Machine learning is being used by people in various ways knowingly or unknowingly. Each time when we Google or Bing search we get the best results because they have used machine learning to rank page. The world biggest search engine giant Google offers recommendation and suggestion based on the previous user searches. In 2012, Google introduced Knowledge Graph -an algorithm used to decipher the semantic content of a search query. Facebook uses a face recognition…
A python library is a collection of functions and methods that allow you to perform lots of actions without writing any code. The libraries usually consist of built-in models providing different functionalities, which you can use directly. There are a lot of libraries offering a broad range of facilities. Below are some important python libraries used for data science. It is divided into three group i.e. Scientific Computing libraries, Visualization libraries, and Algorithmic libraries. Scientific…
In Artificial Neural Network (ANN), the activation function of a neuron defines the output of that neuron given a set of inputs. For Neural Network to achieve maximum predictive power, we must apply activation function in the hidden layers. An activation function allows the model to capture non-linearities. Image 1 below from study.com gives examples of linear function and reduces nonlinear function. If the relationships in the data are not straight line relationships we will…
Bias/Variance Tradeoff is a concept in machine learning which refers to the problem of minimizing two major sources of errors at the same time and prevent the supervised learning algorithms from generalizing to accommodate inputs beyond the original training set. The two error sources here are: Bias Variance Sources of Error Bias Error (Underfitting): In case of underfitting, the bias is an error from a faulty assumption in the learning algorithm. This is such that…
Unsupervised learning is like you only have input variables and no corresponding output variables. It means the dataset is unlabeled or same labeled, unlike supervised learning. The goal of unsupervised machine learning technique is to find similarities/patterns in the data points and group similar data points together. For example, grouping a crowd based on the color of the shirt/ t-shirt they are wearing, or skin color or hair category or gender etc. Unsupervised learning is…
As the name suggests Supervised means there is some supervision in the learning methodology. It is like learning from the teacher or having supervision from the master. The machine learning technique in which well-labeled data is used to classify or predict the correct output by learning from the previous similar attribute in the data. Well labeled means data has an input-output sequence. For this at first, some example training dataset is fetched to create a…