#unsupervised machine learning

[intelligentliving]

AI Identifies Molecular Patterns Of Tumors To Aid With Treatment Plans

A new deep-learning algorithm can analyze genomic data quickly and accurately, picking out key patterns to classify tumors and improve diagnosis.

[ict-123]

Unsupervised Learning Market to Reach $86.1 Billion, Globally, by 2032 at 35.7% CAGR: Allied Market Research

/PRNewswire/ -- Allied Market Research published a report, titled, "Unsupervised Learning Market by Technology (Natural Language Processing

Unsupervised learning is a branch of artificial intelligence that involves the training of an algorithm on unstructured data. Unstructured data is defined as data that does not have any predefined categorizations or labels. 

[usaii]

Supervised vs Unsupervised Machine Learning: Understanding the Contrasts | USAII®

Learn the nuances of supervised and unsupervised machine learning from the perspective of an AI professional. Delve deeper into their functioning, characteristics, and types of algorithms used; and pave a successful AI career.

Read more: https://bit.ly/3XGcm2W

Supervised Learning, supervised learning algorithms, supervised learning in machine learning, supervised and unsupervised machine learning, supervised learning models, unsupervised learning methods, Unsupervised Learning, unsupervised learning algorithms, unsupervised machine learning, AI applications, machine learning algorithms, machine learning techniques, supervised and unsupervised learning

[howlongisthedata]

Great presentation from ING using TF-IDF over BERT in a MVP / unsupervised text clustering tool for product UX managers.

[anjuumkhanna]

Anjuum Khanna- What is Machine learning

Visit: http://anjuumkhanna.in/

[anjuumkhanna]

Anjuum Khanna - What is Machine learning

I, Anjuum Khanna, would give some insights on what Machine Learning. In simpler words machine learning is an application of artificial intelligence that enables the system to learn automatically and the best part of this application is you don’t need to program explicitly. Anjum Khanna also defines it in a different way that it focuses on the development of computer programs that can access relevant data and use it learn for themselves. This learning process starts with data or observations (stated and observed), which we can provide in terms of examples or instruction. This learning can also be gathered through direct experience. Primary aim of machine learning is to allow computers to learn automatically without any human intervention or assistance. Machines should also adjust their actions accordingly. As per author’s definition we can also say that machine learning is subfield of AI. We can see many examples of machine learning such as Siri, Netflix, Google maps, Uber etc. This can tell that how machine learning has upgraded our living. We can learn more about it by knowing more about machine learning methods. As per Anjum Khanna below methods can tell better about machine learning:- 1. Supervised machine learning algorithms: – It is called supervised learning because the process of an algorithm learning from the training dataset can be thought of as a teacher is supervising learning process. We know the correct answers, the algorithm makes predictions on the basis of training data and it gets corrected by the teacher. Learning stops when the algorithm achieves an acceptable level in terms of performance. In Anjum Khanna’s words we can further learn this by few real life examples as 2. Unsupervised machine learning algorithms: – This type of machine learning is more closely aligned with artificial intelligence. On further analysis we can say that in this kind of machine learning a computer can learn to identify complex processes and patterns without a human guidance. Although unsupervised learning is prohibitively complex for some simpler enterprise use cases, but it is more effective while we need to solve problems that humans normally find difficult to tackle. In Anjum Khanna’s words we can further learn this by few real life examples as I, Anjum Khanna has a very positive vibes that machine learning will do wonders in future. There are few predictions which will be true in future for machine learning:- Usage in applications: – In next few years, machine learning will become part of almost every software application. Soon all of our devices will be embed with capabilities of machine learning. After that our personal device will become personalized device. Usage in service industry: – As machine learning becomes increasingly valuable and the technology matures, more businesses will start using the cloud to offer machine learning as a service. This will allow a wider range of organizations to take advantage of machine learning without making large hardware investments or training their own algorithms. As in cutting throat competition personalized service is required in service industry and machine learning will resolve that issue. So these are only few examples, there will be a great revolution with machine learning. But one trend is consistent across all of these predictions. As this technology advances, more businesses will embrace the AI revolution. All the best for disruptive future!!!

[neelamtyagi]

Generative Adversarial Network (GAN) in Unsupervised Machine Learning | Analytics Steps

GANs are neural networks in unsupervised machine learning used for generative modeling that entails a model to compose new samples mapped from the existing population of data instances.

“It’s all about legit”, legit to images and voice generation, discover how?

GAN is about creating a portrait or composing ritornello whether you want to create pictures of any celebrity or data in data-limited solutions. GANs are a simple solution to complex problems that are hard to compare with other deep learning realms. 

Explore how two neural networks train each other through the adversarial process, a branch of unsupervised learning technique in machine learning. 

[sakshimohitedf]

Learn Types of Machine Learning Algorithms with Ultimate Use Cases - DataFlair

Learn about the three different types of machine learning algorithms - supervised, unsupervised & reinforcement learning with use cases of Baidu,Google AQA