Introduction Recurrent Neural Networks (RNNs) are a specific type of Neural Networks (NNs) that are especially relevant for sequential data like time series, text, or audio data. Traditional neural networks process each input independently, meaning they cannot retain information about previous inputs. This makes them ineffective for tasks that require understanding sequences, such as time series forecasting or natural language processing. RNNs however, process the data sequentially, which enables them to remember data from the past.

Introduction Principal Component Analysis (PCA) is an unsupervised Machine Learning algorithm for dimensionality reduction. In Data Science and Machine Learning, large datasets with numerous features are often analyzed. PCA simplifies these complex datasets by retaining their essential information while reducing their dimensionality. It transforms a large set of correlated variables into a smaller set of uncorrelated variables known as principal components. These principal components capture the maximum variance in the data.

Introduction K-Means is an example of a clustering algorithm. Clustering is a fundamental concept in Machine Learning, where the goal is to group a set of objects so that objects in the same group are more similar to each other than to those in other groups. Clustering belongs to the set of unsupervised Machine Learning algorithms, that is no ground truth is needed. Among the various clustering algorithms, K-Means stands out for its simplicity and efficiency.