Table of Contents:

1. Introduction
2. What is machine learning algorithm
   1. What makes machine learning so important today?
   2. Six ways basic machine learning algorithms help in everyday life.
3. Which are the different machine learning algorithms?
4. Role of the Machine Learning Algorithm
5. Responsibility of a machine learning engineer
6. Conclusion

Get ready to take a tour of machine learning algorithms with Atria University.

Though it may sound a tad bit technical at first, we are here to take you through the basics. You will be surprised to know how each one of you is dealing with it so up close and personal. It is actually an integral part of your life as of date. Wondering how?

Today, the use of machine learning algorithms has become inevitable. The advancement in technology and science has made lives simpler by understanding the human brain and easing difficulties. Applications like voice recognition and face detection are clear examples of how a machine learning system plays a pivotal role in our everyday lives. Some more interesting things like Facebook and Instagram recommendations of ‘people you may know’. Google uses a defined machine learning (ML) to suggest related searches. Interestingly, OTT platforms like Netflix, Amazon Prime and more; recommend movies and series similar to what you have already watched.

And of course, some virtual assistants like Alexa, Siri, and Google Assistant, introduced by machine learning algorithms, can play your favourite music track, set your morning alarm or simply follow your command. So it’s not surprising anymore how the use of these systems is spiking every day, and humans are getting more dependent on them. Machines are taking over, and it is clearly for the better.

Therefore, knowing everything about the different machine learning algorithms should be your pressing priority.

What is machine learning algorithm?

To begin with, machine learning is nothing but a subsection of artificial intelligence. The system is trained to comprehend human needs and perform accordingly, with zero human intercede. The algorithm engine efficiently turns data into a structured model with the help of a previous piece of work.

The dependency and growth of machine learning are only surging by the day. A report predicts the machine learning market to touch the figure of INR 7632.45 Bn by 2027 with a CAGR of 37.16%.

So yes, machine learning is widely embraced yet not given the deserved weightage. Let’s begin with discovering its importance in today’s life.
 

What makes machine learning so important today?

Machine learning technology is not new, in fact, it has been significant for years. However, it has now come to prominence for many reasons. Here are some advantages of a machine learning algorithm.

• Undoubtedly, it has not just helped the professional space but also the everyday living.
• It takes away the responsibility of workload by automating everything.
• ML can efficiently handle a large volume of data.
• It is an ideal solution to complicated calculations.
• The data storage solution is an affordable option.
• ML plays a huge role in the success of a business.
• The technology has no barriers or limitations. It is full of furtherance and growth.
• Speed and accuracy.

Six ways basic machine learning algorithm helps in everyday life.

• One of the most common use is google maps for traffic prediction.
• Fraud detection for online transactions.
• Machine learning applications for online video streaming.
• Face recognition for multiple apps.
• The machine learning algorithm implementation in transport services like Uber and Ola can detect your location and store the history data.

Which are the different machine learning algorithms?

Diving further, let’s explore the different machine learning algorithm types:

1. Supervised algorithm

Often come across the supervised and unsupervised algorithm? Let’s begin with the former. To put it simply, the supervised system makes use of past records as examples. That means a set model utilises an old set of inputs to get the output for a new set of inputs. Experts train the system on identifying the variables to achieve the desired level of accuracy.

2. Unsupervised algorithm

The supervised and unsupervised algorithms are completely different from one another. In this system, there is no model based on previous data or examples. The technique is not trained to predict the outcome with any records. On the contrary, the algorithm focuses on grouping by organising the elements gleaned from observations.

3. Reinforcement algorithm

Reinforcement learning works based on feedback and past experiences. The machine constantly undergoes trials and errors and upgrades accordingly. The system involves environmental exposure to gain the greatest knowledge and make error-free decisions. You cannot even imagine how clever the machine could get. It knows the difference between right and wrong.

Commonly used Machine Learning Algorithm List.

Here’s a list of the machine learning algorithms you should know of.

• Linear Regression 
• Logistic regression
• Decision Tree  
• Naive Bayes 
• Support Vector Machine (SVM) 
• K-nearest neighbour
• Random forest  
• Dimensionality reduction
• Gradient boosting

Role of the Machine Learning Algorithm

Currently, a machine learning algorithm is extensively used in every industry. With all the advancements, it’s almost impossible for these domains to function without programming.

• The healthcare sector leverages machine learning widely to analyse the disease and suggest outcomes. The system helps in accuracy with cancer, blood pressure, sugar levels, heartbeat and more health concerns.
• We have already discovered the ability of face recognition, which has been a big benefit to the social media space. Also, filtering spam gets easy with machine learning.
• Security devices and personal virtual assistants at home can only function because of the technology.
• E-commerce websites have gained immensely with this cutting-edge technology. When a user looks up a product, he comes across it even while surfing anything else on the internet.
• The financial sector is also banking on machine learning tools for services like portfolio management and investments.
• The trading industry also implements certain techniques to automate the process for future deals.
• Machine learning algorithms have helped the transportation space exceedingly by giving out accurate ETAs, directing secured routes, and predicting breakdowns.

Barring the mentioned ones, machine learning is also a big deal in sectors like oil and gas, automation, stock market, online marketing, language translation, police department and automation. Well, is there any industry left?

Responsibility of a machine learning engineer

The role of a machine learning engineer is a bit complex, involving designing, building and testing. The ML master must be capable of the following:

• Understand and make use of the ML technique wisely.
• Execute AI-driven solutions while working on a perplexing problem.
• Improvising continuously.
• Working on image analysis.

This requires a great deal of expertise. If you are considering a career in machine learning, there has never been a better time. Some of the top positions are – Artificial Intelligence Scientist, Computer and Information Research Scientist and Software Developer. Curious to know their salary?

One Last Thought:

Forbes reckons a 44% growth in machine learning between 2017 – 2024. A whopping figure of $20.83 billion is predicted for 2024. Even the average salary of an AI expert in future is beyond your imagination. However, all of this is unattainable without a professional degree.

Atria university goes beyond the conventional classrooms. You will have the opportunity to engage yourself in the signature machine learning and predictive human-centred algorithm project. We encourage collecting a dataset of customers and creating an ML model to score incoming users. With this, you can be certain about transcending an entry-level job. Take the plunge and leave the rest to us.