A Detailed Primer on Machine Learning Algorithmsby@AmyraSheldon
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1,864 reads

A Detailed Primer on Machine Learning Algorithms

by Amyra SheldonAugust 14th, 2019
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Machine Learning has taken over the world and it has come out from the fancies of science fiction world to business intelligence reality. 74% of over 1,600 surveyed business owners, decision-makers, and tech leaders consider ML a game-changer, with the potential to transform their job and industry. I am a passionate tech writer. I write on latest technologies (Blockchain, AI, ML) for Valuecoders. In this blog, I will try to bring out the essence for the types of machine learning algorithms and will explain the purpose and benefits of different types of Machine Learning algorithms in detail.

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Machine Learning has taken over the world and it has come out from the fancies of science fiction world to business intelligence reality. It can be termed as a new age business tool that entails multiple elements of business operation. 

If we go by the stats, 74% of over 1,600 surveyed business owners, decision-makers, and tech leaders consider ML a game-changer, with the potential to transform their job and industry.

The illuminating power of Machine Learning has widened the horizon for businesses, machine learning developers and tech enthusiasts as Machine Learning has a direct impact and influence on their business performance, career domain, and technical expertise. 

So, if you are a business owner or a machine learning expert, it is thereby important for you to understand what different kinds of machine learning algorithms can bring to the table?

After a through discussion with our dedicated development team of AI we have prepared a detailed primer on the algorithms. In this blog, I will try to bring out the essence for the types of machine learning algorithms and will try to explain the purpose and benefits of different types of machine learning algorithms in detail:

Types of Machine Learning Algorithms:

Source: Google Images

Before digging into the practicalities and implementation of machine learning algorithms in brief. Let’s try and understand what exactly is Machine Learning? 

Machine learning can be termed as an application of artificial intelligence (AI) that provides computing systems the ability to automatically learn, improve and reiterate from experiences without being explicitly programmed.

The major application or examples of Machine Learning could be seen in medical diagnosis, image processing, prediction, classification, learning association, regression, etc.

It has acquired a major market through its application across different industries. But these major applications are based on machine learning algorithms which are the building blocks for these application functionalities.

Read Related Technology: Top 5 Trends of Artificial Intelligence (AI) 2019

The first major machine learning algorithm that I am going to talk about is:

1. Supervised Machine Learning Algorithms:
As the name suggests, Supervised Machine Learning Algorithm operates on the supervision which means it has direct control over the desired output valued attribute. In this case, a machine learning developer compiles and bundles information to restrict boundaries on how the algorithm behaves. It can also be termed as a spoon-feed mechanism where you can predict and evaluate the output values from the prescribed conditions either through a yes/no or true/false.

The goal of this machine learning algorithm is to predict the output of the variable data through labeled sample data.

Source: Google Images

Supervised Machine Algorithm includes two major processes:

  • Classification: is the process in where the input data has been labeled into categories as per the previous data samples that manually instructs the algorithm to differentiate between different types of data such as an optical character, image, and binary recognition.
  • Regression:  is the process of identifying processes and estimating the predictions of desired data outcomes. The system has to understand numbers, their groups, and labels like (height and width).

The most commonly used Supervised Machine Learning Algorithms have been listed down below:

  • Linear Regression
  • Logistical Regression
  • Random Forest
  • Gradient Boosted Trees
  • Support Vector Machines (SVM)
  • Neural Networks
  • Decision Trees
  • Naive Bayes
  • Nearest Neighbor

Use Cases: Supervised Machine Learning Algorithms

Now that we have understood the meaning of a supervised machine learning algorithm, let’s discuss the major possibilities of this machine learning algorithm in real-life scenarios and business intelligence. 

The major advantage of using a supervised machine learning algorithm is the prediction of variable outcomes and it could be diluted well with price predictions and trend forecasting in sales, retail, trade, and e-commerce. Marketing and ad-tech operations can also see a valuation in their ad evaluation and real-time bidding process as this algorithm operates on real-time data values that will allow you to assess the price for the ad spaces. This will allow you to keep your marketing budget in moderation without probing extra costs.

Watch Out: Top 5 Artificial Intelligence Trends | Awesome New AI and Deep Learning Technology | 2019

2. Unsupervised Machine Learning Algorithm:

Unsupervised Machine Learning Algorithm is the one that doesn’t involve the direct role of the machine learning developer that means you need to sort out the data without knowing the actual outcome or desired result from the unresolved data set or query.

The major difference between Supervised and Unsupervised Machine Learning Algorithms depends on the type of data set they have been operating. In the case of supervised, the data sets have been labeled whereas, in the case of unsupervised, the operation has been performed on the unlabeled data set or data.

Source: Google Images

The unsupervised machine learning algorithm is used for:

  • exploring the structure of  information
  • extracting meaningful insights
  • capturing  patterns
  • Increasing efficiency

This ML algorithm uses these types of techniques to describe data:

  • Clustering: is the process of exploring data that can be used to segment data into meaningful groups or clusters that are based on the internal behavioral patterns without the prior knowledge of group credentials or authenticity. The credentials have been formed based on the similarities and dissimilarities that sets them apart from the rest.
  • Dimensionality Reduction: There is a lot of distortion in the incoming data. Machine learning algorithms use dimensionality reduction process to remove the unwanted noise and thus it can be used to extract relevant information.

The most commonly used unsupervised machine learning algorithms are:

  • k-means clustering
  • t-SNE (t-Distributed Stochastic Neighbor Embedding)
  • PCA (Principal Component Analysis)
  • Association rule

Use Cases: Unsupervised Machine Learning Algorithms

The most viable application of this mi algorithm can be seen in the field of Digital Marketing and Ad Tech as these two fields requires a valuation and validation in the terms of customer information which can be used to build user personas and demographics that can be variably used to optimize their services as per user’s validation.

It can also be used to extract insights as it works on the unknown principles of a data set whose outcome is uncertain, thus to measure the effectiveness of the business operation, the machine learning algorithm stands apart from a supervised machine learning algorithm.

A popular example that equips modern data management is Lotame and Salesforce that are among the most cutting-edge data management platforms that implement this ML algorithm.

3. Semi-Supervised Machine Learning Algorithm:

As the name suggests, this ML algorithms represents a middle ground when it comes to the eccentricity of both the algorithms namely supervised and unsupervised machine learning algorithms. It combines the aspect of both the preceding ml algorithms to build something of its own.

Source: Google Images

Let’s discuss how semi-supervised algorithm combines the functionality of both the algorithms discussed above thereby building a by-product of its own:

  • To shape the requirements of the business operations, it works on the labeled sample data.
  • Limitations encountered in labeling the un-stamped data can be incurred with the help of labeled sample data predictive analysis approach to sample data that can be treated as a psudeo data set.
  • The merger of a labeled and pseudo data set can be used to create a distinctive algorithm that combines the descriptive and predictive analysis approach of supervised and unsupervised ml algorithms.

Use cases: Semi-Supervised Machine Learning Algorithm:

The major application of the Semi-Supervised Machine Learning Algorithm could be seen in industries like Legal and Healthcare while managing web content classification, speech and image predictive analysis through this ml algorithm, which can be termed as combustion of a  supervised and unsupervised machine learning algorithm.

For instance,  uClassify and  GATE (General Architecture for Text Engineering) are the perfect examples for the merger of two machine learning algorithms.

4. Reinforcement Machine Learning Algorithms:

Reinforcement Learning, a  Machine Learning Algorithm incorporating the essence of Artificial Intelligence that allows software and machine user agents to automatically determines the ideal behaviour within a specific context, in order to maximize performance. Simple reward feedback is required for the agent to learn its behavior, this is called a reinforcement signal.

Reinforced ML algorithm uses the technique called exploration/exploitation. These mechanisms are simple - the action takes place, the consequences are observed, and the next action considers the results of the first action.

Most commonly used  Reinforcement Learning Algorithms include:

  • Q-Learning
  • Temporal Difference (TD)
  • Monte-Carlo Tree Search (MCTS)
  • Asynchronous Actor-Critic Agents (A3C)
  • Use Cases: Reinforced Machine Learning Algorithms

Reinforcement Machine Learning fits the bill in instances where limited or inconsistent information is available. In this scenario, the machine learning algorithm operates on the data sets based on interactions and relevant processes.

The most popular example of reinforced learning could be seen in self-driving cars such as Waymo. 

Another popular example is AlphaGo, that went head to head with the second-best Go player in the world and outplayed him by calculating the sequences of actions out of the current board position.

Final Notes:

As you can clearly infer the different types of machine learning algorithms solve a different purpose. However, combining the power of different machine learning algorithms together can help in handling a wide variety of tasks and extract valuable insights from invariable data sets.

So, whether you have a business which is a taxi app or food delivery service or even any other app your next and current business requirement would be benefited from machine learning algorithms. If you require professional expertise then you can also hire software developers from India for better results.