인프런 영문 브랜드 로고
인프런 영문 브랜드 로고
BEST
AI

/

Deep Learning & Machine Learning

Kaggle Machine Learning for Beginners Learn from Industry Professionals - ML Engineer Practical Tips

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.

(4.5) 20 reviews

277 students

Kaggle
Machine Learning(ML)
EDA
Thumbnail

This course is prepared for Basic Learners.

What you will learn!

  • Concept of machine learning

  • How to improve machine learning model performance

  • How to use Google Colab

  • Machine learning libraries - scikit-learn, xgboost

  • Machine Learning/Data Analysis Library - Numpy, Pandas

  • Data visualization library - matplotlib, seaborn

  • How to proceed with a machine learning practical project

Learn machine learning basics with various Kaggle examples.
All the practical tips from working engineers at once! 😀

📌 Brief introduction to the curriculum

0. What is Machine Learning (ML)?

  • Let's get a firm grasp on the concept of machine learning.

1. Simple practice environment that does not require complex installation

  • Introducing Google Colab

2. Introduction to scikit-learn & My first machine learning model

  • Let's predict weight based on height

3. Introduction to Kaggle and Kaggle Competition

4. Linear Regression Algorithm (Ridge, Lasso, ElasticNet) & How Much is My House Worth?

  • Let's predict Boston real estate prices

5. Random Forest, a popular and high-performance predictor

  • Let's predict whether it will rain tomorrow or not using a machine learning model.

6. XGBoost, the algorithm favored by Kaggle winners

  • Let's predict the occurrence of stroke using XGBoost

7. Practical stories from working machine learning engineers

  • Experience the daily life of a machine learning engineer

8. DS/ML practical tips from practitioners

Kaggle Machine Learning for Beginners Learn from Industry Professionals - ML Engineer Practical Tips

  • Through Kaggle projects, you will acquire knowledge related to machine learning (machine learning libraries - Numpy, Pandas, Matplotlib, Seaborn, Scikit-Learn, xgboost, etc.) and learn how to apply machine learning to practical work.
  • The course is structured so that you can learn the basics of machine learning step by step through various Kaggle examples, and also learn practical tips for machine learning projects from machine learning engineers at large IT companies.

Recommended for
these people!

Who is this course right for?

  • For those who are new to machine learning

  • Anyone who wants to learn data analysis techniques

  • Anyone who wants to get a job as a machine learning engineer

  • Anyone curious about the work process after getting a job as a machine learning engineer

  • Anyone who wants to get practical tips from machine learning engineers working at large IT companies

Need to know before starting?

  • Basic Python experience

Hello
This is

7,653

Students

496

Reviews

328

Answers

4.6

Rating

26

Courses

Curriculum

All

60 lectures ∙ (7hr 19min)

Published: 
Last updated: 

Reviews

Not enough reviews.
Become the author of a review that helps everyone!