혼자 공부하는 머신러닝+딥러닝

This lecture covers the contents of the Amazon bestseller <Hands-on Machine Learning 2nd Edition>. Using the representative machine learning library, scikit-learn, you will learn various machine learning algorithms and evaluation methods. Also, using the most famous deep learning libraries, TensorFlow and Keras, you will learn theories and practices from artificial neural networks to reinforcement learning. This lecture is not yet complete. One to two lectures will continue to be added every week.

Wake up your sleeping coding brain. From Python basics to machine learning, all in one book! This book is an introductory computer science book based on MIT lectures, so even beginners who know nothing about programming can easily read it. Wake up your sleeping coding brain with topics essential for introductory programming, such as computational thinking and simple algorithms. Through brain-boosting problems provided in each chapter, we will look at practical topics such as data visualization, simulation, data calculation techniques, and machine learning.

This lecture guides readers to deep learning with the most appropriate pace and straight direction, one step from concept to formula and one step from coding. In addition, there are over 100 graphs, illustrations, and diagrams, so you can easily and quickly accept abstract concepts. Another unique feature of this lecture is that you can start practicing right away by simply accessing a web browser without installing a program. After comfortably understanding the theory, you can directly code and conquer four representative deep learning problems with your eyes, so it is not lacking as a textbook for deep learning. The concepts or terms that you must go over are reviewed twice in the ‘Wait! Let’s move on to the next’ corner in the middle of the text and the ‘Memory Card’ corner at the end of the chapter to increase the learning effect. Let’s quickly overcome deep learning head-on with ‘Do it! Introduction to Deep Learning.’

This course teaches the “core concepts” of deep learning required to work in the deep learning field and the practical skills required to perform actual deep learning projects through hands-on training using PyTorch.

Build your own powerful stock analysis model that predicts not only the S&P 500 and QQQ ETFs, but also individual stocks, by using AI to analyze over 40 economic indicators and stock price data!

In the [Learn Machine Learning with AutoKeras] lecture, you can learn about AutoKeras, which was developed from the Keras library previously used in machine learning, and use it directly through examples.

From the basics of machine learning & deep learning! Creating and utilizing classification/regression/clustering/artificial neural networks with Python

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.

Through this course, students will learn various reinforcement learning theories and implement them themselves, as well as create a reinforcement learning environment to test the reinforcement learning algorithm implemented using Unity Machine Learning Agents.

Learn machine learning concepts, artificial intelligence learning principles, and mathematical principles for model implementation to fully utilize deep learning!

Still managing ML experiments manually? 🤔 Automate everything from experiment tracking to model deployment with MLflow and dramatically boost development productivity! A must-have skill for data scientists and ML engineers 💡 Start now! 🚀

Let's analyze and visualize data and learn machine learning concepts using Orange 3!

Covers linear algebra, which is essential for machine learning/deep learning research.

Ace the Big Data Analysis Engineer certification, a must-have credential that's far from easy to self-study, in a short amount of time by mastering the data analysis tool R, known for its ease of use!

We will learn the core technologies of the 4th industrial revolution, such as ICBM (IoT, Cloud, Big Data, ML) and related technologies such as self-driving cars and metaverse, from the basics to develop the ability to prepare for future technological changes.

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.

Create AI data analysis and modeling results into a web program.

Learn about job analysis related to Data Science (DS) and Artificial Intelligence (AI) and the learning and employment roadmap for building a career in related jobs.

The "Pytorch Projects Explained in One Code" edition implements practical deep learning projects using image, video, and natural language data. It focuses on code-centered learning rather than theory, to understand the structure and flow of models.