Fundamentals of Machine Learning

Introduction To The Fundamentals of Machine Learning

Most of the people who come into the field of machine learning are not from a coding background, so in such a situation you need to clearly understand some important terms that you need to know before you start with machine learning. So in this article, I will cover the fundamentals of machine learning where I will cover some very important terms used in machine learning.

Fundamentals of Machine Learning

The fundamentals of any course mainly cover the topics that you need to know before you start with a course that is meant not to be overly stressed in the full course process. So in this section, I will cover the fundamentals of machine learning which are very important to get started but not explained very well by anyone.

Also, Read – 100+ Machine Learning Projects Solved and Explained.

Now let’s understand the fundamentals of machine learning that you should know before starting with machine learning.

Labels: Labels are the variables whose value we predict. A label can be any value we predict, like the future price of the stock, the type of animal in the image, or even predict what word the person will type next. So any value we predict is represented as a label in machine learning. While this is not the rule of thumb, most machine learning practitioners represent the labels by y.

Features: Since labels are something that we predict, so we can say that labels are the output values we get after using machine learning. Just like them, features are input values that we use to predict labels. Again, this is not the rule of thumb, but most practitioners represent features by x. Now features are of two types dependent and independent. Independent features are represented on the x-axis and the dependent variables are represented on the y-axis.

Models: A model is something that defines the relationship between labels and features which are the input variables and the predicted value. There are two stages in the model process which are training and testing. Training means that we fit the data on the algorithm so that the model learns from features. Testing means testing the model on new unseen examples performed on a new set of data.

Regression: In machine learning, there are two types of model, regression is one of them. Regression means predicting continuous values. The type of problems where regression models are used is like predicting the value of stock prices, predicting the value of house prices, etc. Regression models are used while predicting future values by using historical and present data.

Classification: As mentioned above, there are two main types of models in machine learning, classification models are the other one. Classification models are used to predict discrete values. The types of issues where classification models are used are issues such as classifying emails as spam or not spam, classifying whether the person is wearing a mask or not.

So these were the most important fundamentals of machine learning that you should know before starting with machine learning. I hope you liked this article on the fundamentals of machine learning. Feel free to ask your valuable questions in the comments section below.

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Aman Kharwal
Coder with the ♥️ of a Writer || Data Scientist | Solopreneur | Founder
Articles: 1126

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