Data School
Are you trying to learn data science so that you can get your first data science job? You're probably confused about what you're "supposed" to learn, and then you have the hardest time actually finding lessons you can understand!
Data School focuses you on the topics you need to master first, and offers in-depth tutorials that you can understand regardless of your educational background.
My name is Kevin Markham, and I'm the founder of Data School. I've taught data science using the Python programming language to hundreds of students in the classroom, and hundreds of thousands of students (like you) online.
Finding the right teacher was so important to my data science education, and so I sincerely hope that I can be the right data science teacher for you.
Please click the SUBSCRIBE button to be notified of my new data science tutorials! I look forward to interacting with you in the comments :)
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Course outline: "Master Machine Learning with scikit-learn"
Course overview: "Master Machine Learning with scikit-learn"
Introduction to model ensembling
How to save a scikit-learn Pipeline with custom transformers
Should I shuffle samples with cross-validation?
Cost-sensitive learning in scikit-learn
scikit-learn vs Deep Learning
How to read the scikit-learn documentation
My top 50 scikit-learn tips
21 more pandas tricks
Adapt this pattern to solve many Machine Learning problems
Tune multiple models simultaneously with GridSearchCV
Access part of a Pipeline using slicing
Tune the parameters of a VotingClassifer or VotingRegressor
Ensemble multiple models using VotingClassifer or VotingRegressor
Create feature interactions using PolynomialFeatures
Speed up GridSearchCV using parallel processing
Use OrdinalEncoder instead of OneHotEncoder with tree-based models
Passthrough some columns and drop others in a ColumnTransformer
Drop the first category from binary features (only) with OneHotEncoder
Estimators only print parameters that have been changed
Load a toy dataset into a DataFrame
Get the feature names output by a ColumnTransformer
Create an interactive diagram of a Pipeline in Jupyter
Most parameters should be passed as keyword arguments