Kaggle Learn
[Kaggle Learn](https://www.kaggle.com/learn)
offers hands-on courses for most data science topics. These short courses prepare you with the key ideas to build your own projects.
The
[Machine Learning Course](https://www.kaggle.com/learn/machine-learning)
will give you everything you need to succeed in this competition and others like it.
Other R Tutorials
[Fun with Real Estate Data](https://www.kaggle.com/skirmer/house-prices-advanced-regression-techniques/fun-with-real-estate-data)
Use Rmarkdown to learn advanced regression techniques like random forests and
[XGBoost](https://github.com/dmlc/xgboost)
[XGBoost with Parameter Tuning](https://www.kaggle.com/jiashenliu/house-prices-advanced-regression-techniques/updated-xgboost-with-parameter-tuning/run/362252)
Implement LASSO regression to avoid multicollinearity
Includes linear regression, random forest, and
[XGBoost](https://github.com/dmlc/xgboost)
models as well
[Ensemble Modeling: Stack Model Example](https://www.kaggle.com/jimthompson/house-prices-advanced-regression-techniques/ensemble-model-stacked-model-example)
Use "
[ensembling](http://mlwave.com/kaggle-ensembling-guide/)
" to combine the predictions of several models
Includes GBM (gradient boosting machine),
[XGBoost](https://github.com/dmlc/xgboost)
, ranger, and neural net using the
[caret package](https://cran.r-project.org/web/packages/caret/index.html)
[A Clear Example of Overfitting](https://www.kaggle.com/ozagordi/house-prices-advanced-regression-techniques/a-clear-example-of-overfitting)
Learn about the dreaded consequences of overfitting data
Other Python Tutorials
[Comprehensive Data Exploration with Python](https://www.kaggle.com/pmarcelino/house-prices-advanced-regression-techniques/comprehensive-data-exploration-with-python)
Understand how variables are distributed and how they interact
Apply different transformations before training machine learning models
[House Prices EDA](https://www.kaggle.com/dgawlik/house-prices-advanced-regression-techniques/house-prices-eda)
Learn to use visualization techniques to study missing data and distributions
Includes correlation heatmaps, pairplots, and t-SNE to help inform appropriate inputs to a linear model
[A Study on Regression Applied to the Ames Dataset](https://www.kaggle.com/juliencs/house-prices-advanced-regression-techniques/a-study-on-regression-applied-to-the-ames-dataset)
Demonstrate effective tactics for feature engineering
Explore linear regression with different regularization methods including ridge, LASSO, and ElasticNet using
[scikit-learn](http://blog.kaggle.com/author/kevin-markham/)
[Regularized Linear Models](https://www.kaggle.com/apapiu/house-prices-advanced-regression-techniques/regularized-linear-models)
Build a basic linear model
Try more advanced algorithms including
[XGBoost](https://github.com/dmlc/xgboost)
and neural nets using
[Keras](https://keras.io/)