Artificial Intelligence, Machine Learning & Process Automation

Module 1: Financial Data Engineering and Foundations of Financial Risk

Module 2: Predictive Modelling for Credit Risk and Default Prediction

Module 3: Time Series Analysis and Forecasting for Market Risk

Module 4: Algorithmic Trading Strategies and Machine Learning

Module 5: Natural Language Processing (NLP) for Financial Sentiment Analysis

Module 6: Fraud and Anomaly Detection Systems

Module 7: Model Governance, Validation, and Regulatory Compliance (Basel/XAI)

Module 8: Customer Analytics (LTV, Churn) and Behavioural Finance

Module 9: Deep Learning Architectures in Finance (RNNs, LSTMs)

Module 10: Model Deployment, Monitoring, and MLOps in a Financial Context

Here's What You'll Learn:

Module 1: Introduction to the Generative AI Landscape

Module 2: The Transformer Architecture Deep Dive

Module 3: Masterclass in Prompt Engineering

Module 4: Working with LLM APIs and Open Source Models

Module 5: Retrieval-Augmented Generation (RAG) Systems

Module 6: Fine-Tuning and Model Adaptation

Module 7: Multimodal Generative AI

Module 8: Building Autonomous AI Agents

Module 9: LLMOPs and Production Deployment

Module 10: Ethics, Governance, and AI Security

Here's What You'll Learn:

Module 1: Introduction to the AI Ecosystem

Module 2: Logic and Problem Solving in AI

Module 3: Data: The Foundation of Intelligent Systems

Module 4: Understanding Machine Learning Paradigms

Module 5: Exploring Neural Networks and Deep Learning

Module 6: Natural Language Processing and Communication

Module 7: Computer Vision and Image Recognition

Module 8: Robotics and Autonomous Systems

Module 9: AI Strategy and Project Management

Module 10: Ethics, Governance, and Responsible AI

Here's What You'll Learn:

Module 1: Python Essentials for Data Science

Module 2: Numerical Computing with NumPy

Module 3: Data Manipulation and Analysis with Pandas

Module 4: Data Visualization and Exploratory Data Analysis

Module 5: Foundations of Supervised Learning

Module 6: Advanced Classification and Regression Techniques

Module 7: Unsupervised Learning and Dimensionality Reduction

Module 8: Introduction to Deep Learning and Neural Networks

Module 9: Natural Language Processing (NLP) Fundamentals

Module 10: Model Deployment and Best Practices

Here's What You'll Learn:

Module 1: Foundations of Time Series Analysis

Module 2: Feature Engineering for Temporal Data

Module 3: Classical Statistical Forecasting Models

Module 4: Smoothing Techniques and State Space Models

Module 5: Machine Learning for Regression-Based Forecasting

Module 6: Advanced Ensemble Methods in Time Series

Module 7: Forecasting with Facebook Prophet

Module 8: Neural Networks for Sequence Prediction

Module 9: Validation and Evaluation Metrics for Time Series

Module 10: Productionizing Forecasting Pipelines

Here's What You'll Learn:

Module 1: Introduction to Azure Machine Learning Ecosystem

Module 2: Managing Data and Compute Resources

Module 3: Visual Model Building with Azure ML Designer

Module 4: Accelerated Modeling with Automated ML

Module 5: Code-Based Experimentation with Python SDK

Module 6: Advanced Model Training and Hyperparameter Tuning

Module 7: Creating and Managing ML Pipelines

Module 8: Model Registration and Deployment

Module 9: Responsible AI and Model Explainability

Module 10: MLOps and Workspace Security

Module 1: Geospatial Data Foundations and Python for GIS

Module 2: Advanced Remote Sensing Data Pre-processing

Module 3: Feature Engineering for Geospatial Data

Module 4: Fundamentals of Traditional Machine Learning

Module 5: Supervised Classification: Model Selection and Training

Module 6: Unsupervised Clustering and Segmentation

Module 7: Introduction to Deep Learning and Neural Networks

Module 8: Convolutional Neural Networks (CNNs) for Imagery

Module 9: Semantic Segmentation for Land Cover Mapping

Module 10: Object Detection in High-Resolution Imagery

Module 11: Time-Series Analysis and Change Detection

Module 12: Model Evaluation and Spatial Accuracy Assessment

Module 13: Advanced Vector Data Machine Learning

Module 14: Integrating Lidar Data with Deep Learning

Module 15: Cloud Computing for Machine Learning Remote Sensing

Module 16: Operationalizing and Deploying Geospatial Models

Module 17: Geospatial Ethics and Bias in Satellite Imagery

Module 18: Capstone Project: End-to-End Geospatial AI Solution

Here's What You'll Learn:

Module 1: Image Processing Fundamentals and Optics

Module 2: Feature Extraction and Descriptor Matching

Module 3: Object Detection and Localization

Module 4: Deep Learning for Image Classification

Module 5: Semantic and Instance Segmentation

Module 6: Real-time Video Analysis and Motion Tracking

Module 7: Industrial Quality Inspection and Defect Detection

Module 8: 3D Vision and Depth Estimation

Module 9: Edge AI and Hardware Optimization

Module 10: Computer Vision System Integration and Ethics

Here's What You'll Learn:

Module 1: Introduction to NLP in the Business Context

Module 2: Text Pre-processing and Linguistic Fundamentals

Module 3: Vectorization and Word Embeddings

Module 4: Sentiment Analysis and Opinion Mining

Module 5: Automated Text Classification and Topic Modeling

Module 6: Named Entity Recognition (NER) for Information Extraction

Module 7: Building Conversational AI and Chatbots

Module 8: Text Summarization and Document Generation

Module 9: Harnessing Large Language Models (LLMs) for Business

Module 10: Deploying NLP Models and Ethical Governance

Here's What You'll Learn:

Module 1: The Generative AI Creative Landscape

Module 2: Advanced Prompt Engineering for Text

Module 3: AI-Powered Copywriting and Storytelling

Module 4: Professional AI Image Generation

Module 5: Advanced Visual Refinement and Editing

Module 6: Generative Video and Animation

Module 7: Synthetic Audio and Voice Synthesis

Module 8: AI Workflow Integration and Automation

Module 9: Brand Consistency and Custom AI Models

Module 10: AI Ethics, Copyright, and Policy

Here's What You'll Learn:

Module 1: Foundations of AI in the Healthcare Ecosystem

Module 2: Medical Imaging and Computer Vision

Module 3: Natural Language Processing for Electronic Health Records (EHR)

Module 4: Predictive Analytics for Patient Outcomes

Module 5: AI in Drug Discovery and Genomics

Module 6: Clinical Decision Support Systems (CDSS)

Module 7: Healthcare Data Privacy, Security, and Ethics

Module 8: AI for Population Health and Epidemiology

Module 9: Regulatory Frameworks and AI Model Validation

Module 10: Future Trends and Deploying AI in Clinical Practice

Here's What You'll Learn:

Module 1: Foundations of ML in the Pharma Pipeline

Module 2: Data Engineering and Molecular Representations

Module 3: Virtual Screening and Hit Identification

Module 4: Predictive Modeling for ADMET and Toxicity

Module 5: De Novo Drug Design and Generative AI

Module 6: Target Identification and Multi-Omics Integration

Module 7: AI-Driven Lead Optimization and Synthesis Prediction

Module 8: AI in Clinical Trials and Digital Twins

Module 9: Model Interpretability and Explainable AI (XAI)

Module 10: Regulatory Compliance and MLOps for Pharma