Top 10 Machine Learning Algorithms for an ML engineer

Top 10 Machine Learning Algorithms for an ML engineer

Machine learning and artificial intelligence have been making rounds in the IT industry in a full-fledged manner. All the advancements are in the direction of bringing in business intelligence irrespective of the industry niche.

This has created a boom for data scientists who strive every day to create better machine learning algorithms and techniques. As a progressive step, many are approaching a machine learning consulting company in order to incorporate the newest technologies in their business.

So, the question is why is there such a massive surge in research and development of machine learning algorithms? What does it constitute, and how helpful can it be for your business? All of these questions can be answered once you understand its scope.

Scope of the Machine Learning Algorithms:

According to a report by world-renowned Forbes, “The global machine learning market is projected to grow from $7.3B in 2020 to $30.6B in 2024, attaining a CAGR of 43%“. There is a lot of dependability on the machine learning algorithms.

A simple statistic that reflects its importance and why it is so dependable is that about 75% of people who have Netflix subscriptions have opted to watch the suggested content without knowing that it is the machine learning algorithm that has learned the likes of the particular user and suggests relevantly.

That is the factor that is remarkable about machine learning algorithms, They learn by themselves and become more and more reliable over time due to the massive amount of data that is fed to the system.

Many small and large organizations are utilizing intelligent data science to implement AI applications, Chatbots, Fraud analysis, and process optimization/automation. Before we dive straight into the top machine learning algorithms, it is necessary to understand its classification and hierarchy.

Classification of Machine Learning Algorithms:

Machine Learning algorithms are quite a broad concept. Here we classify them clearly.

Machine Learning:

1. Supervised Learning

Regression

– Decision tree

– Linear Regression

– Logistic Regression

Classification

– SVM

– Naive Bayes

– K-Nearest Neighbors

2. Unsupervised Learning

Clustering

– K-Means

– K-Medoids

– Mean-Shift

Dimensionality Reduction

– Principal Component Analysis (PCA)

– Feature Selection

– Linear Discriminant Analysis (LDA)

3. Reinforcement Learning

– Markov Decision Process

– Q-Learning

Based on this division, we now try to understand which machine learning algorithms are the most viable ones and what are their techniques.

Top Machine Learning algorithms

Naive Bayes Classifier algorithm

The simple Bayes theorem is used to calculate the probability of an event occurring under the condition of another event that has occurred preceding it. The Naive Bayes algorithm considers all the variables independent of each other.

This algorithm can conduct tasks like classifying data from a web, email, or other documents. It is useful for face recognition, identifying the expression in a text i.e positive or negative meaning, classifying text based on genres like entertainment, politics, health, etc.

The Artificial Neural Networks (ANN) Algorithm

We all understand the significance of the neural network in the human body. It has retentive power and wisdom developed which assists in making decisions. That is exactly what is attempted with an ANN.

It is used massively for face recognition purposes, something that would take a lot of time with human intervention if we consider a database of hundreds and thousands of people. Moreover, the more data fed to the system, the more it will learn. ANN is great for dealing with problems in the real world.

K – Nearest Neighbors Algorithm

A very dependable method of ML in CLustering is this algorithm. It divides data points into small clusters based on similarities and measures the distance between such clusters. Thereafter it delivers the output which is a prediction based on analysis of data of the K nearest neighbors.

For regression problems, it gives the mean as its system output whereas, for classification problems, the expected output is the mode of the surrounding data.

Decision Tree Algorithm

The classification and regression trees (CART) implementation is a widely used concept not just in machine learning but other software development projects. It is a simple concept that is very helpful and with the support of fast-paced computers it serves even more effectively.

The tree branching method lays down a set of all possible outcomes and analyzes which condition is true. A set of answers being “Yes” or “No” help is arriving at an exact outcome that holds all the information. It is a tree-like graph or model representation for decision-making.

Support Vectors Machine (SVM)

In the SVM technique, raw data is plotted in the form of points in an n-dimensional space and the value of its features is assigned coordinates. Thus, the data gets classified. Lines called classifiers can be used to split the data and plot them on a graph.

The technique works as data is classified into classes and a line called the “hyperplane” i.e. a classifier that separates the training data set into classes. The advantage of SVM is that it neither makes a strong assumption about the input data nor does it over-fit it.

Random Forest Machine Learning Algorithm

In this approach, the intention is to create a bunch of decision trees with varying subsets of data. The model is trained several times on random samples of datasets which makes it extremely good at predicting the output variable.

For the final prediction of the random forest algorithm, the output of each of the decision trees is combined together. Random forest algorithm can be easily implemented with a few lines of code and it can also grow parallelly. It is also very resilient in the case of missing data. The output from this system has high accuracy.

In Conclusion

Stated are among the top machine learning algorithms that every machine learning engineer must be familiar with and learn to code and implement. Machine learning looks like a very promising science that will bring unprecedented technological possibilities in the future. This is one of the inexhaustive reasons for incorporating ML in your business operations.