Showing posts with label Statistical Learnings. Show all posts
Showing posts with label Statistical Learnings. Show all posts

Saturday 17 June 2023

Statistical Learnings

Statistics is the study and manipulation of data, including ways to gather, analyze and draw conclusions from data.

Statistical learning, aka machine learning or data mining, is a field of study that focuses on developing algorithms and models to automatically extract meaningful patterns and relationships from data. Statistical learning uses statistical techniques and computational methods to make predictions or decisions based on observed data. This learning process encompasses various methods and approaches. These methods are supervised learning, unsupervised learning, and semi-supervised learning.

In supervised learning, the algorithm is trained using labeled data, where the input variables (features) are associated with corresponding output variables (labels or responses). The goal is to learn a function that maps input variables to output variables. In other words, learn a function that maps features to labels or responses. This approach enables the algorithm to make predictions on new and unseen data.

In unsupervised learning, the algorithm explores unlabeled data to discover patterns without specific output variables. Techniques like Clustering and Dimensionality reduction are commonly used in unsupervised learning. These techniques help to identify groups or similarities in the data or to reduce the dimensionality of the input space. Clustering helps to identify common features and group them whereas dimensionality reduction is used to avoid unnecessary columns of the dataset.

Semi-supervised learning combines elements of both supervised and unsupervised learning. It utilizes a limited amount of labeled data along with a more extensive set of unlabeled data to improve the performance of the learning algorithm.

Statistical learning methods often rely on statistical principles and techniques such as regression, classification, decision trees, support vector machines, neural networks, and more. These techniques enable the algorithms to generalize from the observed data to make predictions or make informed decisions on new, unseen data.

Overall, statistical learning plays a crucial role in various fields, including data analysis, pattern recognition, image and speech recognition, natural language processing, and many other applications where extracting meaningful insights from data is essential.

More details on this learning can be found here:

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