[AICE] Data Analysis and AI Modeling with Python

1. Mock exam problem-solving for passing AICE Associate, Korea's only state-certified AI certification. 2. A course featuring 12 mock exam sessions with the same question types as the actual AICE Associate exam.

You can benchmark manufacturing innovation methods for semiconductors and run machine learning and deep learning with semiconductor data without coding.

1. Python coding study to obtain the AI certification, AICE Associate 2. Step-by-step Python coding study from basics to applications 3. Lectures by Professor Joongheon Kim of Korea University, a scholar in the field of AI

Are you familiar with Python now? Python is the most widely used language for collecting, analyzing, and utilizing data. If you want to use Python for data science, today's lesson will be really helpful. We will install and use Python modules and packages, aka libraries, related to data science.

This is a hands-on course where you'll build an AI-based autonomous driving car using Raspberry Pi and various sensors.

This is a lecture where you can learn Deep Learning using TensorFlow. Learn linear regression, logistic regression, and softmax regression models, and finally create an MLP model that can classify fashion images!

If you ask an AI to "build a model with this data," the first few lines might look plausible, but as soon as things get slightly complex, it starts using the wrong columns, introduces data leakage, and results in a model that performs worse despite a higher score. This course does not teach you better prompts. Instead, it covers how to directly design a loop (harness) where an agent understands data, plans, writes code, validates its own results, and backtracks when wrong. After manually walking through the five-piece loop of DS-STAR, you will assemble components one by one, from data profile cards, hooks, and skill hierarchies to independent validators and ablation.

Learn about the basics of Python required for data analysis, the data analysis framework (pandas), and the data analysis process.

This introductory course for AI engineers is designed to provide a brief hands-on experience of the entire process, from data processing and model development to cloud, MLOps, and ethical considerations. It focuses on building practical skills by helping students understand the process of connecting models to actual services, rather than just creating them. The course includes hands-on exercises and examples so that even those learning AI for the first time can easily follow along.

Customer analysis completed with Excel and Orange, from cross-selling to A/B testing and churn risk management all at once

Are you anxious every time you deploy an AI agent? Based on experience with major domestic corporations and global big tech companies, we will show you how to systematically measure and improve agent quality using LangSmith.

Have you studied Python syntax but felt lost when it came to actually handling data? You know you need to learn NumPy and Pandas, but if you've been wondering where to start and how to connect them, this PART 2 is the answer. In PART 2 of our 50-lecture data analysis curriculum, you will experience the step-by-step process of loading, cleaning, processing, and statistically interpreting real data. You won't just learn libraries and syntax; you will establish criteria for which tools to choose in specific situations. PART 2 is the turning point where you move from simply knowing Python code to actually being able to handle data.

📢 "What happens when AI is added to a factory?" 🤖 The era of traditional manufacturing is over; we are now in the age of AI- and data-driven smart factories! For those who have been wondering "what exactly is a smart factory?" and those who want to know "how AI technology is actually applied on the manufacturing floor," we have prepared a very easy-to-understand and clear introductory lecture.

This course covers everything from the basics to hands-on practice of AI platforms, and is a program where you can learn AI image generation technology. Learners can understand the fundamental concepts of AI and develop the ability to generate AI images using various tools and technologies. Through this course, you can enhance your understanding of the AI field and learn how to directly generate AI images in real-world applications.

You will thoroughly understand the principles!

In this course, you will learn the basics of Raspberry Pi and Python and practice creating simple but essential self-driving car and remote home monitoring projects.

Feeling overwhelmed by where to start with complex data analysis? I will clearly guide you through how to utilize R and AI Tutors using the practical know-how I've built up.

Let's learn how to collect data from the web and create a supervised learning model for machine learning!

Numpy and Pandas are the foundation of data analysis and the backbone of AI frameworks, and I've packed this course with the core practical know-how I've accumulated through my work and teaching experience. I've designed it with a hands-on approach so that marketers, planners, and beginner analysts can easily follow along with data cleaning and processing—the areas where they most often get stuck. Through this course, anyone can develop the skills to handle data and produce analysis results on their own, without needing help from developers.

You will learn techniques that can be applied in various fields of IT security, starting from the introduction to Python programming.