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Artificial Intelligence Articles
Page 34 of 35
What are the different learning styles in machine learning algorithms?
There are four learning styles in machine learning algorithms. Let’s have a look at them −Supervised LearningSupervised learning, one of the most used methods in ML, takes both training data (also called data samples) and its associated output (also called labels or responses) during the training process. The major goal of supervised learning methods is to learn the association between input training data and their labels. For this it performs multiple training data instances.Based on machine learning based tasks, we can divide supervised learning algorithms in two classes namely Classification and Regression.Unsupervised LearningUnsupervised learning methods, (opposite to supervised learning methods) ...
Read MoreWhy is Python the most popular programming language among ML professionals?
From process automation to web development to AI-based projects to machine learning, Python is used everywhere, and it helps developers to be productive and confident about the software they are building. Today, because of the benefits like simplicity, consistency, extensive set of libraries, platform independence, flexibility, and a wide community support, Python has become one of the most favored programming languages among machine learning professionals.Simplicity and Consistency − Machine learning relies on complex algorithms and workflows, but it is Python’s simplicity that allows machine learning developers to build reliable applications. Python is so simple that the developers do not need ...
Read MoreWhat are the various challenges for machine learning practitioners?
While machine learning is rapidly evolving, it still has a long way to go. The reasons behind this are the various challenges an ML practitioner faces while developing an application. Let’s take a look at these challenges −Data collection − Data plays the most important role in developing any machine learning application. Most of the work of an ML practitioner lies in collecting good quality data. If you are a beginner and want to experiment with machine learning, you can find datasets from Kaggle or UCI ML Repository. But if you want to implement real case scenarios or need to ...
Read MoreWhat are different components of a machine learning algorithm?
To understand various components of a machine learning algorithm, we first understand the definition of machine learning given by Professor Mitchell −“A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.”As we can see the above definition, the main components of any machine learning algorithm are Task(T), Performance(P), and Experience(E).Based on these three components, let’s simplify the definition of machine learning −Machine learning is a subset of Artificial Intelligence (AI) and a field ...
Read MoreDifference Between Linear and Logistic Regression
In this post, we will understand the difference between linear regression and logistic regression.Linear RegressionIt helps predict the variable that is continuous, and is a dependent variable.This is done using a given set of independent variables.It extrapolates a line to find the value of dependent variable.Least square methods are used to estimate the accuracy.The best fit line is found, that helps predict the output.It is generally a continuous value.The relation between the dependent variable and independent variable has to be linear.The independent variables may have collinearity between them.It is considered a machine learning problem, i.e an applied statistics problem.Logistic RegressionIt ...
Read MoreWhat are the different kinds of gradient descent algorithms in Machine Learning?
The idea behind using gradient descent is to minimize the loss when in various machine learning algorithms. Mathematically speaking, the local minimum of a function is obtained.To implement this, a set of parameters are defined, and they need to be minimized. Once the parameters are assigned coefficients, the error or loss is calculated. Next, the weights are updated to ensure that the error is minimized. Instead of parameters, weak learners can be users, such as decision trees.Once the loss is calculated, gradient descent is performed, and tree is added to the algorithm step wise, so that loss is minimal.Some examples ...
Read MoreHow can Deep Learning be used for facial recognition in Machine Learning?
Face recognition is the task of identifying and verifying people present in a photograph based on their face. This is a trivial task for humans, even if the lights are varying or when faces change due to age or they are obstructed with accessories, facial hair and so on.But it remained a fairly challenging computer vision problem until a few years back. Deep learning methods have been able to leverage large datasets of faces and learn various representations of faces, thereby allowing modern learning models to perform well and better.Facial recognition may be used to identify person in a photograph ...
Read MoreWhat are layers in a Neural Network with respect to Deep Learning in Machine Learning?
A neural network can contains any number of neurons. These neurons are organized in the form of interconnected layers. The input layer can be used to represent the dataset and the initial conditions on the data.For example, suppose the input is a grayscale image, the output of every neuron in the input layer would be the intensity of every pixel of the image.This is the reason we don’t count the input layer as a part of the other layers in the neural network. When we refer to a 1-layer net, we actually refer to a simple network that contains one ...
Read MoreExplain what a neuron is, in terms of Neural Network in Machine Learning.
A neuron is a mathematical function that takes one or more values as input and outputs a ingle numerical value −It can be defined as follows −Here, ‘f’ refers to the function.We first computed the weighted sum of the inputs xi and the weights wiThe weight wi is also known as the activation value or activation function.The input xi can be a numerical value that represents the input data or it can be an output from other neurons if the neuron belong to a neural network.The weight wi is a numerical value that can be used to represent the strength ...
Read MoreWhat is a Neural Network in Machine Learning?
A neural network can be understood as a network of hidden layers, an input layer and an output layer that tries to mimic the working of a human brain.The hidden layers can be visualized as an abstract representation of the input data itself. These layers help the neural network understand various features of the data with the help of its own internal logic.These neural networks are non-interpretable models. Non-interpretable models are those which can’t be interpreted or understood even if we observe the hidden layers. This is because the neural networks have an internal logic working on its own, that ...
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