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Learn about Python machine learning through corporate projects and apply it in practice

Beginner
9 chapters · 17 hours 35 minutes
English · Japanese · Korean|Audio Korean

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Skills You’ll Learn

Machine learning with Python

Use the Sklearn library to apply machine learning and specialty science techniques.

Summary of contextual machine learning algorithms

You will directly practice the algorithms to be applied according to the situations you encounter in practice.

data mining

Explore techniques for applying machine learning, prediction and classification, and clustering.

Data cleansing that cleans up data

Data can be refined to help computers learn better.

Build your own machine learning web platform

Build your own visualization web platform to determine the suitability of machine learning.

유수의 대학교/대기업에서 인정받았던 그 강의, 이제 클래스101에서 온라인으로 만나 보세요.

▶ The lecture, which was recognized by leading universities/large companies, is now available online at Class 101.


The probability that our new customers will become VIPs

How can we know from data?

Customer A, who used our service for the first time today, can we know the probability that this customer will become a VIP?


Machine learning can be used to classify data, make predictions, and obtain the results. If we have a well-built machine learning platform unique to our company, we can predict the result value just by entering data there.


Here, machine learning (machine learning) refers to the process by which a computer learns a large amount of data to derive rules and formulas. There are many attempts to apply machine learning to practice, such as learning countless amounts of data from computers to find new insights or building artificial intelligence services.



Machine learning

Isn't this an area just for experts?

Definitely not. We apply machine learning data analysis regardless of job or job group, such as HR, marketing, planning, strategy, etc., to discover insights and use them in practice.


We can use machine learning to answer questions such as:

  1. Can a new customer become a VIP? What are the chances of becoming one?
  2. Can cell image data be used to classify cancer incidence?
  3. Can the defect rate be predicted in the manufacturing process?
  4. Can we prevent customers from leaving without using our services?

Machine learning analysis is no longer just a “field for experts”!

This is an opportunity to add strength to my arguments and opinions with objective results and level up my career by equipping myself with skills that are different from others.


In what situation

What algorithms should be applied?

In practice, various machine learning algorithms are used according to different situations. It is also important to have a deep understanding of each machine learning algorithm, More importantly, 'What algorithm should be applied to what situation? ' It's. Therefore, in this class, we will use practical data to reproduce various business situations and apply machine learning techniques.


Case 1. Classification of cancer incidence using cell image data

Case 2. Preventing customer abandonment using customer contract data

Case 3. Predicting defect rates to optimize operating conditions in manufacturing processes

Case 4. Calculating VIP odds for new customers


Learn all the processes of machine learning analysis by laying the basics of machine learning algorithms, implementing them through practical exercises. In the future, we Design the process yourself when applying machine learning to workYou can do it, and you can directly make the decision “in this situation, a specific technique should be applied to this data.”


With Iris data

Have you been learning machine learning?

The purpose of this class is to go beyond understanding 'machine learning' and apply machine learning to practice. Therefore, instead of data that is far from practice, we use purchasing/medical/manufacturing/customer data to directly carry out projects and combine theory and practice.

Based on the domain insights gained from working as a data analysis consultant, we will develop the ability to directly solve problems that you may encounter in practice.


An essential step to successfully implement machine learning,

Data cleansing

If you ask a computer to learn data that is not clean, the computer will not be able to accept it properly. whereupon The prerequisite process is to clean up the data so that students can learn wellIt can be said that. It's like choosing a good textbook to study well!

I will show you in detail the process of neatly refining practical data in order for the computer to perform learning well.


Machine learning data analysis

Even building a web platform!

After using machine learning techniques using actual data, we should apply it to see if it can predict and classify other data when it comes in, right?

After using machine learning techniques, we will implement a simple web page to predict what will happen when new data comes in. Build your own data analysis web platform by adding visualization so that you can intuitively show results!


Ability to analyze data

It will be your sure-fire weapon.

수강생 후기

▶ Student reviews



To this class [Data cleansing - model selection - training - evaluation - later application] You can do it all at once, and you can quickly and easily use machine learning in practice.



Class Kit · Coaching Session


💌 1:1 coaching ticket at the data station (1 time, 2 questions)

You can ask two questions per coaching ticket.

  • 300 character answers to 1 question
  • Ask a question after selecting 2 of the 4 items below
  • If you ask questions in as much detail as possible, we will be able to answer them more accurately.


1. Career consulting related to turnover and employment

- Please fill in the following 4 pieces of information and we will respond.

- Major/ Field of interest (manufacturing, production, marketing, medical care, etc.)/Current status/ Desired career path


2. Advising on company projects/university, institution and corporate contests /private projects

- Please write down the details of your current project related to data analysis and send us an answer.

- Project name/field/progress status/question content (topic, direction, analysis techniques, PPT, presentation related)


3. Questions about code and analysis related to classes

- You can give feedback and correct the code on the lab questions provided in the class.


4. Data preprocessing coaching

- We will provide coaching on the data pre-processing currently being carried out by the project or company.

- Along with the data file attachment, please write in detail how you want the data to be preprocessed.

  • - You can get accurate feedback by filling out the image of the data file after preprocessing in the form of a simple table and send it to us.


📌 How to use coaching tickets

  1. Click [My Classes] on the Class 101 web or app.
  2. Go to [My Class], go to [Coaching Ticket Mission] and click [Get Coaching].
  3. Please fill it out in [Write a post] and send it!
  4. Coaching is based on the date the question is received, and you will receive an answer within 7 to 10 days.


🚨 Coaching vouchers can be used for 20 weeks after the date of purchase, and there is no refund for unused use within the period.

📢 The package is subject to some changes, and we will be fully informed if there are any changes.

Curriculum

Creator

DATA STATION

DATA STATION

Hallo

I am doing data analysis, lectures, and corporate consulting in the business data station It's.

Currently, large companies are conducting data analysis lectures and consulting for new employees and employees.


● Major career

• Advisor Professor, Data Innovation Group, POSCO Institute of Talent Creation (2018.12 to present)

• SAS JMP Korea Official Training Partners (201803 to present)

• Chief Researcher, INNOVALUE PARTNERS CO., LTD.

• Korea University, Master of Big Data Convergence


● Training results

• POSCO, “Youth AI - Big Data Academy” Project Course Professor

• LG Innotek, SSBD data analysis training

• Hanwha Total, big data education and consulting

• Korea Hydro & Nuclear Power, Data Analysis Training

• Samsung Multi-Campus, Data Analysis/ Machine Learning Training Using Python

• Hyundai NGB trains incumbents in data analysis

• Special Lecture on University and Graduate School Data Analysis

데이터 스테이션

데이터 스테이션

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Notes on Copyright Protection

  • All videos and materials included in the class are protected intellectual property under relevant laws.
  • You may face legal action if you copy, distribute, transmit, modify or edit the videos or materials included in the class without permission.
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