It is a subcategory of artificial intelligence and it the subset that deals with computation with focus on analytics and interpretation of structure and patterns in data so that learning, reasoning, decision making can be activated. Machine learning involves feeding computers with algorithms which is a very large amount of data. These computers then analyze the data, make reasoning about them and make decisions and recommendations on the data.

If per-adventure, there be any rectifications made on the data, the machine or computer incorporate the correction and uses it for future design making and analytics.

According to Netapp, there are three parts to machine learning. The parts are computational algorithm, variables and features that facilitate decision making, and basic knowledge that allows the machines to learn. Necessarily, machine learning is possible through the availability of data.

The functionality of machine learning in these parts works in the model that proposes that, parameter data is presented, algorithm is run, and then adjustments are made until the algorithm learning agrees with the desired answer. A key thing with machine learning is that, data scientists have the knowledge of the data which is presented and a hypothesized outputs is worked towards. Achieving a desired output means that, the machine has learned.

Do we need machine learning?

We live in the ocean of data. Literally, we feed on data. Before we fly out of the country, we forecast the weather to know if it is safe or not. This is just to mention a few, data is the life of all long standing businesses like the manufacturing industry, healthcare, pharmaceuticals, finance and more. Machine leering has the potential to unlock barriers of all sectors in the global – it’s been doing that since inception anyways…

Although there were many misconception about the future of machine learning several decades ago, the world have been able to recognize tremendous influence since extensive research has been deployed to advance the field 10 years ago.


Machine learning provides maintenance (predictive) and monitoring to the manufacturing industry. In the 70s, artificial intelligence had been channeled to vehicle industry, and with the advent of machine learning, we can always have outstanding predictive analytics in the manufacturing firms.

Modern manufacturing industries are begging to integrate machine learning all through the production process. What is being used is the predictive algorithm. One of the uses if the predictive analytic part of the machine learning to the manufacturing industry, is to plan machine maintenance.

Another thing machine learning does is quality control and this is done with inbuilt super intelligence. A machine learning based computer vision is being used to learn and differentiate between good and flawed products and this enables the industry to know every defect and improve on quality. Necessarily, a flowed and utmost quality is developed and integrated to allow machine effectively differentiate with its vision.

Inventory management: Over $172,000 are wasted yearly in the US on repetitive and time wasting tasks such as searching for order numbers, and calculating of orders. But with the advent of machine learning, production and logistics related work hours are saved in thousands of hours every year.   A lot of money is being spent on machine learning

IDC data showed that $745 Billion currently spent on IoT would increase to $1 trillion by 2022. Also, machine learning investment would rise to $16 billion as compared to $1 billion we had in 2018. This is to show the gigantic investments being made on machine leering because of the use.


Robots are gradually taking over surgical procedures, making surgical operations smart and smooth. A Da Vinci robot has reached the limelight in the robotic industry. With the robot, surgeon could manipulate the robot limbs to perform operations. With robotic life of machine learning in the medical industry, medical practitioners would be able to perform operations without having to break tissues and also helps doctors to operate on patients in a given tight space. Looking at diagnostic imaging, computer vision using artificial intelligence is being used and is still of the popular breakthrough recorded. There is a great area of focus in the medical field which is called “Crowdsourced” medical data collection.

A lot of data pool has been collected in order to make reason of a live health data. A research has been deployed to this area to assess patients medical challenges over time e.g., Asperger’s syndrome and Parkinson disease. With this, patient’s facial recognition would be advanced using interactive apps to be able to monitor diseases and the progress of medication. IBM is also collaborating with Medtronic to store all health data available to us in order to understand diabetes and insulin. Also, there was also a move to buy Truven Heath for $2.6 billion dollars.  In the healthcare, the problems or diagnosis, prevention and treatment are being tackled today – thanks to machine learn.


There are numerous ways which machine learning is being used in energy industry. One of the ways is in anomaly detection. Often times, we don’t see malfunctioning in energy equipment’s as we often stay away from the power surge for safety. But with the use of machine learning, the artificial intelligence algorithm monitors and analyzes energy consumption, conducts predictive analysis to show the likely problems and improves performance.

Another use case is defining the consumption of energy that would be consumed per each day. With machine learning models, it could predict consumption and generate energy demands. An interesting fact in this is that these predictive abilities can be used by facility managers and energy companies to integrate energy saving policies for maximization.

It can indicate maximum price: machine learning model designed for energy companies can optimize use of neural networks to forecast energy consumption and optimize pricing recommendation on which company targets can be reached. This is already in place in the world today.

Cost effective in the energy sector is effective with the use of machine learning model. Its algorithms through maximum selection of contracted capacity, helps in minimizing actual expenses cost.

Other applicability includes, energy disaggregation, analysis of energy production, realtime monitoring, recommendation and many others.

Machine learning will enable critical application and data and resources in our world today.

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