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Understanding Common Machine Learning Algorithms and How They Work


By NIIT Editorial

Published on 20/03/2021

8 minutes

We live in an era where automation has replaced most manual tasks. From introducing voice recognition systems to performing surgeries, machine learning algorithms have revolutionized the whole technological process. Data Analysts have observed the democratization of computing tools and techniques to be the high point of this revolution.

Machine learning has instigated a swift growth in the availability of programming tools and techniques, which has become the center of attraction for data scientists. Machine learning applications are winning hearts with their feature of being highly automated and self-modifying without involving any human intervention. This noted improvement is invincible. Here’s a detailed discussion about some common machine learning algorithms and their types, have a look!

Introduction to Machine Learning Algorithms

 

A machine learning algorithm is the study of computer algorithms. It is a set of processes that runs on data used in structuring a production-ready machine learning model. A machine learning algorithm is the core of the machine learning mechanism that is used to accomplish a particular task. Which kind of machine learning algorithm should one use largely depends on the nature of the problem and the availability of the resources.

The machine learning discipline brings various approaches into play that teaches the computer to succeed in a task when no suitable algorithm exists. This learning process involves a series of tasks. From knowing the functioning that plots the input to the output to learning the hidden structure in unlabeled data, a lot of information is imparted to the computer by these algorithms. Through a machine learning course, one gets an insight into the machine learning mechanism that can aid in creating better solutions.

Types of Machine Learning Algorithms

Fundamentally, there are three types of machine learning algorithms that come under basic machine learning:

 

Supervised Learning Algorithms: Supervised learning comprises targets or outcome variables that are used to generate a function that is further used in mapping the inputs to required outputs. This process does not end until this model attains the desired level of accuracy on the data.

 

Any machine learning online course can inform that a supervised learning algorithm is further categorized into two types: Classification and Regression.

While Classification foresees the result of a given sample when the output variable is in the form of categories, Regression anticipates a given sample’s result only when the output variable is in the form of real values. A classification model assists the algorithm by analyzing the input data while predicting labels like “sick” or “healthy”. This lets the user understand the issue with the data if any. On the other hand, the Regression model helps in knowing details like the amount of rainfall, height of a person, etc. by processing the input data.

 

Unsupervised Learning Algorithms: Unlike the supervised learning algorithm, this type of machine learning algorithm only has the input variables and not the outcomes. Through machine learning training it can be learned that unlabeled training data is used for the structuring of the underlying structure of the data. Unsupervised learning algorithms help in clustering populations in different groups that are extensively used for bifurcating customers in different groups for specific interventions.

 

If tech enthusiasts begin to learn machine learning they come across three types of unsupervised learning algorithms: Association, Clustering, and Dimensionality Reduction.

 

Association is widely used in the market-basket analysis. It helps in understanding the probability of the co-occurrence of items in a collection. For instance, Association models will assist in discovering that if a person purchases eggs; they are 80% likely to buy bread too.

 

Clustering takes place when a grouping of samples is required to achieve one object that is similar to the other one in the same cluster rather than to the objects from different clusters.

 

Dimensionality Reduction is to the rescue when a reduction in the number of variables of a data set is required also keeping in mind that the imperative information is precisely conveyed. This type of unsupervised learning can be conducted by using Feature Extraction methods and Feature Selection Methods.

 

Reinforcement Learning: This type of machine learning involves allowing an agent to make decisions for further actions by analyzing the current state that will give the best result. In simpler words, the machine works on a trial-and-error basis. It is trained to take specific decisions through the said method. It learns from its past mistakes which help in making accurate business decisions in the future.

 

Some of the Commonly Used Machine Learning Algorithms

 

Naïve Bayes Classifier Algorithm: Naïve Bayes Classifier is one of the popular learning methods that are based on the famous Bayes Theorem of Probability (building machine learning models specifically to predict diseases and classification of documents). A Naïve Bayesian model is useful and simple to create massive datasets. In machine learning using python, Naïve Bayes Classifier plays a vital role.

 

Classifying a web page, document, email, etc. would be highly daunting and even practically impossible without the Naïve Bayes Classifier Algorithm. ‘Classifier’ here means a function allocating a population’s element value from one of the available categories. For example, the spam filter option in emails is a classifier that allots the label of spam or not spam.

Linear Regression Machine Learning Algorithm: This type of algorithm depicts the association between two variables and displays how one variable affects the other. Linear Regression Machine Learning Algorithm displays the impact that a dependent variable makes by changing the independent variable. The independent variables are known as the explanatory variables because of their nature of explaining the factors that impact the dependent variable. On the other hand, the dependent variables are commonly called the factor of interest or predictor.

Logistic Regression Machine Learning Algorithm: As the name suggests ‘regression’, infers that a linear model fits into the feature space. The application of a logistic function to a linear combination of features is conducted to anticipate the final product of a categorical dependent variable based on predictor variables. For instance, a person wants to predict whether it will rain tomorrow or not. The prediction result will be either rainfall or no rainfall. Since no continuous number is predicted, linear regression cannot be applied. And hence, logistic regression will be used to predict the outcome variable.

Mathematically, linear regression can be represented as

 

y= a0+a1x+ ε

 

Y= Dependent Variable the Target Variable

X= Independent Variable the Predictor Variable)

a0= intercept of the line  that gives additional degree of freedom

a1 = Linear regression coefficient  that is the scale factor to each input value

ε = random error

 

The values for x and y variables are training datasets for Linear Regression model representation.

 

 

Apriori Machine Learning Algorithm: Apriori algorithm is an unsupervised machine learning algorithm that makes the data set’s association rules. From association rules, we mean, the occurrence of item A will automatically lead to a probability of the occurrence of item B. For instance, if you are buying an iPad, chances are high that you will buy the iPad case too. For the algorithm to optimally work, the number of people who simply bought an iPad is observed and then a ratio is derived to understand how many people bought the iPad case. 

 

K Means Clustering Algorithm: K Means Clustering is an unsupervised machine learning algorithm that is commonly used for cluster analysis. The algorithm works on the given data set via using a predetermined number of clusters i.e., k. Hence, the output of the K Means Clustering Algorithm is k clusters along with the input data that is bifurcated within the clusters. This algorithm applies to the group of web pages that are built on similar concepts.

For example, we want to run K Means Clustering Algorithm on Wikipedia search results. For the term “Dove” which means the name of a bird as well as a cosmetic brand, the Wikipedia page will give results on both the meanings. The algorithm of the cosmetic brand will be grouped into one cluster and the bird in the other cluster.

K-Nearest Neighbours Algorithm (KNN): In the KNN algorithm, an entire data set is used as the training set without even dividing the data set into a training set. To find the k-nearest instances to new instances, the KNN algorithm undergoes the entire data set where the value of k is user-specified. Euclidean distance and Hamming distance are the measures that are used to calculate the similarity between various instances. 

 

Final Thoughts

The aforementioned are some of the commonly used machine learning algorithms that have completely changed the technology game. Using these one can master the art of Machine Learning, paving the path for a plethora of opportunities. By getting yourself to understand these algorithms you can provide solutions to the most complex problems. 

Applying for a machine learning certification from a reputed institution like NIIT, will enhance your skill-set and enable you to grab better opportunities in the technology world. To help you in this quest we’ve launched a range of data science programs mentioned below: 

Mentored by seasoned trainers students will be backed with job assurance and the opportunity to work as a data scientist. 

Apply now!



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