Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.Types of Machine Learning | Machine Learning Concepts
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 ValuesChallenge: Imputing Missing ValuesOrdinalEncoderOne-Hot EncoderLabelEncoderChallenge: Encoding Categorical VariablesWhy Scale the Data?StandardScaler, MinMaxScaler, MaxAbsScalerChallenge: Scaling the FeaturesPreprocessing Summary
3. Pipelines
What is PipelineColumnTransformerEfficient Data Preprocessing with PipelinesChallenge: Creating a PipelineFinal EstimatorChallenge: Creating a Complete ML Pipeline
4. Modeling
ModelsKNeighborsClassifierEvaluating the ModelCross-ValidationChallenge: Evaluating the Model with Cross-ValidationGridSearchCVThe Flaw of GridSearchCVChallenge: Tuning Hyperparameters with RandomizedSearchCVModeling SummaryChallenge: Putting It All Together

book
Types of Machine Learning

Supervised Learning

The most popular supervised learning tasks are:

  • Regression (for example, predicting the price of a house): you will need a training set labeled with other house prices for that;
  • Classification (for example, classifying email as spam/ham): you will need a training set labeled as spam/ham for that.

Unsupervised Learning

The most popular unsupervised learning tasks are clusterizaion, anomaly detection, and dimensionality reduction.

Clusterization

It is a process of grouping similar data points into clusters. You do not need to label the data for it. For example, a training set of emails without labels spam/ham will do.

Anomaly Detection

It is a process of detecting deviations from normal data behavior. For example, fraud detection in credit card transactions. No need to label fraud/not fraud. Simply give the transaction information to a model, which will determine if the transaction stands out.

Dimensionality Reduction

It is a process of reducing the number of dimensions while retaining as much relevant information as possible.
It also does not require any labels.

Reinforcement Learning

Reinforcement learning differs significantly from the previous two types. It is a technique used to train self-driving vehicles, robots, AI in gaming, and more.

In the case of a vacuum cleaner robot, it would receive a reward if it moves to a dirty area and a penalty if it moves to an area already cleaned. Also, it would get a large reward once the whole area is cleaned.

1. To train the ML model for a supervised learning task, you need a training set to contain target (be labeled). Is it correct?
2. To train the ML model for a unsupervised learning task, containing a target (being labeled) for a training set is not required. Is it correct?
To train the ML model for a supervised learning task, you need a training set to contain target (be labeled). Is it correct?

To train the ML model for a supervised learning task, you need a training set to contain target (be labeled). Is it correct?

Select the correct answer

To train the ML model for a unsupervised learning task, containing a target (being labeled) for a training set is not required. Is it correct?

To train the ML model for a unsupervised learning task, containing a target (being labeled) for a training set is not required. Is it correct?

Select the correct answer

Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 1. Chapter 2
We're sorry to hear that something went wrong. What happened?
some-alt