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Types of Machine Learning | Machine Learning Concepts
ML Introduction with scikit-learn
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ML Introduction with scikit-learn

ML Introduction with scikit-learn

1. Machine Learning Concepts
2. Preprocessing Data with Scikit-learn
3. Pipelines
4. Modeling

bookTypes of Machine Learning

Supervised Learning

Supervised learning is a Machine Learning technique in which the model is trained on a labeled training set.
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

Unsupervised learning is a Machine Learning technique in which the model is trained on an unlabeled training set.
The most popular unsupervised learning tasks are:

  • 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 a lot from the previous two types. It is a technique used to train self-driving vehicles, robots, AI in gaming, etc.
Reinforcement Learning is a Machine Learning technique in which the agent(e.g., vacuum cleaner robot) learns by making decisions and getting a reward if the decision is correct and a penalty if the decision is wrong.
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?

Виберіть правильну відповідь

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?

Виберіть правильну відповідь

Все було зрозуміло?

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