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.
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! 😀
0. What is Machine Learning (ML)?
1. Simple practice environment that does not require complex installation
2. Introduction to scikit-learn & My first machine learning model
3. Introduction to Kaggle and Kaggle Competition
4. Linear Regression Algorithm (Ridge, Lasso, ElasticNet) & How Much is My House Worth?
5. Random Forest, a popular and high-performance predictor
6. XGBoost, the algorithm favored by Kaggle winners
7. Practical stories from working machine learning engineers
8. DS/ML practical tips from practitioners
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
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Answers
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60 lectures ∙ (7hr 19min)