Free Courses to Learn AI (Artificial Intelligence) in 2024

Intro to Artificial Intelligence– Udacity

Time to Complete- 4 Months

Level- Intermediate

This is a completely free course to learn artificial intelligence. In this course, you will learn the basics of artificial intelligence( Statistics, Uncertainty, Bayes networks, Machine learning, Logic, and planning).

You will also learn the application of artificial intelligence( Image processing, computer vision, robotics, and robot motion planning, Natural language processing, and information retrieval).

Who Should Enroll?

• Those who have understanding of probability theory.

Interested to Enroll?

Intro to Artificial Intelligence

 

Artificial Intelligence (AI) beyond the realms of Machine Learning (ML) and Deep Learning (DL).

1. AI (Artificial Intelligence):

   • Definition: AI encompasses technologies that enable machines to mimic cognitive functions associated with human intelligence.
   • Examples:
     • 🗣️ Natural Language Processing (NLP): AI systems that understand and generate human language. Think of chatbots, virtual assistants (like Siri or Alexa), and language translation tools.
     • 👀 Computer Vision: AI models that interpret visual information from images or videos. Applications include facial recognition, object detection, and self-driving cars.
     • 🎮 Game Playing AI: Systems that play games like chess, Go, or video games using strategic decision-making.
     • 🤖 Robotics: AI-powered robots that can perform tasks autonomously, such as assembly line work or exploring hazardous environments.

2. Rule-Based Systems:

   • Definition: These are AI systems that operate based on predefined rules or logic.
   • Examples:
     • 🚦 Traffic Light Control: Rule-based algorithms manage traffic lights by following fixed patterns (e.g., green for a specific duration, then yellow, then red).
     • 📜 Expert Systems: These systems use rules to make decisions in specialized domains (e.g., medical diagnosis, tax planning).

3. Symbolic AI:

   • Definition: Symbolic AI represents knowledge using symbols and rules.
   • Examples:
     • 🌐 Knowledge Graphs: Representing relationships between entities (e.g., Wikipedia infoboxes).
     • 🧠 Logic Programming: Using formal logic to infer conclusions (e.g., Prolog).

4. Genetic Algorithms:

   • Definition: AI techniques inspired by natural selection and genetics.
   • Examples:
     • 🧬 Optimization Problems: Genetic algorithms evolve solutions over generations (e.g., optimizing flight schedules).

5. Swarm Intelligence:

   • Definition: AI models inspired by collective behavior in natural systems.
   • Examples:
     • 🐝 Ant Colony Optimization: Mimicking ant foraging behavior to solve optimization problems.
     • 🦋 Particle Swarm Optimization: Simulating bird flocking to find optimal solutions.

Remember, AI is a vast field, and these examples showcase its diversity beyond ML and DL! 🚀🤓

Difference between AI (Artificial Intelligence) and Machine Learning (ML)

 🤖 AI (Artificial Intelligence):

• Definition: AI refers to creating machines or software that can perform tasks that typically require human intelligence.
• Example: Think of AI as the brain of a robot. It allows the robot to make decisions, learn from its environment, and adapt to new situations.
• Emoji: 🤖

🧠 Machine Learning (ML):

• Definition: ML is a subset of AI. It’s like teaching a computer to learn from data without explicitly programming it.
• Example: Imagine you’re teaching a pet dog to recognize different toys. You show the dog examples of a ball, a bone, and a frisbee. Over time, the dog learns to identify these objects on its own.
• Emoji: 🧠

🤖 vs. 🧠:

• AI is the big concept—the idea of creating smart machines.
• ML is the practical implementation within AI—it’s how we teach those machines to learn and improve.

Remember, AI is like the dream of creating intelligent beings, and ML is the practical way we make that dream come true! 🛠️🚀

Differences between Azure OpenAI, GPT-4, and GPT-4 Turbo

 

1. Azure OpenAI:

   • Azure OpenAI Service is a cloud-based platform provided by Microsoft.
   • It integrates OpenAI’s powerful language models into Azure services.
   • Azure OpenAI Service offers various models, including GPT-4 and GPT-4 Turbo, for natural language understanding and generation.
   • It provides advanced security features, network isolation, and custom authentication for private access to models.

2. GPT-4:

   • GPT-4 is a large multimodal model developed by OpenAI.
   • It accepts both text and image inputs and generates text as output.
   • GPT-4 improves upon GPT-3.5 and has a broader general knowledge and advanced reasoning capabilities.
   • It is optimized for chat and traditional completions tasks.

3. GPT-4 Turbo:

   • GPT-4 Turbo is an enhanced version of GPT-4.
   • It balances advanced capabilities with faster response times.
   • Suitable for interactive applications, it provides accurate responses while maintaining efficiency.

In summary, Azure OpenAI integrates GPT-4 and GPT-4 Turbo models, offering powerful language capabilities for various applications. GPT-4 Turbo is particularly well-suited for chat interactions and complex problem-solving. 🛠️🚀

Azure OpenAI Service models - Azure OpenAI | Microsoft Learn

Generative AI : tool-calling-with-langchain

tool-calling-with-langchain 

Application of Generative AI

 

Application of Generative AI