ML Introduction with scikit-learn

ML Introduction with scikit-learn

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1. Machine Learning Concepts

Learn the Machine Learning concepts and the ML project workflow.

Types of Machine Learning

Machine Learning Workflow

2. Preprocessing Data with Scikit-learn

Preprocessing is probably the most important stage of an ML project. This chapter covers the preprocessing steps needed for almost any dataset.

Scikit-learn Concepts

Getting Familiar with Dataset

Dealing with Missing Values

Impute Missing Values

One-Hot Encoder

Encode Categorical Variables

Why Scale the Data?

StandardScaler, MinMaxScaler, MaxAbsScaler

Scale the Features

Preprocessing Summary

3. Pipelines

A pipeline is a neat way to combine all the preprocessing steps as well as a model. Pipelines make it much easier to train and use a model.

What is Pipeline

ColumnTransformer

Creating a Pipeline Challenge

Final Estimator

Create a Complete ML Pipeline

4. Modeling

Modeling is the most fun stage of an ML project. Let's learn to build, fine-tune and evaluate the model!

KNeighborsClassifier

Evaluating a Model. Train-Test split.

Cross-Validation

Evaluate the Model with Cross-Validation

The Flaw of GridSearchCV

Tune Hyperparameters with RandomizedSearchCV

Modeling Summary

Putting It All Together

Course Content

ML Introduction with scikit-learn

ML Introduction with scikit-learn

Create a Complete ML Pipeline

Now let's build a proper pipeline with the final estimator. As a result, we will get a trained prediction pipeline that can be used for predicting new instances simply by calling the .predict() method.
To train a predictor (model), you need y to be encoded. This is done separately from the Pipeline we build for X.
Remember that LabelEncoder is used for encoding the target.

You have the same Penguins dataset.
The task is to build a pipeline with KNeighborsClassifier as a final estimator, train it, and predict values for the X itself.
Since the predictions will be encoded (0, 1, or 2), we will use the .inverse_transform() method of LabelEncoder to get the predictions back to 'Adelie', 'Chinstrap', or 'Gentoo'.

Task

Encode the y variable using the LabelEncoder.
Make a pipeline containing ct, SimpleImputer, StandardScaler, and KNeighborsClassifier (in that order).
Train the pipe object.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 3. Chapter 6

toggle bottom row

Create a Complete ML Pipeline

Now let's build a proper pipeline with the final estimator. As a result, we will get a trained prediction pipeline that can be used for predicting new instances simply by calling the .predict() method.
To train a predictor (model), you need y to be encoded. This is done separately from the Pipeline we build for X.
Remember that LabelEncoder is used for encoding the target.

You have the same Penguins dataset.
The task is to build a pipeline with KNeighborsClassifier as a final estimator, train it, and predict values for the X itself.
Since the predictions will be encoded (0, 1, or 2), we will use the .inverse_transform() method of LabelEncoder to get the predictions back to 'Adelie', 'Chinstrap', or 'Gentoo'.

Task

Encode the y variable using the LabelEncoder.
Make a pipeline containing ct, SimpleImputer, StandardScaler, and KNeighborsClassifier (in that order).
Train the pipe object.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 3. Chapter 6

toggle bottom row

Create a Complete ML Pipeline

Now let's build a proper pipeline with the final estimator. As a result, we will get a trained prediction pipeline that can be used for predicting new instances simply by calling the .predict() method.
To train a predictor (model), you need y to be encoded. This is done separately from the Pipeline we build for X.
Remember that LabelEncoder is used for encoding the target.

You have the same Penguins dataset.
The task is to build a pipeline with KNeighborsClassifier as a final estimator, train it, and predict values for the X itself.
Since the predictions will be encoded (0, 1, or 2), we will use the .inverse_transform() method of LabelEncoder to get the predictions back to 'Adelie', 'Chinstrap', or 'Gentoo'.

Task

Encode the y variable using the LabelEncoder.
Make a pipeline containing ct, SimpleImputer, StandardScaler, and KNeighborsClassifier (in that order).
Train the pipe object.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Now let's build a proper pipeline with the final estimator. As a result, we will get a trained prediction pipeline that can be used for predicting new instances simply by calling the .predict() method.
To train a predictor (model), you need y to be encoded. This is done separately from the Pipeline we build for X.
Remember that LabelEncoder is used for encoding the target.

You have the same Penguins dataset.
The task is to build a pipeline with KNeighborsClassifier as a final estimator, train it, and predict values for the X itself.
Since the predictions will be encoded (0, 1, or 2), we will use the .inverse_transform() method of LabelEncoder to get the predictions back to 'Adelie', 'Chinstrap', or 'Gentoo'.

Task

Encode the y variable using the LabelEncoder.
Make a pipeline containing ct, SimpleImputer, StandardScaler, and KNeighborsClassifier (in that order).
Train the pipe object.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 3. Chapter 6

Switch to desktop for real-world practiceContinue from where you are using one of the options below

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