Intuition Behind A Data Science

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PCA is a dimensionality reduction technique that reduces the dimensionality of volume datasets by transforming a large set of variables into a smaller one that still contains most of the information in the large set. The main objective of PCA is to simplify model features into fewer components to help visualize patterns in the data and to make the model run faster. Using PCA also reduces the chance of overfitting the model by eliminating features with high correlation. PCA is defined as an orthogonal linear transformation that finds mutually orthogonal directions of maximal variance. It transforms the data so that the greatest variance lies on the 1st coordinate (called the 1st Principal Component), the 2nd greatest variance on the second coordinate, and so on. PCA is sensitive to outliers in the data that produce large errors. Therefore, there is common practice to remove outliers before computing PCA.  Usually, PCA relies on a linear model. If a dataset has a pattern hidden inside it that is nonlinear, then PCA can actually steer the analysis in the complete opposite direction of progress. The results of PCA depend on the scaling of the variables. #patternrecognition #dimensionalityreduction #machinelearning #datascience https://www.instagram.com/p/CQAYUM8D8vf/?utm_medium=tumblr

Multiview Learning - Intuitive illustration. Multi-view Learning is a machine learning paradigm that considers learning the data by looking at it from different perspectives. It is also known as a data fusion or data integration from multiple feature sets. Multiview learning paradigm aims to learn one function to model each view and jointly optimizes all the functions to improve the generalization performance. A naive solution for multiview learning considers concatenating all multiple views into one single view and applies single-view learning algorithms directly. Learning with multiple distinct feature sets or has a well theoretical underpinnings and good practical success.  (infographics source: Pinterest) #machinelearning #multiview #datascience #artificialintelligence https://www.instagram.com/p/CP5DAaCAark/?utm_medium=tumblr

Analytics is the scientific process of discovering and communicating the meaningful patterns which can be found in data. It is concerned with turning raw data into insight for making better decisions. Data analytics is a discipline focused on extracting insights from data. It comprises the processes, tools and techniques of data analysis and management, including the collection, organization, and storage of data. There are four types of analytics: descriptive, diagnostic, predictive and prescriptive #analytics | #dataanalytics | #datascience https://www.instagram.com/p/CPx_VfUAMPs/?utm_medium=tumblr

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All you ever wanted to know about Feature Selection in machine learning - Exhaustive Overview. Read the full article: https://link.medium.com/gQIt3rRhNgb Feature Selection Techniques - All You ever wanted to know and more. You can read an Exhaustive Overview in my new comprehensive article in Analytics Vidhya https://link.medium.com/r4WjRZJIOgb This article contains the following topics: - General intro - What is Feature Selection - What makes some features better than others - Feature selection by label information - Supervised feature selection - Unsupervised feature selection - Feature selection - data perspective - Characteristics of the feature selection algorithm - Feature stability - Feature Representation in Financial Crime #featureengineering #machinelearning #datascience #featureselection (at Haifa, Israel) https://www.instagram.com/p/CPs-rdCjeW5/?utm_medium=tumblr

All you ever wanted to know about Feature Selection in machine learning - Exhaustive Overview. — Read the full article: https://link.medium.com/gQIt3rRhNgb — This article contains the following topics: - General intro - What is Feature Selection - What makes some features better than others - Feature selection by label information - Supervised feature selection - Unsupervised feature selection - Feature selection - data perspective - Characteristics of the feature selection algorithm - Feature stability - Feature Representation in Financial Crime — #featureengineering #machinelearning #datascience (at Haifa, Israel) https://www.instagram.com/p/CPqwN0lDXCl/?utm_medium=tumblr

R-squared versus Adjusted-R-squared — Everything you wanted to know about R-squared in one picture. #statistics #datascience — Learn more on http://linkedin.com/in/danybutvinik (at Haifa, Israel) https://www.instagram.com/p/CPpmXM1DzRK/?utm_medium=tumblr

Deep Learning Tools — #deeplearning #datascience — Learn more on http://linkedin.com/in/danybutvinik (at Haifa, Israel) https://www.instagram.com/p/CPpB6tTDq1Q/?utm_medium=tumblr

Difference between Offline (classic) Machine Learning and Online Incremental Machine Learning - High-Level architecture. Online incremental learning provides continuous learning on real-time streaming data. The online model learns in sequential order, as data becomes available. In case of model degradation, online machine learning models are capable to update themselves and prevent catastrophic deterioration in model performance. It provides can provide scalable automation in deployment with low cost and reduced maintenance in a post-deployment era. #machinelearning #onlinemachinelearning (at Haifa, Israel) https://www.instagram.com/p/CPcGX2TjY2s/?utm_medium=tumblr

To find out more about this taxonomy, read my new article https://link.medium.com/m4giMTtxsgb #machinelearning #datascience (at Haifa, Israel) https://www.instagram.com/p/CPKkMSZjn1r/?utm_medium=tumblr

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Handling missing data - Selected techniques. Extended overview (at Haifa, Israel) https://www.instagram.com/p/CO4bu1ygZK5/?igshid=93vhool1lew1

Handling Missing Values - Selected Methods. Extended Overview (at Haifa, Israel) https://www.instagram.com/p/CO4amw1AnPi/?igshid=18l32ogp36dnr