Machine learning is a type of artificial intelligence that involves giving computers the ability to learn from data without being explicitly programmed.
This is achieved by training the computer on a large amount of data, and then allowing it to use what it has learned to make predictions or take actions on new data.
In other words, it is a method of teaching a computer to make decisions or take actions based on the data it has been given, rather than following a set of rules or instructions provided by a human.
This can be a powerful tool for solving complex problems, and it is being used in a wide range of applications, from self-driving cars to medical diagnosis.
Machine learning allows us to make better decisions, faster and more accurately, by harnessing the power of data and algorithms. It is a key part of the future of artificial intelligence, and it is already changing the way we live and work.
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Which Machine Learning model should you use to solve a problem? π€
There are several factors to consider when choosing which machine learning (ML) model to use for a particular problem.
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The type of problem you are trying to solve: Different ML models are suited to different types of problems, such as regression, classification, or clustering. You should choose a model that is well-suited to the type of problem you are trying to solve.
The size and quality of the data you have available: Different ML models have different requirements and capabilities when it comes to the size and quality of the data they can handle. You should choose a model that is able to handle the size and quality of the data.
Supervised learning is a type of machine learning algorithm that uses labeled training data to learn a function that can map input data to the desired output.
In supervised learning, the training data consists of a set of input data and corresponding labels or output values, which are used to train the model to produce the desired output for a given input.
For example, in a supervised learning model for image classification, the training data would consist of a set of images and their corresponding labels, such as "dog", "cat", or "car".
Supervised learning is a type of machine learning algorithm that uses labeled training data to learn a function that can map input data to the desired output.
In supervised learning, the training data consists of a set of input data and corresponding labels or output values, which are used to train the model to produce the desired output for a given input.
For example, in a supervised learning model for image classification, the training data would consist of a set of images and their corresponding labels, such as "dog", "cat", or "car".
Pandas also includes a number of functions for visualizing and plotting data. To create a basic line plot in Pandas, use the .plot() method on a dataframe. This method takes optional arguments to customize the appearance
For example, if you had a dataframe df with two columns x and y, you could create a line plot of these columns using the following code:
Pandas also provides convenience functions for creating common plot types, such as bar plots, histograms, and scatter plots