machine learning introduction

machine learning introduction
machine learning

Machine learning is the study of computer algorithms that are automatically refined based on experience and data usage. Considered part of artificial intelligence.
Machine learning is a branch of artificial intelligence that includes algorithms or techniques for creating data models automatically

Types of machine learning

1)Supervised Learning

Supervised Learning describes a class of problems that a model uses to study the relationship between sample inputs and target variables. An application in which examples of input vectors in training data are included in each target vector is called supervised learning problems.

types of supervised learning problems:

classification, which predicts classroom etiquette, and
regression, which predicts numerical values.

2)Unsupervised Learning

Unsupervised learning uses a model to describe a class of problems that describe or extract links in data. Thus, unsupervised learning has no teachers to whom the model can be applied, as in the case of supervised learning.
there are several types of unsupervised learning. A density estimate that summarizes data distribution and clustering that looks for groups in the data.

Clustering: Unsupervised learning problem when groups are in data.
Density Estimation: An unsupervised learning problem using data distribution.

3)Semi-Supervised Learning

Semi-Supervised Learning is controlled training in which the training data contains very few marked examples and a large number of unlabeled examples.

The goal of a semi-managed training model is to use all available data effectively, not just tagged data as in controlled training.
In a semi-managed training course, we are given some marked examples and we have to do what we can from a large collection of unlabelled examples. Even the label itself may not be the truth of the predictions we expect.

4)Self-Supervised Learning

refers to unsupervised learning problems in the form of controlled learning problems to implement controlled learning algorithms to solve them.

Controlled learning algorithms are used to solve alternative problems or pretext problems. The result is a model or representation that can be used to solve the original (actual) modeling problem.

Machine learning applications

⦁ Neural networks and “dream machines”
Nowadays, it may be easier to list the areas of research and development that Google – or rather, parent company Alphabet – are not working on, than trying to sum up Google’s technological ambitions.

Needless to say, Google has been very busy in recent years as it has diversified into areas such as antiaging technology, medical devices, and – perhaps most appealing to technicians – neural networks.

The most visible development in Google’s neural network research is the DeepMind network, the “dream machine.” It’s the same network that creates those psychedelic images that everyone was talking about a while ago.

According to Google, the company is investigating “nearly every aspect of machine learning,” which will lead to exciting developments in what Google calls “classic algorithms” and other uses, including natural language processing, language ranking, and translation systems, and demand prediction.

⦁ Virtual assistant
So if virtual assistant technology can leverage VR technology, where will these devices ultimately take us? Virtual assistants like Cortana and Siri are already known to understand human language as well as chatbots. As these devices progress to becoming more and more human, we will gain more insight into these artificial intelligent assistants, opening up possibilities in our everyday lives.

By the way, if you’re not sure where to start with virtual assistants, we recommend picking up the Microsoft Kinect game. It’s a perfect introduction to the futuristic world of virtual assistants



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