Frequenlty Used Machine Learning Terms | 2021

 Machine Learning sub-set of Artificial Intelligence focuses mostly on Machine. In this, computer algorithms are reinforced by training automatically, resulting in increased efficiency and better prediction. It detects similarities in the data, allowing data-driven decisions to be taken by the computer or system rather than being directly designed to do a certain task.

Machine Learning Terms You Should Know

• Data Wrangling

  Data Wrangling also know as Data Cleaning or "munging", is the process of gathering, selecting, cleaning , structuring and enriching raw data into the desired format for better decision making in less time. Data wrangling help to create an efficient ETL (Extract Transform and Load) or create beautiful data visualizations.It can take a lot of work and time but it is worth the time and effort as it can give vital information from the data.
  

• Data Imputation

   Data Imputation is the substitution of estimated values for missing or inconsistent data item (fields). The substituted values are intended to create a data record that does not fail edits. Data Imputation is important as missing data can create problems for analyzing data, imputation is seen as a way to avoid pitfalls involved with list wise deletion of cases that have missing values. The most common technique is mean imputation, where you take the mean of the exiting data in the field and fill in the blanks with this. 
  

• Supervised Learning

  Supervised Learning is an approach to creating AI [Artificial Intelligence], where the program is given labelled input data and the expected output results.The AI system is specifically told what to look for, thus the model is trained until it can detect the underlying patterns and relationship, enabling it to yield good results when presented with never-before-seen-data. It is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples.

• Unsupervised Learning

  In Unsupervised Learning, a Dataset is provided without labels, and a model learns useful properties of the structure of the Dataset. We do not tell the model what it must learn, but allow it to find patterns and draw conclusion from the unlabeled data. The algorithms in unsupervised learning are more difficult than in supervised learning, since we have little or no information about the data. Unsupervised Learning task typically involve grouping similar example together,  Dimensionality reduction, and density estimation.Unsupervised learning problems can be further grouped into clustering and association problems.

• Classification

  Classification refers to a predictive modeling problem, classification algorithm tries to determine the class of the category of the data it is presented with. Many times, an object might belong to several categories, and the AI needs to determine what those categories are and how much confidence the algorithm has in its prediction.
  

• Regression

  Regression analysis consists of a set of machine learning methods that allow us to predict a continuous output variable (y) based on the value of one or more predictor variables (x). It is the type of Supervised Learning in which labelled data used, and this data is used to make prediction in a continuous form. Regression problems include types where the output variables are set as a real number. The format for this problem often follows a liner format. Regression models are used to predict a continuous value.

• Clustering

  Clustering is a Unsupervised Machine learning task. It is the task of diving the population or data points into a number of groups such that data points in the same groups such that data points in the same groups are more similar to other data points in the same group than those in the other groups. In simple words, the aim is to segregate groups with similar traits and assign them into clusters.