Building OCR that actually works in real-world scenarios, here's how to do it.

🚀 Practical AI Anomaly Detection with Databricks! 💡 Stop using expensive and slow models! From large-scale data optimization to real-time deployment, complete an anomaly detection AI that can be used immediately in corporate practice.

By the end of this course, you will be able to implement Apache Spark projects in your organization.

Create your own AI chatbot using Python's basic syntax and libraries. Learn through a step-by-step process of completing 5 projects, including PDF document-based RAG, and deploy them as web services.

This course will help you become a deep learning-based computer vision expert needed in the field through in-depth theoretical explanations of Object Detection and Segmentation, along with practical examples that can be immediately applied in real-world work environments.

In this course, you will learn how to operate PostgreSQL and explore methods for integrating it with Python.

This course explains the basic concepts of LLM and GPT in an easy-to-understand way for everyone. Students can directly use the GPT API to create chatbots and run them on the web using Streamlit. By experiencing everything from basics to hands-on practice, you can solidly establish your first steps in AI utilization.

Let's create a web app that uploads an image and uses OpenCV to process it and detect faces.

We implement the latest Transformer-based PixArt and lightweight adaptation SANA step by step from theory to code. Building on DDPM·DDIM·LDM·DiT covered in Parts I·II, we complete hands-on practice including text encoder integration, samplers (DDIM/ODE), v-prediction/CFG tuning, and small-scale data style fine-tuning.

This course is a practical pre-training program designed to help you quickly complete the core technologies needed for **smart mirror** production in the form of "pre-missions." Rather than simple feature explanations, it aims to reach a level where you can immediately create an integrated demo (PoC) in the field. The course requires completion of the following 4 components: LLM API-based conversational response module Including role separation (system/user), conversation history maintenance, retry/timeout, and log handling, you'll learn patterns for creating short, card-style summary responses suitable for smart mirrors. Face recognition + filter application with MediaPipe FaceMesh Extract facial landmark coordinates and implement a PoC that tracks visual filters like sunglasses/masks/stickers in real-time. Raspberry Pi ↔ Arduino serial communication (Python) Transmit Arduino sensor values via serial and perform reception, parsing, and error handling on Raspberry Pi using Python (pyserial) to first complete the system backbone of "sensor → Pi → (UI/storage)" flow. Smart mirror frame/hardware 3D modeling Considering display, Pi, power supply, cables/heat dissipation, complete an assemblable frame (front/back, brackets/screw holes, etc.) as STEP/STL deliverables. Additionally, optional sections allow expansion based on team capabilities, connecting step-by-step: Weather data input on Pi, Firebase/Supabase DB integration, Responsive web UI (mobile/desktop), AWS deployment basics. As a result, upon completing the course, students will have not just "individual technology pieces," but an integrated portfolio (AI response + face recognition + sensor communication + hardware design) ready for immediate demonstration at makerthons.

From core theories of deep learning and CNN to various CNN model implementation methods, and practical deep learning development know-how through real-world problems, If you want to become a deep learning CNN technology expert based on Pytorch, join us in this lecture :)

This course is a fun project that learns theories and processes images and videos with OpenCV (Open Source Computer Vision Library) and uses deep learning modules (dnn, Deep Neural Network) to identify and recognize faces, eyes, and various objects.

Do you have data but feel lost on how to read and process it in Python? Don't worry. Pandas can handle it with its magic. Pandas is the most powerful, efficient, and useful data processing library. Skill-UP your data preprocessing with Pandas! Insights galore!

Learn LangChain's basic concepts and usage with Python. Hands-on practice will be conducted primarily in a Google Colab (colab) environment, with materials provided via Github.

This course is a step-by-step learning process that covers the principles of image and object recognition using deep learning, from fundamentals to the latest models. - Building Foundations: Understanding tensors and basic neural network structures with PyTorch - Understanding Images: Learning computer vision concepts, image data structures, and augmentation techniques - CNN Model Training: Hands-on image classification practice with Convolutional Neural Networks (CNN) using datasets like CIFAR-10 - Transfer Learning: Leveraging pre-trained models to achieve fast learning with limited data - Object Detection: Understanding and practicing with the latest object detection models including R-CNN, YOLO, SSD, DETR - Segmentation: Experiencing pixel-level object segmentation through U-Net and Mask R-CNN

AI Artificial Intelligence, it goes without saying that mobile is the trend. But how do you learn machine learning and deep learning and use them on mobile? Now, let's learn useful mobile Computer Vision projects that are used in daily life.

This course focuses on developing practical visualization skills that can be immediately applied in the workplace by utilizing Seaborn, a core tool for data visualization, and plotly/Dash for building interactive dashboards. Through various hands-on exercises, you can enhance both your ability to create insight-driven visual materials and implement web-based dashboards.

When researching AI or conducting a project using it, basic paper implementation is essential. Let's upgrade our practical skills by implementing an actual paper through this lecture!

Here are some key competencies that are often overlooked by others in the fiercely competitive and complex semiconductor process job market.

You can implement hands-on practice of infinitely generating cryptocurrency trading signals automatically using ChatGPT and Python, and backtesting and validating them. Additionally, you can learn how to combine all the strategies automatically generated by AI into a single strategy and apply it to actual trading.

Trying to get a job, but it's not easy, is it? I, a liberal arts non-major and academy graduate, will share the 'effective portfolio' that I personally struggled for 'n' months to widely open the door to employment.

# Silicon Valley Internship That Won't Get Cut Even in the AI Era & Complete Course on 30 Big Tech System Designs & Open Source Practical Contributions All students who submit course missions can apply for an AI startup internship in Palo Alto, USA, with exemption from document screening and guaranteed interview opportunities. This course involves designing and scaling 30 real-world system designs based on big tech cases including Netflix, Tesla, Meta, and Tinder. Finally, you'll design your own service from architecture → DB → scaling, contribute to an actively operating open source AI coding agent project (achieving 1.8k downloads per week) to complete your portfolio, and finalize your resume.

Hi Guys. Welcome to my Full AI Course 2026: ChatGPT, Google Gemini, Midjourney, Firefly. In my course you will learn everything about all AI Tools. I have created video lessons on every feature of these AI tools. You will get to see the real practical implementation of how to use every feature of this tools.