10 Machine Learning Techniques and their Definitions – AiThority

Posted: December 9, 2019 at 7:52 pm


without comments

When one technology replaces another, its not easy to accurately ascertain how the new technology would impact our lives. With so much buzz around the modern applications of Artificial Intelligence, Machine Learning, and Data Science, it becomes difficult to track the developments of these technologies. Machine Learning, in particular, has undergone a remarkable evolution in recent years. Many Machine Learning (ML) techniques have come in the foreground recently, most of which go beyond the traditionally simple classifications of this highly scientific Data Science specialization.

Read More: Beyond RPA And Cognitive Document Automation: Intelligent Automation At Scale

Lets point out the top ML techniques that the industry leaders and investors are keenly following, their definition, and commercial application.

Perceptual Learning is the scientific technique of enabling AI ML algorithms with better perception abilities to categorize and differentiate spatial and temporal patterns in the physical world.

For humans, Perceptual Learning is mostly instinctive and condition-driven. It means humans learn perceptual skills without actual awareness. In the case of machines, these learning skills are mapped implicitly using sensors, mechanoreceptors, and connected intelligent machines.

Most AI ML engineering companies boast of developing and delivering AI ML models that run on an automated platform. They openly challenge the presence and need for a Data Scientist in the Engineering process.

Automated Machine Learning (AutoML) is defined as the fully automating the entire process of Machine Learning model development right up till the process of its application.

AutoML enables companies to leverage AI ML models in an automated environment without truly seeking the involvement and supervision of Data Scientists, AI Engineers or Analysts.

Google, Baidu, IBM, Amazon, H2O, and a bunch of other technology-innovation companies already offer a host of AutoML environment for many commercial applications. These applications have swept into every possible business in every industry, including in Healthcare, Manufacturing, FinTech, Marketing and Sales, Retail, Sports and more.

Bayesian Machine Learning is a unique specialization within AI ML projects that leverage statistical models along with Data Science techniques. Any ML technique that uses the Bayes Theorem and Bayesian statistical modeling approach in Machine Learning fall under the purview of Bayesian Machine Learning.

The contemporary applications of Bayesian ML involves the use of open-source coding platform Python. Unique applications include

A good ML program would be expected to perpetually learn to perform a set of complex tasks. This learning mechanism is understood from the specialized branch of AI ML techniques, called Meta-Learning.

The industry-wide definition for Meta-Learning is the ability to learn and generalize AI into different real-world scenarios encountered during the ML training time, using specific volume and variety of data.

Meta-Learning techniques can be further differentiated into three categories

In each of these categories, there is a unique learner, meta-learner, and vectors with labels that match Data-Time-Spatial vectors into a set of networking processes to weigh real-world scenarios labeled with context and inferences.

All the recent Image Processing and Voice Search techniques use the Meta-Learning techniques for their outcomes.

Adversarial ML is one of the fastest-growing and most sophisticated of all ML techniques. It is defined as the ML technique adopted to test and validate the effectiveness of any Machine Learning program in an adverse situation.

As the name suggests, its the antagonistic principle of genuine AI, but used nonetheless to test the veracity of any ML technique when it encounters a unique, adverse situation. It is mostly used to fool an ML model into doubting its own results, thereby leading to a malfunction.

Most ML models are capable of generating answer for one single parameter. But, can it be used to answer for x (unknown or variable) parameter. Thats where the Causal Inference ML techniques comes into play.

Most AI ML courses online are teaching Causal inference as a core ML modeling technique. Causal inference ML technique is defined as the causal reasoning process to draw a unique conclusion based on the impact variables and conditions have on the outcome. This technique is further categorized into Observational ML and Interventional ML, depending on what is driving the Causal Inference algorithm.

Also commercially popularized as Explainable AI (X AI), this technique involves the use of neural networking and interpretation models to make ML structures more easily understood by humans.

Deep Learning Interpretability is defined as the ML specialization to remove black boxes in AI models, providing decision-makers and data officers to understand data modeling structures and legally permit the use of AI ML for general purposes.

The ML technique may use one or more of these techniques for Deep Learning Interpretation.

Any data can be accurately plotted using graphs. In Machine Learning techniques, a graph is a data structure consisting of two components, Vertices (or nodes) and Edges.

Graph ML networks is a specialized ML technique used to connect problems with edges and graphs. Graph Neural Networks (NNs) give rise to the category of Connected NNs (CNSS) and AI NNs (ANN).

There are at least 50 more ML techniques that could be learned and deployed using various NN models and systems. Click here to know of the leading ML companies that are constantly transforming Data Science applications with AI ML techniques.

(To share your insights about ML techniques and commercial applications, please write to us at info@aithority.com)

Read more from the original source:

10 Machine Learning Techniques and their Definitions - AiThority

Related Post

Written by admin |

December 9th, 2019 at 7:52 pm

Posted in Machine Learning