Learn both the concepts and practical techniques of machine learning using Python and Tensorflow.
We will hone your skills by working together on five different projects based on key topics.

Learn the concept of linear regression, the foundation of machine learning.

Learn the basic concepts of linear regression and use machine learning to analyze and predict Boston Marathon data using Python TensorFlow.

Using about 80,000 Boston Marathon big data, machine learning learns the records up to 30 km by selecting the desired runner. Then, the records of the remaining sections of 35, 40, and 42.195 km are predicted using linear regression and compared with actual data. We learn the concepts and techniques of solving linear regression problems using TensorFlow.

Understand multi-variable regression problems and learn how to solve them.

Learn the basics and solutions to multi-variable regression problems and predict your and your friends' Boston Marathon completion times by inputting gender, age, and pace data.

Check out the results of machine learning that predicted the completion record by using the Multi Variable Regression technique, which inputs gender, age, and pace values and learned about 80,000 Boston Marathon big data. Even without running the marathon, it learns machine learning and predicts the record based on the analyzed data.

Understand Multi Variable, Output Regression problems and learn how to solve them.

This time, you will learn the basics and solutions of regression problems that input multiple variables and output multiple outputs. If you input gender, age, and pace data, you can predict not only the Boston Marathon completion record but also the expected records for each 10, 20, and 30 km section.

Using the Multi Variable Input and Multi Output Regression technique, the machine learning that receives gender, age, and pace values as input and learns from about 80,000 Boston Marathon big data predicts not only the completion record but also the records for each 10, 20, and 30 km section.

Understand the concept of Binary Classification, the basis of Logistic Regression/Classification, and learn how to solve it.

Before you apply for the marathon, enter your gender, age, and pace data to see if you qualify. We will predict whether you qualify based on your past Boston Marathon records.

Using the Binary Classification technique, which is the basic of Logistic Regression, we will predict whether or not a marathon will qualify. We will also learn the technique of adding the qualification status to the existing Boston Marathon data of about 80,000 cases using advanced Python pandas technology.

Understand the concept of Multinomial Classification in Logistic Regression/Classification and learn how to solve it.

Before you enter the marathon, enter your gender, age, and pace information to check your expected time class. We will predict your time class based on your past Boston Marathon times.

Using the Multinomial Classification technique of Logistic Regression, we divide your marathon completion record into three grades: 'Outstanding (>25%)', 'Average (25~75%)', and 'Below (<75%)', and predict your expected grade.

Please look forward to various lectures on deep learning, IoT, etc. using machine learning in the future.
The materials and program sources used in the lecture can be obtained from the Creapple (www.creapple.com) website, a knowledge learning center that I run.

Taking a course on the basics of Python and data visualization and analysis will be of great help in carrying out your project.

Python 100-minute core lecture
The core and fundamentals of Python
Once you master the skills, it will be a great help in other courses.

Python Data Visualization Analysis Practical Project

Machine learning using Python's Pandas, Matplotlib, and Seaborn.
It can be used in various projects such as deep learning.
Learn data visualization and analysis techniques all at once.