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ML Introduction with scikit-learn
course content

Course Content

ML Introduction with scikit-learn

ML Introduction with scikit-learn

1. Machine Learning Concepts
What is MLTypes of Machine LearningTraining SetTypes of DataMachine Learning Workflow
2. Preprocessing Data with Scikit-learn
Scikit-learn ConceptsGetting Familiar with DatasetDealing with Missing ValuesImpute Missing ValuesOrdinalEncoderOne-Hot EncoderLabelEncoderEncode Categorical VariablesWhy Scale the Data?StandardScaler, MinMaxScaler, MaxAbsScalerScale the FeaturesPreprocessing Summary
3. Pipelines
What is PipelineColumnTransformerPipelineCreating a Pipeline ChallengeFinal EstimatorCreate a Complete ML Pipeline
4. Modeling
ModelsKNeighborsClassifierEvaluating a Model. Train-Test split.Cross-ValidationEvaluate the Model with Cross-ValidationGridSearchCVThe Flaw of GridSearchCVTune Hyperparameters with RandomizedSearchCVModeling SummaryPutting It All Together

bookGetting Familiar with Dataset

Let's begin our preprocessing journey by exploring the dataset. Throughout the course, we will use the Penguin dataset. The task is to predict a species of penguin.

There are three possible options:

And the features are: 'island', 'culmen_depth_mm', 'flipper_length_mm', 'body_mass_g', and 'sex'.
The data is contained in a .csv file, penguins.csv.

We will load this file from a link using the pd.read_csv() function. Let's load it and look at the contents:


              
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import pandas as pd

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/a65bbc96-309e-4df9-a790-a1eb8c815a1c/penguins.csv')

print(df.head(10))
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Looking at this training set, we can already find some issues we need to resolve. Those are:

  • Missing data;
  • Categorical variables;
  • Different scales.

Missing data

Most ML algorithms can't handle missing values automatically, so we need to remove them (or replace them with some values, which is called imputing) before feeding the training set to a model.
pandas fills empty cells of the table with NaN.
Most ML models will raise an error if at least one NaN exists in the data.

Categorical data

The data contains categorical data, which we already know can't be handled by Machine Learning models.

So we need to encode categorical data into numerical.

Different Scales

'culmen_depth_mm' values range from 13.1 to 21.5 while 'body_mass_g' values range from 2700 to 6300.
Because of that, some ML models consider the 'body_mass_g' feature much more important than 'culmen_depth_mm'.

Scaling solves this problem. It will be covered in later chapters.

question-icon

Match the problem with a way to solve it.

Missing values –
Categorical data –
Different Scales –

Click or drag`n`drop items and fill in the blanks

Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 2. Chapter 2
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