Machine Learning: what is it?
Posted: Sun Dec 22, 2024 6:00 am
Arthur Samuel coined the term Machine Learning in 1959. He was a pioneer in artificial intelligence and computer games, and defined Machine Learning as “A field of study that gives computers the ability to learn without having to program.”
In this article, we will first discuss in detail what Machine Learning is, and we will cover several aspects such as processes and applications. Then, we will talk about the importance of Machine Learning. We will explain the terms that are used in Machine Learning and the step-by-step process of a Machine Learning approach. Later, we will also understand the basis of Machine Learning and how it works. Furthermore, we will talk about why Python is the best language for Machine Learning. And finally, we will talk about the different Machine Learning approaches and their applications in the industry.
Coming back to the topic of what Machine Learning is. french email address list Learning is a subcategory of artificial intelligence. Machine Learning is the study of transforming machines into human-like beings in their behaviors and decisions by giving them the ability to learn and develop their own programs. All this is done with little human intervention, in other words, without anything explicitly programmed. The learning process is automated and improved based on the machine’s experience. During this process, good quality data is fed to the machine, and different algorithms are used to create machine learning models to train the machines on this data. The choice of algorithm depends on the type of data at hand, and the type of activity that needs to be automated.
Now you might be wondering, how is this different from traditional programming? Well, in traditional programming, we feed input data and a well-coded and tested program into the machine to generate an output. When we talk about Machine Learning, the input data along with the output data are fed into the machine during the learning phase, and it will create a program for itself. To better understand what Machine Learning is, take a look at the illustration below.
Now that you know what Machine Learning is, you might be wondering, why should I learn it? Keep reading to find out!
Why should we learn Machine Learning?
Machine Learning is getting a lot of attention these days. Machine Learning can automate many tasks, especially those tasks that only humans can do with their intelligence. Replicating this intelligence to machines can only be conceived with the help of Machine Learning.
With the help of Machine Learning, businesses can automate routine tasks. It also helps in automating and quickly creating models for data analysis. Many businesses rely on massive amounts of data to optimize their operations and make smart decisions. Machine Learning helps in creating models that can process and analyze large amounts of complex data to deliver accurate results. These models are accurate and scalable and work with little delay. By creating such accurate Machine Learning models, businesses can leverage profitable opportunities and avoid unknown risks.
Image recognition, text generator, and many other use cases of Machine Learning are being used in the real world. This is making Machine Learning professionals highly sought after.
Don't just settle for knowing what Machine Learning is and why we should learn it. Keep reading to learn more in depth and in practice how Machine Learning works.
How to get started with Machine Learning?
You may have become interested and want to go further to the point of wanting to get started with ML, so before we know how to get started let's first take a look at some important terminologies within Machine Learning:
Some terminologies within Machine Learning
Model: Also known as “Hypothesis”, a Machine Learning model is the mathematical representation of a real-world process. A Machine Learning algorithm, along with training data, builds a Machine Learning model.
Feature: A feature is a measurable property or parameter of the data set.
Feature Vector: It is a set of numerical features. We use it as input to the machine learning model for training and prediction purposes.
Training: The algorithm takes a set of data called “training data” as input. The learning algorithm finds patterns in the input data and trains the models for expected (target) results. The output of the training process is the machine learning model.
Prediction: Once the machine learning model is prepared, it can be fed with input data to provide the predicted output.
Target (Label): The value that the machine learning model has to learn is called the target or label.
Overfitting: When a large amount of data is used to train a machine learning model, it tends to learn from noise and inaccurate input data. Here the model fails to characterize the data correctly.
Underfitting: It is the scenario where the model fails to decipher the implicit bias of the input data. It destroys the accuracy of the machine learning model. In simpler terms, the model or algorithm does not fit the data perfectly.
In this article, we will first discuss in detail what Machine Learning is, and we will cover several aspects such as processes and applications. Then, we will talk about the importance of Machine Learning. We will explain the terms that are used in Machine Learning and the step-by-step process of a Machine Learning approach. Later, we will also understand the basis of Machine Learning and how it works. Furthermore, we will talk about why Python is the best language for Machine Learning. And finally, we will talk about the different Machine Learning approaches and their applications in the industry.
Coming back to the topic of what Machine Learning is. french email address list Learning is a subcategory of artificial intelligence. Machine Learning is the study of transforming machines into human-like beings in their behaviors and decisions by giving them the ability to learn and develop their own programs. All this is done with little human intervention, in other words, without anything explicitly programmed. The learning process is automated and improved based on the machine’s experience. During this process, good quality data is fed to the machine, and different algorithms are used to create machine learning models to train the machines on this data. The choice of algorithm depends on the type of data at hand, and the type of activity that needs to be automated.
Now you might be wondering, how is this different from traditional programming? Well, in traditional programming, we feed input data and a well-coded and tested program into the machine to generate an output. When we talk about Machine Learning, the input data along with the output data are fed into the machine during the learning phase, and it will create a program for itself. To better understand what Machine Learning is, take a look at the illustration below.
Now that you know what Machine Learning is, you might be wondering, why should I learn it? Keep reading to find out!
Why should we learn Machine Learning?
Machine Learning is getting a lot of attention these days. Machine Learning can automate many tasks, especially those tasks that only humans can do with their intelligence. Replicating this intelligence to machines can only be conceived with the help of Machine Learning.
With the help of Machine Learning, businesses can automate routine tasks. It also helps in automating and quickly creating models for data analysis. Many businesses rely on massive amounts of data to optimize their operations and make smart decisions. Machine Learning helps in creating models that can process and analyze large amounts of complex data to deliver accurate results. These models are accurate and scalable and work with little delay. By creating such accurate Machine Learning models, businesses can leverage profitable opportunities and avoid unknown risks.
Image recognition, text generator, and many other use cases of Machine Learning are being used in the real world. This is making Machine Learning professionals highly sought after.
Don't just settle for knowing what Machine Learning is and why we should learn it. Keep reading to learn more in depth and in practice how Machine Learning works.
How to get started with Machine Learning?
You may have become interested and want to go further to the point of wanting to get started with ML, so before we know how to get started let's first take a look at some important terminologies within Machine Learning:
Some terminologies within Machine Learning
Model: Also known as “Hypothesis”, a Machine Learning model is the mathematical representation of a real-world process. A Machine Learning algorithm, along with training data, builds a Machine Learning model.
Feature: A feature is a measurable property or parameter of the data set.
Feature Vector: It is a set of numerical features. We use it as input to the machine learning model for training and prediction purposes.
Training: The algorithm takes a set of data called “training data” as input. The learning algorithm finds patterns in the input data and trains the models for expected (target) results. The output of the training process is the machine learning model.
Prediction: Once the machine learning model is prepared, it can be fed with input data to provide the predicted output.
Target (Label): The value that the machine learning model has to learn is called the target or label.
Overfitting: When a large amount of data is used to train a machine learning model, it tends to learn from noise and inaccurate input data. Here the model fails to characterize the data correctly.
Underfitting: It is the scenario where the model fails to decipher the implicit bias of the input data. It destroys the accuracy of the machine learning model. In simpler terms, the model or algorithm does not fit the data perfectly.