Cognitive Class

Most vector database advice concerns the database: which index, which distance metric, how to shard. But what about the step before that? In this guided project, you’ll learn how vectors can be represented, stored, and optimized without relying on specialized binary formats. You’ll explore the trade-offs, techniques, and practical patterns for building efficient vector storage systems, with a focus on simplicity, performance, and scalability. Perfect for developers working with embeddings, vector databases, and AI-powered search. Finish the course with a formatter you wrote and tested, plus an evaluation harness that works on data this course has never seen. Ready to start learning? 🚀 Enrol here 👉 cognitiveclass.ai/courses/op… #VectorStorage #Embeddings #RAG #Python #MachineLearning #IBM

When wrong answers from AI sound just as confident as right ones, testing your own AI apps becomes harder than it looks. In this guided project, you’ll learn to build a Python program that uses one AI model to grade another AI’s answers, testing it on a support chatbot for a fictional cloud storage company. Along the way, you’ll learn that a judge’s scores mean nothing until you check them, and that most judge issues can be fixed by writing better instructions, not by switching to a bigger model. By the end of this project, you'll be able to: - Build a golden set of answers with trusted human scores to test against - Write a rubric that has an LLM score answers against the source facts - Detect length bias and remove it by improving the rubric You’ll walk away with a working eval harness you can adapt to your own AI apps. Ready to start building? 🚀 Enrol here 👉 apps.course-dev.skills.netwo… #AI #LLM #Python #MachineLearning #IBM

You don’t need a physical server to run your first one. A cloud virtual machine gives you a computer you can reach from anywhere. Getting one running comes down to four steps, and the order matters: - Prepare your cloud workspace. Confirm your IBM Cloud account has access to the resources a VM needs. - Create your SSH key. The private key stays on your computer. The public key goes into your cloud configuration. - Provision your virtual machine. Create the instance with the SSH key attached, so you can log in later. - Connect. Get the VM’s IP address, open your terminal, and connect using SSH. Step 2 comes before step 3 for a reason. If the key doesn’t exist when the instance is created, you’ve built a server you can’t get into. These steps come from Module 4 of IBM’s Foundations of IT Systems Administration course on Coursera. If you’re starting out in IT support or systems administration, it’s a good place to begin: ibm.biz/~6W7cJFW6Q hashtag#CloudComputing hashtag#SysAdmin hashtag#ITCareers hashtag#IBMCloud

You don't need to own a quantum computer to use one. IBM's Applied Quantum Computing course is a beginner-friendly next step in learning quantum software. You write quantum circuits in Qiskit, test them, and then run them on IBM's real quantum hardware. What the course covers: • Understanding quantum circuits • Building and visualizing circuits in Qiskit • Applying quantum algorithms to real use cases • Running your work on IBM's real quantum hardware It's self-paced, so you can learn on your own schedule. Enroll here: ibm.biz/~9LN9HRBXe #QuantumComputing #Qiskit #IBM #Upskilling

Understanding whether text expresses a positive or negative opinion is one of the most widely used applications of AI today. The good news? You don't need to be a machine learning expert or own a GPU to get started! IBM SkillsNetwork’s Build a Sentiment Analyzer with a Pre-Trained AI Model course teaches you how to use a real, pre-trained AI model to classify text as positive or negative. In under 30 minutes, you’ll learn to load a model with just a few lines of Python and test it on your own text. By the end of this guided project, you’ll be able to: - Use a pre-trained AI model to classify the sentiment of text as positive or negative - Interpret a model's confidence score for its predictions - Apply the model to your own custom text - Compare results across two different pre-trained models Ready to put AI into practice? Enrol here 👉 cognitiveclass.ai/courses/bu… #AI #MachineLearning #Python #IBM #ArtificialIntelligence

Not every prediction works the same way. Predicting a house price and predicting whether a customer will leave need different approaches. Choose the wrong model and your results can mislead you. Start with one question: what are you predicting? A number, like a price? Use linear regression. A category, like yes or no? Use logistic regression. Here are 3 regression models worth knowing: 1. Simple linear regression: one input, one numerical prediction. Example: predict a car’s price from its mileage. 2. Multiple linear regression: several inputs, one numerical prediction. Example: predict a house’s price from its size, location, and age. 3. Logistic regression: one or more inputs, the probability of belonging to a category. Example: predict whether a customer will cancel a subscription. Want to go deeper? Explore Machine Learning with Python from IBM on Coursera: ibm.biz/~oX6zmIzad #MachineLearning #Python #DataScience #Regression

Responsible governance is increasingly the difference between AI that scales and AI that stalls. AI governance counts if it holds up when someone asks how a decision was made. Here are 4 checks worth running on any AI policy: - Know whether you’re citing a voluntary framework, a certifiable standard, or binding law. - Make every risk tier trigger real obligations with an owner. - Test whether human review can keep up at real volume. - Document the reasoning behind each decision, including gaps. IBM’s AI Governance, Risk, and Executive Communication Course teaches you to apply AI governance and ethics frameworks to an initiative so that responsibility is designed in rather than added after an incident. Ready to start learning? Enrol here 👉 ibm.biz/~si0H77qhv #IBM #Coursera #AIGovernance #ResponsibleAI #AIRisk #AIEthics

A device can have security gaps before anyone ever attacks it. Outdated software, unused services, weak passwords, and loose firewall rules all leave openings. Closing them is everyday work for security professionals, and it’s a good place for beginners to start practicing. Here are the four techniques in the graphic: 1️⃣ Patch. Security updates fix known weaknesses attackers could exploit. If you skip them, those weaknesses stay open. 2️⃣ Harden. Configure the device to have fewer weak points: disable services you don’t use and uninstall software you no longer need. 3️⃣ Filter traffic. A firewall uses rules to allow or block network traffic, such as unauthorized inbound connections. 4️⃣ Protect accounts. Password policies require strong, unique passwords and multi-factor authentication to reduce unauthorized access. To try it yourself, pick one device you own and check all four: Are the latest updates installed? Is there software you can remove? Is the firewall on? Is MFA turned on for your main accounts? These ideas come from IBM’s Introduction to Cybersecurity Essentials on Coursera. Check it out now: ibm.biz/~YPD7RcGzR #Cybersecurity #InfoSec #CareerChange #LearningAndDevelopment

How do you build the skills at the core of AI leadership strategy? By learning how to turn broad business goals into a focused, defensible AI strategy Knowing how to prioritize the right use cases and prove their value is what sets great leaders apart. Here are a few key terms to know when connecting business goals to AI priorities: - Value-Feasiblity-Risk Matrix - Weighted Composite Scoring - Defensible Prioritization Want to learn more about how to build your AI leadership strategy? Enrol here 👉 ibm.biz/~mOViOBpTa #ai #aileadership #coursera #ibm #ibmcourses

Before you can lead AI, you need to understand it in business terms. That means identifying real opportunities and evaluating where AI can create measurable value. IBM’s Lead AI Transformation Certificate Course will help you build the AI literacy every modern leader needs. You’ll learn what today’s AI can and cannot do, including generative AI and agentic systems that can plan and take action on their own. Here are a few key terms to know as you build your core AI literacy: - Generative AI - Predict versus generate - Agentic AI Want to learn more about how to build your AI leadership strategy? 🔗 Enrol now: ibm.biz/~sLFxovAzU #AI #AILeaders #IBMCourse #Coursera #AILiteracy #AICourse