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

Training Set

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

Challenge: Imputing Missing Values

One-Hot Encoder

Challenge: Encoding Categorical Variables

Why Scale the Data?

StandardScaler, MinMaxScaler, MaxAbsScaler

Challenge: Scaling 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

Efficient Data Preprocessing with Pipelines

Challenge: Creating a Pipeline

Final Estimator

Challenge: Creating 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 the Model

Cross-Validation

Challenge: Evaluating the Model with Cross-Validation

The Flaw of GridSearchCV

Challenge: Tuning Hyperparameters with RandomizedSearchCV

Modeling Summary

Challenge: Putting It All Together

Зміст курсу

ML Introduction with scikit-learn

ML Introduction with scikit-learn

1. Machine Learning Concepts

What is ML Types of Machine Learning Training Set Types of Data Machine Learning Workflow

2. Preprocessing Data with Scikit-learn

Scikit-learn Concepts Getting Familiar with Dataset Dealing with Missing Values Challenge: Imputing Missing Values OrdinalEncoder One-Hot Encoder LabelEncoder Challenge: Encoding Categorical Variables Why Scale the Data?StandardScaler, MinMaxScaler, MaxAbsScaler Challenge: Scaling the Features Preprocessing Summary

3. Pipelines

What is Pipeline ColumnTransformer Efficient Data Preprocessing with Pipelines Challenge: Creating a Pipeline Final Estimator Challenge: Creating a Complete ML Pipeline

4. Modeling

Models KNeighborsClassifier Evaluating the Model Cross-Validation Challenge: Evaluating the Model with Cross-Validation GridSearchCV The Flaw of GridSearchCV Challenge: Tuning Hyperparameters with RandomizedSearchCV Modeling Summary Challenge: Putting It All Together

Training Set

If we talk about supervised or unsupervised learning, the training set will usually be in a table form.

Consider the diabetes dataset, which has the task of predicting whether a person has diabetes. It holds information about 768 females with parameters like age, body mass index, blood pressure, etc. These parameters are called features.

The dataset also contains information on whether the person has diabetes in an 'Outcome' column, which is what we want to predict. It is called target.

Each row in a table is called instance(or data point or sample). In this case, it is information about one female.

The table (training set) has a target column in it, which means it is labeled.

The task is to train the ML model on this training set, and once it is trained, it can predict for other people (new instances) whether they have diabetes based on features only.

While coding, feature columns are usually assigned to X and target columns assigned as y.

And features of new instances are assigned as X_new.

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