Machine Learning is in demand and right now everything revolves around Data. It is core area of Artificial Intelligence which help computer learn and grow itself. Machine learning (ML) is the scientific study of algorithms which are used in a wide variety of applications, like email filtering, image recognition, predictions, recommendations etc. It is closely related to computational statistics, which focuses on making predictions using computers.
As a software development company, we know the technologies are continuously evolving and so the demands are. We are working on some of the in-house projects, in order to know, understand, and implement ML.
If you are looking for any consultation regarding Machine Learning for your business, get in touch with us and we will help you finding the best solutions for you.
In order to help enterprises to build, adapt, and implement Machine Learning, we have developed Movies app using MEAN stack technologies and we are updating it to the next level by introducing ML to it for movie recommendation.
The first thing we required was a training set that will help us with the DATA we needed to provide the information to users on the basis of their preferences and likings. We have created few recommendation algorithms like content-based, popularity based and collaborative filtering.
Our whole idea was to build an engine that calculates the similarity between movies based on certain metrics and suggests movies that are most similar to a particular movie that a user liked.
Similarly, we are working on Gadgets Shopping recommendation app – Eleet. Eleet is the easiest way out there to catalog, understand and showcase all digital devices including smart phones, tablets, laptops, TVs and cameras. We are trying to create algorithm that uses collaborative filtering to process data from a large set of people and finding a smaller set with taste similar to other users.
Unsupervised learning algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. The algorithms, therefore, learn from test data that has not been labeled, classified or categorized.
We have recently ran into a machine learning data set that has data on 6000 Canadian wines that includes a 1-10 quality rating, which seems like a great excuse to build a neural network that can predict the 1-10 quality rating based on factors like residual sugar and alcohol content.
Effectively, this neural network attempts to match the wine palate of whoever put this data set together. Brain.js is a simple npm module for building neural networks, a common machine learning model that you might see in an undergraduate AI class. Training a Neural Network with Brain.js.
In any Machine learning process, the most important asset is DATA to learn from. Data can be text, images, video, or anything that can be used for any supervised learning. In order to predict the data point of each label, we always consider taking labeled data.
Semi-supervised learning is an approach to machine learning that combines a small amount of labeled data with a large amount of unlabeled data during training.
Thousands of people on the internet share their experiences and choices so, collecting information about any topic has become easy.
Our Movie recommendation system called as ViewFlix, searches for the information on the internet by providing individualized recommendations. This framework proposes stream-based recommendations where the synopsis of each movie is taken as data like directors, actors, writers, etc, which helps to understand the preferences of users.
Reinforcement learning is about making decisions sequentially. Due to its generality, the field is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. Reinforcement learning algorithms are used in autonomous vehicles or in learning to play a game against a human opponent.
Feature learning algorithms, often attempt to preserve the information in their input but also transform it in a way that makes it useful. However, data such as images, video, and sensor data doesn’t algorithmically define specific features. An alternative is to discover such features or representations through examination, without relying on explicit algorithms.
Feature learning can be either supervised or unsupervised. In supervised feature learning, features are learned using labeled input data. Examples include artificial neural networks, multilayer perceptrons, and supervised dictionary learning.
In unsupervised feature learning, features are learned with unlabeled input data. Examples include dictionary learning, independent component analysis, autoencoders, matrix factorization, and various forms of clustering.