overview of popular transformer-based models (BERT, GPT) and their applications.
• Introduction to generative AI and how these models are used for text generation, summarization, and more.

• Scope:
  Provide a detailed guide on setting up a Python-based AI development environment using free and open source tools.
• Objective:
  Ensure you can configure your development environment (using Python, Jupyter, etc.) to experiment with AI frameworks and libraries.
• Audience:
  Beginners with basic computer literacy who wish to set up an environment for AI development.
• What You’ll Learn:
• How to install Python, create virtual environments, and install Jupyter Notebook.
• Step‑by‑step instructions for installing popular libraries: TensorFlow, PyTorch, scikit‑learn, and Hugging Face Transformers.
• Basic troubleshooting and environment configuration tips.

• Scope:
  Walk through the practical steps of training a simple ML model and deploying it using open source tools.
• Objective:
  Provide step‑by‑step instructions to train a model on a sample dataset and deploy it using a free cloud service (e.g., Heroku or Google Cloud Run).
• Audience:
  Intermediate users who want to apply their environment setup to build and deploy models.
• What You’ll Learn:
• How to train a simple model (e.g., linear regression or a small neural network) using scikit‑learn or TensorFlow.
• Steps for saving and deploying the model using a simple web API.
• Techniques for monitoring the deployed model and handling updates.

• Scope:
  Demonstrate how AI and ML models are applied to automate financial operations, such as fraud detection and risk assessment.
• Objective:
  Show a real-world scenario where AI is used to improve accuracy and reduce manual workload in financial operations.
• Audience:
  Finance professionals, data scientists, and IT specialists.
• What You’ll Learn:
• How to deploy a fraud detection model using open source tools.
• Integration of the model with business workflows for real-time decision-making.
• Benefits including cost savings, increased accuracy, and enhanced operational efficiency.

Final Exam