These days, the support of libraries and frameworks is easily accessible in machine learning. But in this article, we will implement a basic machine learning project without using frameworks like Scikit-learn, Keras, or Pytorch. We will use the NumPy library for numerical operations and Matplotlib to visualize the graphs.
When we build a solution for any regression problem, we compare its performance with the existing work using standard metrics, like measuring distance in meters, plot size in square feet, etc. Similarly, we need some standard evaluation metrics to evaluate two regression models. Some of them are MAE, MSE, RMSE, and R-Squared.
K-Nearest Neighbor is a supervised learning algorithm that can be used to solve classification as well as regression problems. This algorithm learns without explicitly mapping input variables to the target variables. It is probably the first "machine learning" algorithm, and due to its simplicity, it is still accepted in solving many industrial problems.
The surrounding environment highly influences house prices but machine learning techniques can help us accurately predict the house price by taking account of all important features.
Optimization of error function is the respiratory process for Machine learning algorithms. But this error function varies for classification and regression problems.
With ongoing advancements in Machine Learning and Data Science, we can precisely predict the remaining life span of a person given the essential parameters.
Both classification and regression deal with the problem of mapping a function from input to output. However, when it comes to classification, the output is a discrete (non-continuous) class label or categorical output. While on the other hand, when the problem is a regression problem, the output is continuous.
In this article, we will try to find the answer to another most critical question in machine learning and artificial intelligence - How exactly the machine learns?
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