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.

Time series data is found everywhere, and to perform the time series analysis, we must preprocess the data first. Time Series preprocessing techniques have a significant influence on data modelling accuracy.

To learn the optimal values of these parameters, machines randomly try several combinations. But if we keep selecting values randomly then it may take infinite time and we take help of gradient descent.

Unsupervised learning algorithms come into existence that can extract meaningful information from the junk and present it in a human-readable format. Clustering is one of them.

Bias, Variance, and Bias-Variance tradeoff are the most popular terms in machine learning and the most frequent questions asked in machine-learning interviews.

In earlier stages, machines might be making some mistakes and learning from several experiences. But how? Let’s move towards finding the answer to this question.