AI and Machine Learning

Module 1: Introduction to AI & Machine Learning:

·Demystifying AI & ML: Understanding key terms, capabilities, and their differences.

·Understanding the role of data in AI & ML: Data collection, pre-processing, and ethical considerations.

Module 2: AI in Action: Business Applications Across Industries (Intermediate):

Finance:

·Fraud Detection: AI algorithms analyze transaction patterns to identify and prevent fraudulent activity (e.g., FICO Falcon Fraud, NICE Actimize).

·Algorithmic Trading: AI-powered systems analyze market data and execute trades based on pre-defined strategies (e.g., Kensho, Quantopian).

Healthcare:

·Medical Diagnosis: AI assists doctors in analyzing medical images (X-rays,

MRIs) for disease detection (e.g., Zebra Medical Vision, Paige).

·Virtual Assistants for Patient Support: AI-powered chatbots answer patient inquiries, schedule appointments, and provide basic medical information (e.g., Babylon Health, Ada).

Module 3: AI for Customer Experience (Intermediate):

Retail:

·Personalized Product Recommendations: Recommendation engines

powered by AI suggest relevant products to customers based on their browsing history and purchase behavior (e.g., Amazon Recommender Systems, Salesforce Einstein).

·Chatbots for Customer Service: AI-powered chatbots handle basic customer inquiries, provide 24/7 support, and escalate complex issues to human agents (e.g., ManyChat, Drift).

Logistics & Transportation:

·Route Optimization: AI algorithms analyze traffic patterns and real-time data

to optimize delivery routes, saving time and fuel (e.g., Google Maps Platform,

HERE Technologies).

·Predictive Maintenance: Similar to manufacturing, AI predicts potential

failures in vehicles, enabling preventive maintenance and avoiding

breakdowns (e.g., IBM Maximo Asset Management, Uptake).

Customer Service (General):

·Sentiment Analysis: AI analyzes customer feedback (textual or audio) to

understand sentiment and identify areas for improvement (e.g., Microsoft

Azure Text Analytics, Amazon Comprehend).

·Voice Assistants for Customer Support: Virtual assistants powered by AI

respond to customer voice queries, providing information and resolving basic issues (e.g., Amazon Alexa for Business, Google Assistant for Business).

Module 4: Deriving Value from AI & ML (Intermediate):

·Building a Business Case for AI & ML: Identifying potential applications and demonstrating ROI for your organization.

·Selecting the Right AI & ML Tools: Matching your needs with available technologies and platforms.

·Implementation Strategies: Planning and executing AI & ML projects within your organization, considering ethical considerations and potential risks.

Module 5: The Future of AI & Business (Advanced):

·Emerging trends in AI & ML: Exploring advancements in AI, their potential impact on business, and ethical considerations for the future.

·Developing an AI & ML roadmap: Planning for long-term integration of AI & ML into your business operations.