AI for Businesses

The "AI and Data Science Dictionary" training provides a comprehensive overview of key terminologies across statistics, data visualization, data analysis, machine learning, deep learning, generative AI, NLPs, data management, big data technologies, and cybersecurity. This one-day session is designed to demystify complex concepts and provide a bird’s-eye view of essential terms, ensuring participants gain a solid foundational understanding of AI and data science vocabulary.

This workshop is ideal for business leaders, managers, analysts, and professionals who want to confidently navigate discussions on AI, data science, and related technologies without getting lost in technical jargon.

• Understand key AI and data science terminologies across multiple domains.
• Differentiate between statistics, data analysis, and machine learning concepts.
• Recognize the core principles of deep learning and generative AI.
• Identify common NLP (Natural Language Processing) terms and their applications.
• The Role of the AI Agents
• Familiarize with data management and big data.
• Gain insights into the essentials of cybersecurity.
• Build confidence in engaging with AI and data science discussions.

Morning Session:
- AI, data science, and their impact on industries.

- Fundamental terms in statistics, data analysis, and visualization.

- Understanding machine learning and deep learning vocabulary.

Afternoon Session: Advanced Topics & Industry Applications
- Generative AI and NLP: Key terms and real-world applications.

- Big Data & Data Management: Essential terminologies

- Cybersecurity in AI

• Introduction to AI and Data Science and their terminologies.
• Statistics & Data Analysis: Key concepts like mean, variance, correlation, regression, and hypothesis testing.
• Data Visualization: Terms such as histograms, scatter plots, dashboards, and storytelling.
• Machine Learning: Supervised vs. unsupervised learning, training models, overfitting, feature engineering.
• Deep Learning & Generative AI: Neural networks, transformers, GANs, and diffusion models.
• NLP ad AI Agents: Tokenization, embeddings, LLMs, sentiment analysis, chatbots.
• Data Management & Big Data: Structured vs. unstructured data, data lakes, NoSQL, Hadoop, Spark.

The "AI & Data Science Roadmap" workshop is designed to provide a comprehensive journey through the foundational and advanced aspects of generative AI tools and their interconnected disciplines: Data Science. The workshop explores how these technologies work and can be applied across industries, from data exploration to machine learning models and the revolutionary world of generative AI. The program looks at how to build a solid foundation for a practitioner aiming to complete the Data Science and AI knowledge puzzle; this three-day immersive training will blend theoretical insights with practical applications. 

• Recognize data visualization's role in presenting insights.
• Use descriptive statistics to summarize datasets.
• Conduct statistical analysis to find patterns in data.
• Differentiate between supervised and unsupervised ML.
• Understand the progression from DL to generative AI.
• Explore how generative AI tools create content.
• Learn the essentials of RPA tools.
• Compare Data Management to Big Data Essentials
• Develop critical thinking on AI ethics and applications.
• Identify prerequisites and pitfalls for successful AI projects.

Day 1: 

- Visual and Data Analytics Dashboards

- Machine Learning Dashboards 

Day 2:

- Why Deep Learning?

- From Generative Deep to Generative AI

Day 3:

- CRISP-DM process and RPAs

- AI ethics and successful strategies

- Project Implementation

- AI and Data Science and their interconnections.

- Explore the full AI and Data Science lifecycle.

- Gain hands-on experience with data visualization, statistical analysis, and machine learning.

- Learn the differences between supervised, unsupervised, and reinforcement learning.

- Understand the principles of deep learning, generative AI, and NLP applications.

- Discover Big Data technologies and data management strategies.

- Develop a strategic roadmap for AI projects.

- Address AI ethics, bias, and responsible AI implementation.

- ChatGPT prompt engineering

- Examples of AI projects

The "AI Ethics & Business Strategies" training provides a structured approach to understanding AI’s ethical implications and strategic impact on businesses. It begins with a brief introduction to AI and the CRISP-DM framework for structured AI project execution. The workshop then delves into AI Ethics, addressing key concerns like bias, transparency, and regulatory compliance. Finally, it explores business strategies, incorporating Porter’s three thumb rules to balance AI innovation with ethical and competitive decision-making. 

• Understand AI fundamentals in modern industries.
• Apply the CRISP-DM framework for AI projects.
• Identify and address AI bias and transparency.
• Navigate regulatory and ethical AI guidelines.
• Implement responsible AI governance and compliance.
• Develop AI strategies aligned with ethics.
• Use Porter’s rules to maximize AI advantage.
• Balance AI innovation with corporate responsibility and risk.

Day 1: 

- Introduction to AI

- CRISP-MD overview

- AI ethical framework and principles

Day 2:

- Porter's rules of thumbs

- Business strategies

- Complete case study

- Overview of AI and its business impact.

- CRISP-DM framework for AI project management.

- AI ethics: fairness, transparency, accountability, and bias mitigation.

- Regulatory compliance and frameworks.

- Strategies for AI adoption in competitive markets.

- Porter’s rules for balancing AI opportunities and risks.

- Best practices for AI governance and risk management.

- Ethical decision-making for sustainable AI development.

The "Generative AI Tools for Businesses" training provides a hands-on exploration of cutting-edge AI tools that can enhance productivity, creativity, and decision-making in various business domains.

The workshop begins with a brief introduction to AI, followed by an in-depth look at key generative AI tools such as Synthesia, HeyGen, AI-Clip Art, Leonardo AI, Parrot AI, Runway ML, Tome AI, MidJourney, Misgif, Eleven Labs, CoPilots, and ChatGPT.

Participants will learn how to leverage these tools for content creation, automation, and business efficiency.

• Understand the fundamentals of AI and its business applications.
• Explore AI-driven content creation tools like Synthesia, HeyGen, and MidJourney.
• Generate AI-assisted visual content using AI-Clip Art, Leonardo AI, and Runway ML.
• Utilize AI-powered text and speech tools like ChatGPT, Eleven Labs, and CoPilots.
• Create interactive and multimedia-driven presentations with Tome AI.
• Automate marketing, branding, and storytelling using AI-based solutions.
• Identify the best generative AI tools for different business needs.
• Implement AI strategies to improve efficiency, creativity, and engagement.

Day 1: 

- Introduction to Data Science & AI

- Synthesia, HeyGen, Midjourney

Day 2:

- ChatGPT

- Eleven Labs, RunwayML

- Tome AI, Otter

- Copilots

- Introduction to AI and its role in business transformation.

- AI-powered video generation: Synthesia, HeyGen, and Runway ML.

- AI-driven design and illustration: AI-Clip Art, Leonardo AI, and Midjourney.

- Text, voice, and chatbot solutions: ChatGPT, Eleven Labs, and CoPilots.

- AI tools for presentations and storytelling: Tome AI and Misgif.

- Use cases and best practices for AI-driven content creation.

- Hands-on exercises with key AI tools for business applications.

- Future trends in generative AI and business innovation.