test
Voraussetzung: Register bei Kaggle, dann Login
Kurs: https://www.kaggle.com/learn/overview > Intro to Machine Learning - Learn the core ideas in machine learning, and build your first models.
1 How Models Work: The first step if you're new to machine learning
2 Basic Data Exploration: Load and understand your data
- Using Pandas to Get Familiar With Your Data
melbourne_file_path = '../input/melbourne-housing-snapshot/melb_data.csv'melbourne_data = pd.read_csv(melbourne_file_path)melbourne_data.describe()# save filepath to variable for easier access melbourne_file_path = '../input/melbourne-housing-snapshot/melb_data.csv' # read the data and store data in DataFrame titled melbourne_data melbourne_data = pd.read_csv(melbourne_file_path) # print a summary of the data in Melbourne data melbourne_data.describe() - Interpreting Data Description
- Exercise: Explore Your Data
home_data.describe().Thome_data.describe().YearBuilt['min']home_data.describe().LotArea['mean'].round(0)
3 Your First Machine Learning Model: Building your first model. Hurray!
- Selecting Data for Modeling
- Choosing "Features"
- Building Your Model
- Model Building Exercise
4 Model Validation: Measure the performance of your model ? so you can test and compare alternatives
- What is Model Validation
- The Problem with "In-Sample" Scores
- Coding It
- Wow!
5 Underfitting and Overfitting: Fine-tune your model for better performance.
- Experimenting With Different Models
- Example
- Conclusion
6 Random Forests: Using a more sophisticated machine learning algorithm.
- Introduction
- Example
- Conclusion