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This course contains the use of artificial intelligence.A practical, illustration-rich course that teaches you how to build software with AI agents without creating a fragile “house of cards” codebase.If you’ve ever shipped a quick prototype with an AI assistant and then watched it fall apart the moment you tried to scale it, integrate it, or maintain it, this course is for you. Spec-driven development is the discipline that turns AI from a clever code generator into a predictable engineering capability. You’ll learn how to make intent the system of record, how to convert that intent into executable plans and tasks, and how to keep AI agents aligned with your architecture, standards, and quality bars.What You GetVideo explainers: ~3 hours 45 minutes of content with clean illustrations built directly from the lesson content.Quizzes: Every lesson includes a quiz (8-10 questions) to lock in concepts and apply them immediately.Who This Course Is ForDevelopers: At any level who want consistent results from AI coding tools.Technical Project Managers: Who need a repeatable workflow to guide teams from requirements to production without ambiguity, drift, or hidden assumptions.What You’ll Be Able to Do After the CourseYou’ll know how to replace improvisational prompting with a four-phase gated workflow (Specify - Plan - Tasks - Implement) that produces code you can actually review, test, and maintain.Write Specs: Create specs that AI can follow literally.Define Constraints: Prevent agent overreach using explicit boundaries.Set Up Governance: Make quality the default rather than a late-stage rescue mission.Extend SDD: Apply SDD to real-world operations like runtime diagnostics, drift detec

“This course contains the use of artificial intelligence”Unlock the power of brain–computer interfaces (BCIs) by learning how to decode human intention directly from EEG signals using EEGNet, one of the most widely adopted deep-learning models in neurotechnology. This hands-on course teaches you how to build a complete Motor Imagery Classification pipeline—from loading real EEG datasets to training, evaluating, and deploying a fully functional model.You will work extensively with the BNCI-Horizon 004 (BCI Competition IV 2a) dataset, a gold-standard benchmark used in academic research and industry. You’ll learn how to perform signal preprocessing, including bandpass filtering, epoch creation, and standardization, followed by constructing a full training workflow using TensorFlow/Keras. The course also covers model optimization, performance evaluation, and interpreting neural patterns that distinguish left-hand, right-hand, feet, and both-hands imagery tasks.Beyond training EEGNet, you will gain practical experience in real-time BCI concepts, enabling you to extend your model toward interactive control systems. The step-by-step practical labs ensure you not only understand the theory but also build a working BCI system from scratch.By the end of this course, you will be able to confidently preprocess EEG data, train and validate deep-learning models for motor imagery, and understand how BCIs transform neural activity into real-world applications such as prosthetics, gaming, assistive robotics, and neurofeedback systems.This course is ideal for anyone interested in AI, neuroscience, machine learning, or human–computer in

Core Concepts of Generative AI is an introductory–to–intermediate course designed to equip learners with a strong foundational understanding of generative artificial intelligence—its theories, methods, tools, and real-world applications. This course demystifies how modern AI systems create text, images, audio, and other content, while helping students develop the technical intuition needed to work confidently with generative models.Learners will explore the evolution of generative AI, from early probabilistic models to today’s large language models (LLMs) such as GPT, Claude, Llama, and diffusion-based image generators like Stable Diffusion and Midjourney. Through hands-on exercises, students will practice prompt engineering, fine-tuning, evaluation methods, and responsible AI principles.By the end of the course, students will understand how generative AI works, how to use it effectively, and how to apply it to real-world tasks across industries such as education, marketing, software development, and creative content production.Learning OutcomesUpon completing this course, learners will be able to:Explain the fundamental concepts behind generative AI and machine learning.Understand the architecture and training principles of large language models and diffusion models.Understand generative AI tools.Evaluate generative AI outputs for accuracy, bias, and safety.Understand model fine-tuning, and embeddings.Apply generative AI to solve practical problems through mini-projects.Topics CoveredIntroduction to Artificial Intelligence Machine LearningLarge Language Models (GPT, Llama, Claude, Gemini)Transformers Attention MechanismsDiffusion Models for Image GenerationFine-tuning and Masked Lan

Push your game development skills further by creating a Stardew Valley style game! In this course you will learn about a lot of advanced Godot features while making a fun game. This will give you amazing tools to create more complex games in Godot with ease. You will learn about: Shaders to enhance the look of the gameTools to add interactive elements in the editorResources to manage data Performance monitor lets you diagnose bottlenecks in your gameAnimation trees to create sophisticated animation logicAutotiling to easily create massive levelsBy the end of the course, you will have gone from a Godot novice to a much more seasoned developer; especially for professional games these concepts are essential. To learn these concepts, I will go through every single step and explain concepts in great detail. Everything will be explained in a hands-on way with multiple examples and I will answer questions daily. If you are just starting with Godot and you want to push ahead this course is perfect for you. We will take the basic Godot concepts, add useful tools to make game development easier, and expand on all of it. That way you not only learn new ideas you also reinforce existing knowledge.

Are you tired of machine learning tutorials that stop at theory? Ready to build something real that you can actually show employers?This course takes you beyond the basics. You'll build a complete, production-ready text classification system from scratch—the kind of project that gets you hired.Here's what makes this different: You won't work with toy datasets like Titanic or Iris. Instead, you'll train a machine learning model on 47,692 real social media posts, achieving over 81% accuracy in detecting cyberbullying. This is the scale and complexity employers expect.But we don't stop at training. Most courses teach you to build models in Jupyter notebooks, then leave you wondering "now what?" This course shows you the complete workflow—from raw data to a live, deployed application anyone can access on the internet.You'll master the essential skills data scientists use every day: preprocessing messy text data, extracting meaningful features with TF-IDF, training classification models with scikit-learn, and evaluating performance with industry-standard metrics. You'll work with Python libraries including NumPy, Pandas, Matplotlib, and Seaborn to analyze data and create professional visualizations.Then comes the part that separates you from other candidates: deployment. You'll build an interactive web dashboard using Streamlit—no HTML, CSS, or JavaScript required—and deploy it to the cloud completely free. Your application will have a real URL you can share in job interviews and include in your portfolio.What truly sets this course apart is our focus on ethical AI. In 2025, companies aren't just looking for people who can build AI—they need people who can build it responsibly. You'll learn to detect and mitigate bias in machine learning systems, design human-in-the-loop workflows, and make AI decisions transparent and accountable. These are the skills that make you invaluable.This isn't just another course—it's your bridge from Python developer to AI/ML engineer. Wh

This course will teach you the very basics of Creating a simple Machine Vision System, but more specifically how to build a Machine Vision System using Keyence's IV 2 Vision Sensor with built in Artificial Intelligence. This sensor brings the Machine Vision control from the hands of a certified Machine Vision Engineer to the hands of any Novice/Beginner wanting to get started in the Machine Vision world. This course will teach you the very basics that you will need to know to get your feet wet in the world of Machine Vision. Then we will go right into the basic components that make up Keyence's IV 2 Vision System. With all of this information, you will be able to create your very own Machine Vision system for inspections using the Keyence IV 2 Vision Sensor system. This course will only go so far as to fully demonstrate one way to use the Keyence IV 2 Vision Sensor System. You can use this system in all sorts of applications but, your personal project will fully dictate what you will need for your vision application. Starting with the Keyence IV 2 is certainly a great place to start, especially for beginners and experts alike. This system brings true simplicity to what once was an overwhelming task to setup.