TRAINING
MENTORSHIP
SUCCESS
Education is the most powerful weapon which you can use to change the world. It isn't just for economic success; it's about nation building and personal development.
Nelson Mandela
AI Introductory Learning Session
I. What is AI
To master AI and use it effectively, it's essential to understand its basis: the what, why, when, who, and how of artificial intelligence. That's our aim here.
Historically
Allan Turing
British Mathematician, logistician. Father of Computer science as we know it.
Created a machine called the "Bombe" to break the Enigma Code during War War 2.
Turing Test: He was the first to ask "Can Machine Think" and laid out the philosophical foundation for Artificial Intelligence.
Artificial Intelligence is about building computer systems that can perform tasks that typically require human intelligence — things like understanding language, recognizing images, making decisions, and even creating content (literature, art, …).
II. Foundation
To truly understand AI, it helps to see how the different branches of technology relate to each other. Think of it like a set of nesting dolls — each layer goes deeper and becomes more specialized.
Let's break it down with a simple analogy everyone can relate to: cooking.
Traditional Programming
Following a recipe step-by-step. You tell the computer exactly what to do.
Machine Learning
Learning by tasting many dishes. The system discovers patterns from experience.
Deep Learning
Understanding flavors at a molecular level. The system grasps deep, complex patterns.
What Is Machine Learning?
Machine Learning is the layer under AI where systems learn from data and improve from experience — without being explicitly programmed for every scenario.
Instead of writing rules for every possible situation, you feed the system examples, and it figures out the patterns on its own. The more data it sees, the smarter it gets.
What is Deep Learning
Deep Learning takes Machine Learning to the next level by using artificial neural networks inspired by the structure of the human brain.
These networks contain layers of interconnected nodes that process information in increasingly complex ways — allowing machines to understand images, speech, and even generate human-like text.
Deep Learning is the technology behind the AI revolution we're living through right now.
Large Language Models (LLM)
Powers ChatGPT, Claude, and Gemini that can hold conversations, write essays, and solve problems.
Self-Driving Cars
Enables self-driving cars to "see" the road.
Voice Assistants
Allows voice assistants to understand your words.
What are AI Models?
Deep Learning rely on Models to do the learning and the prediction.
Brain of AI
What is an AI Model?
A model is a software program or mathematical representation that has been "trained" on a dataset to recognize patterns and make decisions without being explicitly programmed for every scenario.
If an algorithm is a recipe (a set of instructions), then the model is the final dish (the result of applying those instructions to specific ingredients, or data).
Core Components
An AI model is typically composed of three elements:
Data
The "fuel" used during training, such as millions of images, books, or sensor logs.
Algorithm
The mathematical logic (like a neural network or linear regression) that determines how the model processes information.
Parameters
Internal settings (often billions of numerical "weights") that the model adjusts during training to improve its accuracy.
Common Types of AI Models
Models are often categorized by how they learn or what they are designed to do:
By Learning Method
Supervised Learning
The model is trained on labeled data (e.g., photos tagged as "cat" or "dog") so it learns to associate specific features with correct answers.
Unsupervised Learning
The model finds hidden patterns in unlabeled data on its own, such as grouping customers with similar buying habits.
Reinforcement Learning
The model learns through trial and error, receiving "rewards" for correct actions, commonly used in self-driving cars or robotics.
By Task or Capability
Large Language Models (LLMs)
Massive models like GPT-4 designed to understand and generate human-like text.
Computer Vision Models
Specialized in identifying objects, faces, or medical anomalies in images and video.
Generative Models
Designed to create entirely new content, such as original artwork, music, or computer code.
Foundation Models
Broad, versatile models trained on huge datasets that can be "fine-tuned" for many different specific tasks.
How Models Are Used
Once a model is trained and validated, it is "deployed" into a real-world application to perform tasks such as:
Predicting
Predicting future stock prices or weather patterns.
Classifying
Classifying emails as spam or not spam.
Recommending
Recommending songs on Spotify or movies on Netflix.
Summarizing
Summarizing long documents or translating languages.
Top 10 AI Categories
In 2026, the AI landscape has shifted from simple "chatbots" to deeply integrated systems that can "see," "hear," and "act" autonomously.
Here are the top 10 AI categories:
III. AI Applications | Utilisation d'IA | AI in Action
How Businesses Use AI Today
AI is no longer a futuristic concept for businesses — it's a present-day competitive advantage. Companies of every size, from startups to Fortune 500 giants, are using AI to serve customers faster, make smarter decisions, and operate more efficiently.
If your competitor is using AI and you're not, you're already falling behind.
IV. INDUSTRY LEADERS
Major AI Players
NVIDIA
Microsoft
OpenAI
Amazon (AWS)
Anthropic
Meta
IBM
AI Industry Leaders — Deep Dive
NVIDIA
The hardware backbone of AI.
- ~80% AI compute share
- GPUs: H100, B200, GB200
Microsoft
AI embedded across:
- Copilot (Office apps)
- GitHub Copilot
- Azure AI
- Power Platform AI
- Gemini family (multimodal)
- Gemini 2.5 Pro
- Gemini Flash
- NotebookLM
- Workspace AI
OpenAI
- ChatGPT
- GPT 4o
- DALL·E
- Whisper
- Custom GPTs
Amazon / AWS
- Amazon Bedrock
- SageMaker
- Amazon Q
- CodeWhisperer
Anthropic, Meta & IBM
- Claude (Anthropic) — safe, accurate AI
- Llama (Meta) — open-source models
- Watsonx (IBM) — enterprise AI
V. CHOOSING YOUR TOOLS
With so many AI tools available, it can feel overwhelming to know where to start. The good news is that you don't need to master every tool — you just need to find the right one for your specific needs. Use this simple decision framework to match your goals with the best AI tools available.
Start with the tool that fits your needs.
Most tools offer free tiers.
AI Tools Comparison (Summary)
VI. BENEFITS OF AI
Efficiency & Cost Savings
- 70% time saved
- 40% cost reduction
- 24/7 availability
- 10x scalability
Better Decision Making
- Pattern recognition
- Predictive analytics
- Risk assessment
- Personalization
New Opportunities
- New AI careers
- New business models
- Entrepreneurship acceleration
Time Saved
Efficiency gains through automation
Cost Reduction
Lower operational expenses
VII. GETTING STARTED
How to Start Today
Practice daily (10–15 min)
Learn prompting
Pick one tool
Join communities such as CDMA Academy
Use AI on real tasks
Prompting Basics — How to Talk to AI
The quality of your AI output depends almost entirely on the quality of your input. This is called prompting — the art of communicating clearly with AI to get the results you want. Think of it like giving instructions to a very capable but very literal assistant. The more specific and clear you are, the better the results.
Be specific
Instead of "Write about dogs," try "Write a 200-word blog post about the top 3 health benefits of owning a golden retriever, targeted at first-time dog owners."
Give context
Tell the AI who you are, who the audience is, and what the goal is. "I'm a small business owner writing to potential customers" gives far better results than no context.
Provide examples
Show the AI what good output looks like. Paste an example of the tone, format, or style you want and say "Write something similar to this."
Ask for formats
Specify how you want the output structured: "Give me a bulleted list," "Create a table," or "Write this as a professional email."
Iterate and refine
Your first prompt is a starting point, not the finish line. Refine your prompt based on the output: "Make it shorter," "Add more examples," "Use a more casual tone."
Leverage AI Instead of Novelty
Develop a strategy
In the age of AI, you're not hired for your time anymore. You're paid for your taste. Our Real Edge Is Being Human.
Example of Strategies I Use
Strategy #1: The Clone
Build a Persistent AI That Knows You
- Extract & Synthesize Your 'Operating System': Interview and summary of me
- Project — Store Context Without Mixing Domains
Strategy #2: The Team
Agent Mode, Vision, and Deep Research
Strategy #3: Practice
Practice makes perfect. Practice Practice Practice
Strategy #4: True Understanding
You are the master of your destiny. Be able to explain your own project. Don't copy and paste
Live Demo
Summarize documents
Generate emails
Create images
Analyze data
Build a business plan
VIII. LOOKING AHEAD
The Future of AI
Agentic AI
Physical AI (Robots)
Personalized AI assistants
AI in every device
Closing Message
AI Is a Tool — You Are the Power
- AI multiplies your abilities
- You don't need to be technical
- Start small, grow fast
"The best time to start learning AI was yesterday. The second best time is right now."
Q&A
Ask anything about AI — practical, curious, or ambitious.
Community Dream Makers Academy