Artificial Intelligence

🔅 Responsible AI and Application Development 📝 Learn all of the steps of creating an AI application with machine learning (ML), ensuring fairness and mitigating biases throughout the process. 🌐 Author: Laurence Moroney 🔰 Level: Intermediate ⏰ Duration: 1h 37m 📋 Topics: AI Software Development, Responsible AI, Artificial Intelligence 🔗 Join Machine Learning for more courses

Machine Learning vs. Deep Learning

Machine Learning vs. Deep Learning

Machine Learning vs. Deep Learning

Machine Learning vs. Deep Learning 🤖 Machine Learning: Requires engineered features + clean data. Examples: Credit scoring (Logistic Regression), sales forecast (Decision Trees). 🧠 Deep Learning: Takes raw data (pixels, text, audio). It learns features end-to-end via hidden layers. Examples: Self-driving cars (CNN), voice assistants (RNN), LLMs (Transformers). ML needs your brain. DL builds its own.

🤖 IMPORTANT AI TERMS & TOOLS YOU SHOULD KNOW 🧠 If you want to understand modern AI better, these are some of the most important terms and tools to know. 🧠 1. Generative AI AI that can create new content such as: ✔️ Text ✔️ Images ✔️ Audio ✔️ Video ✔️ Code Examples include ChatGPT, Gemini and Claude. 💬 2. Large Language Model (LLM) An AI model trained on large amounts of text to understand and generate human-like language. LLMs power many modern AI chatbots and writing tools. 📝 3. Prompt A prompt is the instruction or input you give to an AI system. A clear and specific prompt generally helps you get more useful results. 🎯 4. Prompt Engineering The practice of designing and improving prompts to get better results from AI models. It can involve giving context, defining the task, specifying the format and providing examples. 🔢 5. Tokens AI models process text as tokens rather than simply reading complete words. A token can represent a word, part of a word, punctuation, or other pieces of text. 📚 6. Context Window The amount of information an AI model can consider within a conversation or request. A larger context window allows a model to work with more text and information at once. 🧩 7. Fine-Tuning A process of further training an existing AI model on specific data to improve its performance for a particular task or domain. 🔎 8. RAG RAG stands for Retrieval-Augmented Generation. It allows an AI system to retrieve relevant information from external sources and use it when generating an answer. This can be useful for working with company documents, knowledge bases and other specific information. 🤖 9. AI Agent An AI agent is a system that can use AI to plan and perform multiple steps toward a goal, sometimes using external tools or applications. For example, an agent could research information, analyze it and then prepare a report. 🔌 10. API An API allows different software systems to communicate with each other. AI APIs allow developers to integrate AI capabilities into websites, apps and other software. ⚡️ 11. Machine Learning A branch of AI where systems learn patterns from data and use those patterns to make predictions or decisions. 🧠 12. Deep Learning A type of machine learning that uses neural networks with multiple layers to learn complex patterns. It is widely used in areas such as computer vision, speech and language processing. 🕸 13. Neural Network A machine-learning model inspired by the way biological neural networks are structured. Neural networks are a foundation of many modern AI systems. 👁 14. Multimodal AI AI that can work with multiple types of information, such as: ✔️ Text ✔️ Images ✔️ Audio ✔️ Video This allows AI systems to understand and generate different forms of content. 🛠 15. AI Tools You Should Know Some popular categories of AI tools include: ✔️ ChatGPT — AI assistant, writing, research and problem-solving ✔️ Gemini — AI assistant and multimodal tasks ✔️ Claude — Writing, analysis and coding ✔️ Perplexity — AI-powered search and research ✔️ Midjourney — AI image generation ✔️ Runway — AI video generation and editing ✔️ GitHub Copilot — AI-assisted coding ✔️ ElevenLabs — AI voice generation

4 learning paradigms in machine learning, explained visually: 1. Transfer Learning 2. Fine-tuning 3. Multi-task Learning 4. Federated Learning

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🔅 RAG Tuned AIs with the Cohere API Platform 📝 Learn how to develop RAG-based AI tuning with Cohere's API platform, optimizing models for various cloud environments. 🌐 Author: Ray Villalobos 🔰 Level: General ⏰ Duration: 21m 📋 Topics: Retrieval-Augmented Generation 🔗 Join Machine Learning for more courses

🧠 Understanding Machine learning algorithms

Not all Machine Learning algorithms learn the same way. Choosing the right one can make or break your model. This visual breaks down: 🔹 How ML algorithms differ 🔹 Supervised vs Unsupervised learning 🔹 Model complexity (simple → deep) 🔹 Popular algorithm families 🔹 Real-world business impact 📊 From Linear Models to Tree-Based Models to Neural Networks, every algorithm is a trade-off between: ✅ Accuracy ⚡️ Speed 🧩 Interpretability 📈 Real example: The right algorithm improved customer churn prediction by +15% accuracy - turning lost users into retained customers.

Exploring Data & AI Career Paths: Which Role Fits You? The world of data offers diverse opportunities – here’s a breakdown of key roles in AI and data science: 1️⃣ ML Engineers - Design and optimize data pipelines - Work closely with data engineers - Focus on scalable ML systems 2️⃣ ML Ops Specialists - Manage databases & data tools - Master storytelling & visualization - Ensure smooth model deployment 3️⃣ Data & Analytics Experts - Apply stats & ML modeling - Run experiments & A/B tests - Deliver business insights - Specialize in metrics & reporting Why This Matters ✅ High demand across industries ✅ Competitive salaries in tech ✅ Opportunities to work with cutting-edge AI

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🔅 How to Use AI Reasoning Models: Practical Applications with Hands-On Exercises 📝 Develop the know-how and skills to leverage OpenAI's o-Series models for advanced reasoning tasks, business applications, and scientific research. 🌐 Author: Anurag Karuparti 🔰 Level: Intermediate ⏰ Duration: 1h 59m 📋 Topics: Azure AI Studio, OpenAI API, Large Language Models 🔗 Join Machine Learning for more courses

Normalization vs Standardization: Why They’re Not the Same People treat these two as interchangeable. they’re not. 👉 Normalization (Min-Max scaling): Compresses values to 0–1. Useful when magnitude matters (pixel values, distances). 👉 Standardization (Z-score): Centers data around mean=0, std=1. Useful when distribution shape matters (linear/logistic regression, PCA). 🔑 Key idea: Normalization preserves relative proportions. Standardization preserves statistical structure. Pick the wrong one, and your model’s geometry becomes distorted.

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