Inflearn - Lifetime Career Platform

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We will help you easily understand the core concepts of machine learning and acquire the ability to implement practical machine learning applications by moving away from theory-based machine learning courses.

This course covers everything from the basic concepts of recommendation systems to the principles of applying deep learning. Learn about various recommendation algorithms such as collaborative filtering, content-based filtering, and hybrid recommendation systems, and develop practical skills for developing recommendation services!

You can acquire an overview of machine learning and deep learning, how to use basic tools, and the Python language knowledge necessary for learning in a short period of time.

Learn how to build deep learning models directly through intuitive and Pythonic PyTorch. This reflects the latest PyTorch version.

Do you want to take your first step into the world of data? Learn machine learning and deep learning, the core technologies of AI, with Python. This course will guide you step by step, from the basics of machine learning and deep learning to practical applications. Traditional machine learning and deep learning are based on many of the same principles and technical systems. Therefore, this course does not separate the two into separate subjects, but rather organizes them into one connected course so that beginners can increase their understanding of machine learning as a whole.

This course is designed to upgrade your skills as a practical machine learning development expert by implementing the machine learning model of the Home Credit Default Risk competition on Kaggle.

You can create a deep learning analysis environment in the cloud using Docker. When Docker images managed by Google, MS, etc. are combined with the cloud, you can work with the latest deep learning analysis methods on your computer.

This lecture is designed to give you a general understanding of AI without overwhelming you with difficult topics like mathematical formulas. It's perfect for learning with a light and easy mindset.

If you want to be recognized as a machine learning expert based on large-scale data, from understanding the core framework of Spark machine learning, to SQL-based data processing through difficult practical problems, to data analysis through business domain analysis, and to the ability to implement optimized machine learning models, please join this course.

This course provides essential knowledge needed to understand the structure and operating principles of various artificial neural networks and create good models.

This is a lecture where you easily learn important mathematical concepts used in games. People who know math well take that knowledge for granted, so they cannot teach under the assumption that you know nothing. I will explain from a beginner's perspective, truly treating you as someone with no prior knowledge.

The first step in learning AI: We've created the perfect curriculum for AI beginners! The first step: getting familiar with AI foundational theory! Learn basic machine learning concepts with various examples and take quizzes to review the theory!

Unlike existing lectures, this course has been newly designed based on the instructor's initial failures in learning machine learning, enabling easy understanding and application to real-world problems.

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!

Time is constantly flowing, and data is constantly accumulating. In this dynamic world, time series data plays a vital role in every aspect of our lives. From the volatility of financial markets to subtle signals of climate change, time series data captures it all. Now learn how to interpret and leverage this powerful data using the power of Python and deep learning!

JavaScript, which is known to all web developers, now explore the world of machine learning with this powerful language! This course learns how to build and deploy machine learning models using JavaScript and the powerful machine learning library Tensorflow.js. It guides you step-by-step through all the technologies required to develop web-based machine learning applications. Through this course, learners will systematically understand the core principles of machine learning. In addition, you will learn how to develop deep learning models using JavaScript and Tensorflow.js APIs, how to utilize transfer learning based on pre-trained models, and how to apply all this knowledge interactively in a browser environment.

All the recent amazing achievements in the field of artificial intelligence have been announced in the field of reinforcement learning. We have covered reinforcement learning technology, which is revolutionizing true artificial intelligence technology such as robots, autonomous driving technology, and human-like machines, from the basics to the advanced level, in an easy-to-understand way for beginners.

Learn the basics of machine learning step by step through various Kaggle examples, and learn vivid project experiences and practical tips from hard-to-access field machine learning engineers all at once.

We will introduce how to create deep learning models and CNN models using artificial neural network algorithms.