According to trained AI ML engineers, the world needs 5 times more data analysts and ML supervisors to deliver on their project targets. This can be achieved by accelerating research and innovations in the industry by deploying various machine learning algorithms types.
There has been a constant demand from the data science community to make Artificial Intelligence (AI) and machine learning technologies more accessible to the general public, not necessarily trained in these techniques. However, there is one major challenge to making this possible.
It’s related to internet connectivity. In today’s Internet Era, only 35% of the world’s total population has access to some form of stable internet connection. The remaining population is either completely oblivious to internet services or receives intermittently unstable or weak internet signals. This limits the penetration of AI and ML capabilities and its development for good use.
In this article, I have provided a quick overview of the various machine learning algorithms types and where these are currently used or are planned to be adopted in the near future.
Semi-supervised Learning Algorithms
What is a Semi-supervised Learning Algorithm?
Semi-supervised learning is the mid-level data science intelligence modelthat is generalized based on the extent to which training data can be labeled to generate desired output values. A large percentage of the training data is ‘unlabelled’ and that’s why it’s called semi-supervised learning. Most text processing, analytics, and text classifier software are trained on semi-supervised machine learning algorithms types.
Semi-supervised learning algorithms types can be further trained to turn a weak supervision model into an active learning model by collecting new information and integrating these into existing data lakes so as to refine the quality of queries and desired output. Often different types of active learning models are trained alongside the main semi-supervised learning platform to generate high-quality labeled training data. When 2 or more active learning models are used, semi-supervised algorithms have to be trained to “learning to rank”, which means the final output will be based on the “best possible” label.
Agglomerative Clustering
Agglomerative clustering is the well-known machine learning training model that involves the use of customer segmentation for broad spectrum applications. Its bottoms-up approach and often used as a replacement technique to Divisive clustering and added to parametric clustering algorithms like k-Means.
The common types of hierarchical clustering applications include Stock market predictions intelligence, Twitter hashtag discovery and monitoring, and so on.
In fact, the use of agglomerative clustering machine learning algorithms types made the biggest news during the last US Presidential Elections, where researchers and politicians used Twitter and Facebook strategies to predict the mood of voter’s behavior and sentiments.
In the medical field, this clustering algorithm is used in genome sequencing and creating phylogenetic trees to understand the evolution of various species. In the conservation of endangered species, charting evolution history plays a huge role in safeguarding the future of Mother Earth. Another novel application of machine learning models is related to find out the origin of a virus outbreak and how it could be used to re-engineer drugs to combat pandemics, like the COVID-19 pandemic or HIV Aids.
In our future articles, we will extensively cover the applications in spectral clustering and conventional clustering.
Let’s hop to the next.
Gaussian Mixture Model
If you are working on a population database, it is most likely that you would come across mixture models, which are nothing but an extension of probabilistic databases that constitute a major portion of development in ‘unsupervised machine learning algorithms types’. GMMs have been around for some time now in the developmental course of speech recognition, NLPs, and object tracking techniques, commonly used across all Apple, Google, and Samsung devices.
In a top machine learning course dealing with GMMs, you would have to make a clear distinction between various Expectation Maximizations(EMs) and then use techniques like Baye’s theorem.
From pricing models of a booking / ticketing platform used by hotels and airlines industries to product recommendations engines in the e-commerce business, GMMs have found a solid ground for themselves, courtesy enhanced the popularity of top trending programming languages for machine learning, such as Python and R.
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