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Machine Learning Algorithms

Learn all about machine Learning Algorithms with complet description with us

What is Machine Learning?

ML is a field of data science that deals with providing the machine with the capability to learn without explicitly being programmed.ML is one of the most energizing innovations that one would have ever gone over. As it is clear from the name, it gives the machines that which makes it progressively like people: The capacity to learn. ML is effectively being utilized today, maybe in a lot greater number of spots than one would anticipate. Some prominent subcategories of

Machine learning algorithms are:

  • Supervised algorithms

  • unsupervised algorithms

  • Semi-supervised algorithms

  • Deep Learning

Supervised Machine Learning algorithms

Supervised learning as the name specifies shows the presence of a supervisor as an educator. Essentially supervised learning is a learning wherein we teach or train the machine utilizing information in which the target labels are present. Once the machine has been trained then we present it with data without the presence of a target variable. In the second case, the machine itself determines the correct targets for the data presented to it. Some of the most famous Supervised

algorithms include:

  • Support Vector Machines

  • Linear Regression

  • Logistic Regression

  • Naive Bayes

  • Linear Discriminant Analysis

  • Neural Networks

Unsupervised Learning

Unsupervised learning as the name specifies has no supervisor present, this means that there are no targets or labels present in the training phase. The data consists of just the feature variables and is grouped based only on the feature set. Some of the most famous Unsupervised algorithms

in today's time include:

  • K-means clustering

  • K-NN

  • Principal Component Analysis

  • Singular Value Decomposition

  • Hierarchical Clustering

Semi-Supervised Learning

Semi-Supervised learning is derived from the cumulation of supervised and unsupervised learning. In semi-supervised learning, the data set is partially labeled this means in some of the cases we are provided with the targets but in most cases, the data is still not labeled. Semi-supervised algorithms solve problems by making different assumptions. Some of these assumptions are
listed below:

  • Continuity assumption

  • Cluster assumption

  • Manifold assumption

  • Generative models

  • Low-density separation

  • Graph-based Methods

Deep Learning
The field of Artificial Intelligence at its core is based on the idea that a machine should be able to mimic human intelligence. Deep learning is a sub-field of artificial intelligence, where machines can learn from experience and acquire skills without human involvement. It consists of Artificial Neural networks, algorithms that try to emulate the working of a human brain and learn from a large amount of data provided to them. Similar to how we learn from experience, the deep learning algorithm would perform a task repeatedly, each time tweaking it a little to improve the outcome.

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