Introduction to Data Literacy

• Importance of data-driven decision-making
• Types of data: Structured, unstructured, and semi-structured
• Real world applications of data analysis

Python for Data Handling

• Python basics: Lists, dictionaries, and loops
• Libraries: NumPy, pandas for data manipulation
• Data-driven decision-making frameworks

Introduction to SQL

• Basic SQL Commands and Syntax
• Data Retrieval and Aggregation

Data preprocessing

• Handling Missing and Inconsistent Data
• Data Transformation and Feature Engineering

Power BI and visualization

• Power BI Interface and Data Importing
• Building Visual Reports and Dashboards

Introduction to Machine Learning

• Definition and applications.
• Supervised, unsupervised, and reinforcement learning.

Supervised Learning Models

• Linear regression and logistic regression.
• Decision trees and Boosting machines (XGBoost).

Unsupervised Learning Models

• Clustering: K-means and hierarchical clustering.
• Dimensionality reduction: PCA, Association rules.

Model Evaluation

• Metrics for regression and classification.
• Cross-validation and hyperparameter tuning.

Introduction to Deep Learning

• Neural networks: Structure and components.
• Use cases in vision, NLP, and more.

Building Neural Networks

• Basics of TensorFlow and PyTorch.
• Implementing fully connected neural networks.
• Activation functions and optimization algorithms.

Advanced Architectures

• Convolutional Neural Networks (CNNs) for image data.
• Recurrent Neural Networks (RNNs, LSTMs) for sequential data.

Deep Learning Applications

• Real-world use cases: Image recognition, sentiment analysis, NER.
• Deployment strategies for DL models.

Introduction to Generative AI

• Overview and applications of GenAI.
• Evolution from traditional AI to Generative AI.

Core Techniques in GenAI

• Generative Adversarial Networks (GANs): Basics and applications.
• Transformers and their role in GenAI.
• Prompt engineering.

Applications of GenAI

• RAG
• Text generation (e.g., GPT models).
• Image generation (e.g., Stable Diffusion, DALL·E).
• Code generation and augmentation.

Future of Generative AI

• Ethical implications and challenges.
• Trends and emerging technologies.

Introduction to Azure

• Overview of Azure Cloud Platform
  • Understanding core Azure services: Compute, Storage, Networking, and Databases
  • Key concepts: Regions, Resource Groups, Subscriptions, and Azure Portal
• Identity, Access, and Resource Management
  • Introduction to Azure Active Directory (Azure AD) and role-based access control (RBAC)
  • Managing resources using the Azure Resource Manager (ARM)

Data Handling on Azure

• Azure Storage Services and Data Integration
  • Working with Azure Blob Storage, Azure Data Lake, and Azure Files
  • Ingesting and moving data using Azure Data Factory and Azure Synapse Pipelines
• Data Security and Monitoring
  • Implementing data encryption, authentication, and secure access
  • Using Azure Monitor and Azure Purview for data governance and tracking

Machine Learning on Azure

• Azure Machine Learning (Azure ML) Workspace
  • Setting up experiments, datasets, and compute resources in Azure ML Studio
  • Understanding pipelines, training, and model management lifecycle
• Model Deployment and Monitoring
  • Deploying machine learning models as web services (real-time or batch)
  • Monitoring model performance using Azure ML metrics and drift detection tools

Azure AI Foundry

• Overview of Azure AI Services and Foundry Capabilities
  • Exploring services like Azure OpenAI, Cognitive Services, and AI Search
  • Introduction to Azure AI Studio and integrating pre-trained models
• Building Custom AI Workflows
  • Leveraging the Foundry framework to design, test, and scale AI solutions
  • Using ML pipelines, model registries, and collaborative development tools

Introduction to Model Deployment

• The importance and challenges of deployment in the ML lifecycle.
• Overview of deployment types: Batch, real-time, and edge.
• Deployment of pipelines and best practices.

Model Packaging and Serialization

• Exporting models with Pickle, ONNX, and TensorFlow SavedModel.
• Environment setup using Docker and Conda for portability

Serving Models Locally

• Deploying models with Flask or FastAPI to create RESTful APIs.
• Hands-on: Build and serve a model as an API locally.

Cloud Deployment

• Deploying ML models on cloud platforms (AWS SageMaker, Google AI Platform, Azure ML).
• Introduction to serverless deployment options.
• Hands-on: Deploy a model to AWS SageMaker or a similar cloud platform.

Monitoring, Scaling, and Maintenance

• Implement monitoring systems (e.g., MLflow, Prometheus).
• Handling model drift and updating models.
• Scaling models with Kubernetes or cloud-native solutions.

A final project covering the entire AI project lifecycle with real world impact.

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End-to-end project execution and documentation for your portfolio

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