Welcome to the world of Machine Learning (ML)! If you have no background in computer science or programming, don’t worry. In this blog post, we will explain the basics of ML in a way that is easy to understand and interesting. By the end, you will have a good sense of what ML is, how it differs from traditional programming, and how it works.
Machine Learning (ML) is a way of teaching computers to learn and make decisions on their own, without being explicitly programmed by a human. To do this, we feed the computer a lot of data and use algorithms to help it recognize patterns and relationships within the data.
The choice of algorithms depends on the type of data we have and the task we want the computer to perform. ML can be thought of like studying for an exam.
We feed our brain (the computer) a lot of information and practice solving problems, gradually improving our performance and understanding of the subject. Researchers are constantly working to improve the algorithms and techniques used in ML to make these models even better.
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Basic Difference in ML and Traditional Programming
Before we dive into the world of ML, let’s first talk about traditional programming. In traditional programming, a programmer writes a set of instructions (also known as code) that tell a computer what to do. These instructions are very specific and tell the computer exactly what to do step by step.
On the other hand, ML is a type of programming that allows computers to learn from data without being explicitly programmed. In other words, instead of telling the computer exactly what to do, we give it a large amount of data and let it figure out the patterns and relationships within the data.
Example to understand ml programming vs traditional
Imagine we want to create a program that can identify whether a given email is spam or not spam.
In traditional programming, we might approach this problem by writing a set of specific instructions for the computer to follow. For example, we might tell the computer to look for certain keywords (e.g., “free,” “win,” “click here”) and to flag the email as spam if any of these keywords are present.
On the other hand, in ML, we would approach this problem by giving the computer a large dataset of labeled emails (i.e., emails that have been manually labeled as spam or not spam) and letting the computer learn from this data. We would then use an ML algorithm to build a model that can take in a new email and predict whether it is spam or not spam based on the patterns it has learned from the labeled data.
The main difference between these two approaches is that traditional programming relies on explicit instructions, whereas ML relies on the computer learning from data. This means that ML can be more flexible and adaptable than traditional programming, as it can continue to learn and improve as it is given more data. However, it also means that ML requires a large amount of labeled data in order to work effectively.
What does “learning” mean for a computer?
When we talk about learning in the context of computers, we are usually talking about supervised learning. This means that we provide the computer with a set of labeled data, which includes both the input data (also known as features) and the output data (also known as labels). The computer uses this labeled data to learn a function that can map the input data to the output data.
For example, let’s say we want to create a computer program that can identify different types of flowers based on their features, such as the color of the petals and the shape of the petals. We could provide the computer with a large set of labeled data that includes the features and labels of different types of flowers. The computer would then use this data to learn a function that can take in the features of a flower and output the correct label (e.g., “roses” or “daisies”).
How does ML work?
Now that you have a basic understanding of what ML is and how it differs from traditional programming, let’s talk about how it works.
There are several steps involved in the ML process:
- Collect and prepare the data: The first step in any ML project is to collect and prepare the data. This includes things like collecting the raw data, cleaning the data (e.g., removing any errors or inconsistencies), and formatting the data in a way that is suitable for the ML algorithms.
- Choose an ML algorithm: There are many different types of ML algorithms, and each one is suited for different types of problems. Some common types of ML algorithms include decision trees, k-nearest neighbors, and support vector machines.
- Train the model: Once we have chosen an ML algorithm and prepared our data, we can train the model using the labeled data. This involves feeding the data into the algorithm and letting it learn the patterns and relationships within the data.
- Evaluate the model: After the model has been trained, we need to evaluate its performance to see how well it can predict the output data given the input data. This usually involves using a separate dataset (called the test set) that the model has not seen before.
- Fine-tune the model: If the model’s performance is not satisfactory, we can fine-tune it by adjusting the hyperparameters (e.g., the learning rate) or by using a different type of ML algorithm.
- Deploy the model: Once the model is performing well, we can deploy it in a real-world setting, where it can be used to make predictions or decisions.