Course Taxonomy: Data & AI

Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We’ll work with you to tune this course and level of coverage to target the skills you need most. Topics, agenda and labs are subject to change, and may adjust during live delivery based on audience skill level, interests and participation.

1. Introduction to AI & Machine Learning
2. Deeper Dive into Machine Learning
3. Leveraging AI in Business & Decision Making
4. Hot Trends for AI in Business: Large Language Models (LLM), Generative AI and GPT
5. Basics of Neural Networks
6. Natural Language Processing (NLP) & Sentiment Analysis
   1. Lab: Use an online sentiment analysis tool to analyze customer feedback from a popular business.
7. Using AI for Image, Video, and Audio Processing
   1. Lab: Use AI in recognizing and analyzing images
8. AI for Business Technical Tools: Data Science, Deep Learning & The Cloud
   1. Tools: Azure, Google, IBM, Amazon solutions
9. Practical Applications and the Future of AI in Business

Next-Steps

• Hands-on Practice
• Resources
• AI & ML Communities

1.  Introduction to Artificial Intelligence

• Defining AI: Understanding what AI is and what it is not
• Brief history of AI: From early concepts to modern AI
• Types of AI: Narrow AI vs. General AI

2.  Foundations of Machine Learning

• What is Machine Learning?
• How ML differs from traditional programming
• Overview of ML types: Supervised, Unsupervised, and Reinforcement Learning

3.  Key Concepts and Terminologies

• Understanding algorithms, models, and data in ML
• Introduction to Neural Networks and Deep Learning
• Exploring key metrics for evaluating ML models

4. Applications of AI and ML

• Real-world applications across various industries (e.g., healthcare, finance, transportation)
• Impact of AI and ML on society and the future of work
• Ethical considerations and challenges in AI and ML

5. The AI and ML Ecosystem

• Overview of tools, languages, and platforms used in AI/ML development
• Introduction to the role of data scientists and AI/ML engineers
• Emerging trends and future directions in AI and ML

6. Getting Started with AI and ML

• Resources for further learning and exploration in AI and ML
• Pathways to becoming an AI/ML professional
• Community and networking opportunities in the AI/ML field

1. Introduction to AI APIs

• Overview of AI and its applications
• Understanding APIs in the context of AI
• Types of AI APIs (e.g., natural language processing, computer vision, machine learning models)

2. Key Players in the AI API Market

• Overview of leading AI APIs (e.g., OpenAI's GPT, Google Cloud AI, IBM Watson, Microsoft Azure AI)
• Comparative analysis of features, strengths, and limitations
• Pricing models and accessibility

3. Evaluating AI APIs for Your Needs

• Defining project requirements and objectives
• Criteria for selecting an AI API (e.g., performance, scalability, ease of integration)
• Legal and ethical considerations

4. Getting Started with AI APIs

• Setting up developer accounts and API keys
• Understanding documentation and SDKs
• Basic operations with AI APIs (e.g., sending requests, handling responses)

5. Integrating AI APIs into Applications

• Best practices for API integration
• Handling errors and exceptions
• Optimizing API usage and managing costs

6. Practical Workshop

• Guided project: Integrating an AI API into a sample application
• Troubleshooting and optimization tips

7. Q&A and course wrap-up

• The AI Revolution: 1950’s to now
• Generative AI: Real-World Applications
  • Content generation
  • Business development
  • Client delivery
  • Training
• Try Cprime’s AI Chat Bot: On your laptop, tablet, or phone
  • Chained prompts
  • Strong verbs
  • Summarize or expand responses
  • Focus for an audience or channel
  • … and much more
• What more you can do: APIs, Plugins, and Vectors – Oh my!
  • A secure AI
  • Leverage your own data
  • Import and data or website you need

1.    Custom Prompt Engineering:

• Basics of prompt engineering for specific outcomes.
• Advanced techniques for refining prompts to improve responses.
• Strategies for iterative prompt testing and optimization.
• Examples of effective prompts for various business scenarios.
• Exercise: Refining prompts in CprimeAI

2.   Advanced Gen-AI Applications:

• Introduction to advanced features and capabilities of CprimeAI.
  • (Note that other tools like ChatGPT have many of the same features. However, CprimeAI supports fully private mode – important to many organizations. Also, the free version of ChatGPT is extremely out of date.)
• Case studies of advanced applications.
• Integration with other software and platforms.
  • Multiple different modes of integration.
  • Vectorization: Pull relevant records and create a cache, e.g. recent/frequent records.
  • Direct: Pull specific records e.g. using JQL for Jira.
  • Either way, the relevant records are used as context.
  • Introduce “RAG” concept.
• Customizing responses for specific organizational needs.
  • You can type a prompt such as “Remember: if you are asked x, do y.”
  • This can be injected “under the covers” as a set of system prompts.
• Discussion: List your organization’s potential applications of Gen-AI

3.   File Manipulation:

• Techniques for automating document creation, editing, and formatting.
• Strategies for comparing and merging documents.
• Best practices for managing file storage and retrieval integration.
• Security considerations when manipulating files.
• Exercise: File manipulation in CprimeAI

4.   Data Analysis:

• Fundamentals of data querying and analysis.
• Visualizing data analysis results.
  • (Currently, LLM image generation is primitive and unreliable. However, LLMs are good at transforming output into different formats, e.g., you can output as CSV, then attach that to an Excel chart. More advanced integration is possible using commercial or open-source tools, so long as the data format can be explained to the LLM.)
• Enhancing data accuracy and reliability in reports. Introduce “do this, then think about the result of this.”
• Advanced data analysis techniques with external tools.
  • (Note: This agenda item can be customized for a specific tool, as requested for private delivery. At the very least, you can get a report from an external tool, load it, and tell the LLM to look for certain things in it. But if we know more what they want to do, we can do better.)
• Exercise: Data analysis in CprimeAI

5.   Productivity Enhancement:

• Streamlining workflow processes.
• Automating routine tasks and communications.
• Customizing for team collaboration and project management.
• Time-saving tips and tricks for daily tasks.
• Discussion: Your organization’s productivity-enhancement opportunities

6.   Integration with Internal Data Sources:

• Overview of connecting to internal databases and data lakes.
• How to ensure data privacy and security during integration.
• Techniques for real-time data analysis and insight generation.
• Case studies on successful internal data source integrations.
• Discussion: Your organization’s data that would need to be integrated with Gen-AI

7.    Business Development Applications:

• Market research and competitive analysis.
• Generating leads and identifying business opportunities.
• Crafting personalized outreach and marketing strategies.
• Analyzing customer feedback and trends.
• Exercise: Research competitors/clients using CprimeAI

8.   Limitations and Best Practices:

• Understanding the computational and contextual limitations of an LLM interface.
• Ethical considerations and responsible use.
• Troubleshooting common issues and challenges.
• Staying updated with the latest Gen-AI developments and best practices.
• Discussion: Roadmap for Gen-AI at Your organization

Part 1: Introduction

• Workshop Objectives & Attendee Pain Points
• Demystifying AI: From Myth to Reality

Part 2: Technology & Terminology Primer

• Breaking Down AI: Essential Terms and Concepts
• Linking AI to Business Operations

Part 3: Pitfalls of AI Adoption

• Common Challenges: Data Quality, Scalability, and Integration
• Addressing Ethical and Bias Concerns

Part 4: ROI Analysis for AI Initiatives

• Quantifying AI's Real-World Business Value
• Decoding the Real-World Value of AI for Your Business
• Practical Steps for Measuring AI Impact

Part 5: Map AI Solutions to Customer’s Business

• From Current Pain Points to Potential AI Solutions
• Assessing existing systems, databases, and software
• Identifying communication and integration gaps
• AI as a solution to streamline and optimize
• Strategies for Effective Data Management with AI Integration

Part 6: Conclusion

• Aligning AI with Long-Term Goals & Future Steps

Appendix: Selected AI Solutions Deep Dive

• Resources and Best Practices for Implementation