Was ist Supervised Learning?

Supervised learning is one of the most common techniques in the field of Machine Learning . This method trains algorithms to make predictions on new, unseen data by learning from previous examples that have known results or labels.

The goal of supervised learning is to find a function that maps the input variables (features) into the corresponding output variable (label). The model learns from example data, which consists of input variables and the associated output variables. This process allows the model to identify and generalize patterns in the input data to correctly predict new, previously unknown data.

An example of supervised learning is classification, where the model decides whether new data belongs to a particular class or category based on examples. Another example is regression, where the model makes predictions based on numerical input variables to produce a numerical output value.

Supervised learning is used in various application areas, such as image recognition, voice recognition , medical diagnostics or financial analysis.

What are the most famous supervised learning techniques?

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There are various supervised learning techniques used in practice, here are some of the most popular:

• Linear Regression: A simple procedure to model a relationship between an independent and a dependent variable. The method uses a linear function to describe the relationship between the variables.
• Logistic Regression: An extension of linear regression used for classification tasks. The method uses a sigmoid function to make predictions between 0 and 1, which represent the probability that an input falls into a particular class.
• Decision Trees: A decision-making method that uses a tree structure to make decisions. The tree has decision nodes that test for certain properties of the data and leaves that make predictions.
• Random Forest: An ensemble method that combines multiple decision trees to make more precise predictions. It is often used for large amounts of data and complex problems.
• Naive Bayes: A method based on Bayes theorem to calculate the probability of a class based on certain properties of the data. It is often used in text classification and spam filters.
• Support Vector Machines (SVM): A method of classification or regression that finds an optimal dividing line between data points in a multidimensional space.

These techniques all have different advantages and disadvantages and are suitable for different types of data sets and problems. Choosing the most appropriate method depends on the specific needs of the project.

Which companies use supervised learning?

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Supervised learning is used by many companies and organizations around the world. Some examples of this are:

• Amazon: Amazon uses supervised learning to generate personalized product recommendations for customers and perform real-time fraud detection.
• Google: Google uses supervised learning in various products such as Google Translate, Google Assistant and Google Photos.
• Facebook: Facebook uses supervised learning to recommend personalized content for users and to combat spam and hate speech.
• Netflix: Netflix uses supervised learning to generate personalized movie and series recommendations based on users' viewing habits and preferences.
• Uber: Uber uses supervised learning to enable precise travel time prediction and optimal route planning.
• Microsoft: Microsoft uses supervised learning in various products such as Bing, Cortana and Office 365.

These companies use supervised learning to improve their products and services and to provide personalized experiences to their customers. Supervised learning has the potential to be used in almost all industries and application areas, from medicine to finance.

Which training is best for getting started in supervised learning?

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To get started in supervised learning, knowledge of mathematics, statistics and computer science is usually required. A typical training course that is suitable for entry into the field is a degree in computer science, mathematics or statistics at bachelor's or master's level. Some universities also offer specialized courses such as data science or machine learning.

There are also numerous online courses that are specifically aimed at imparting knowledge of machine learning and supervised learning. Some popular online platforms that offer such courses are Coursera, Udemy, edX and Codecademy. These courses often offer a combination of theoretical concepts and practical applications, including programming assignments and project work.

It is also helpful to gain practical experience through internships or work experience in industry. This can allow you to solve real-world problems and improve your skills in a practical environment.

In summary, a combination of theoretical knowledge of mathematics, statistics and computer science as well as practical experience and project work is best suited to get started in supervised learning.

Why does supervised learning seem like science fiction in films?

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Supervised learning often seems like science fiction in movies because it is a form of artificial intelligence that allows machines to learn and predict by example. This is a relatively new concept, having only emerged in the last few decades, and it has the potential to revolutionize many aspects of our daily lives.

In films, supervised learning is often portrayed as a kind of "magical" tool that enables machines to interact with the world around them in complex ways and make intelligent decisions. However, there are also films in which supervised learning is portrayed as a threat to humanity when it gets out of control and machines act independently of human control.

In reality, however, supervised learning is a very precise technique that has certain requirements and limitations. For example, it can only learn from the data it was trained on and can only limit itself to making predictions within the context of that data. Additionally, supervised learning requires careful planning and implementation to ensure that the results are meaningful and reliable.

So while there is some overlap between how supervised learning is portrayed in films and reality, it is important to understand that in practice the technology is much more complex and limited than in science fiction.

Which training is best for getting started in supervised learning?

To get started in supervised learning, knowledge of mathematics, statistics and computer science is usually required. A typical training course that is suitable for entry into the field is a degree in computer science, mathematics or statistics at bachelor's or master's level. Some universities also offer specialized courses such as data science or machine learning.

There are also numerous online courses that are specifically aimed at imparting knowledge of machine learning and supervised learning. Some popular online platforms that offer such courses are Coursera , Udemy , edX and Codecademy . These courses often offer a combination of theoretical concepts and practical applications, including programming assignments and project work.

It is also helpful to gain practical experience through internships or work experience in industry. This can allow you to solve real-world problems and improve your skills in a practical environment.

In summary, a combination of theoretical knowledge of mathematics, statistics and computer science as well as practical experience and project work is best suited to get started in supervised learning.