Machine Learning – Learn the Basics


All of us have seen movies where robots that were designed to make life simpler for humans ultimately turned on us. These robots were extremely capable and intelligent machines with the ability to think and make their own decisions. They observed the world around them and based on their understanding, behaved accordingly. Driven by artificial intelligence (AI), these machines went from helping hands to sentient forces of destruction. All of this might be limited to fiction, for now. However, this is a lot easier said than done. At the core of it is the concept of machine learning where, as the name suggests, machines are fed with data and taught how to respond to different situations.

Machine Learning – What Is It?

Machine learning (ML) has been defined as a subset of AI that gives systems the ability to learn, adapt, and improve. Based on different experiences, these systems can learn new things without being programmed for said task. Moreover, these systems are given access to data that they then use to learn.

As students, we learn by virtue of being provided information in class, at home, or in our day-to-day lives. Similarly, for machine learning, the machine requires input. This includes training data and knowledge graphs along with other relevant data sources. If you want access to data sets for machine learning, you can use Kaggle. They have thousands of public data sets to go through to learn and implement your machine learning expertise. Using suddenlink internet you can get started with your machine journey. If you want to learn more, get in touch with the  Spectrum offer

The Importance of Machine Learning

Arthur Samuel was the first person to use the term ‘machine learning’. He had designed a program to play checkers and he observed that the more time it spent playing, the better it got. It did this by gaining experience and using different algorithms to predict movements. This showed that computers are smart enough to learn as humans do. Moreover, it has shown that it can do the job a lot quicker and at scale which is not something humans can do.

Leveraging the immense capabilities of ML, one system is able to do the jobs of multiple people. Furthermore, when we integrate it with the internet of things (IoT), we have a system that can work on its own, around the clock. The reason why ML is considered such a strong driving force is its ability to do the job on its own once data has been fed. For an ML system to operate as it needs to, the information that you input into it will determine how well it will perform.

A system that is able to analyze, process, and present data autonomously has the ability to improve the decision-making process and skyrocket efficiency since computers do not suffer from fatigue. Moreover, thanks to all the processing power available, operations may be scaled to new heights.

Real World Uses for Machine Learning

Machine learning is not limited to theory; it is out there being used in the real world. Many organizations have already begun an ML rollout across different industries. It has been observed that some 41% of organizations have made significant advances in AI implementation.


Brokerage firms, fintech startups, and banks have employed different ML systems in an effort to automate trading, provide financial services, and across other departments. The aim is to make the process of making, saving, investing, and using your money as smooth as possible. This could be something simple like using an AI chatbot or something large-scale such as anti-money laundering systems. ML can be used to go through customer documentation when opening accounts or when payments are being processed.

Fraud Detection

Using AI, the banking sector can use ML to sift through thousands of transactions and trace suspicious activity. In the past, this was done by humans which takes a lot of time and there is always a chance that something slips through, by mistake or intentionally. With AI, it runs 24/7 combing through countless spools of data. Since the parameters can be programmed into it, any deviation from this will be flagged as soon as it is detected. Capgemeni, the technology services firm, has claimed that using fraud detection systems with ML can improve fraud detection by 90% and reduce fraud investigation time by almost 70%. This is very telling of the boundless capacity that ML has for growth.


In a world where there is still no cure for cancer and aids, any and every resource is invaluable. With machine learning, large patient data sets can be studied and used in the discovery of cures. They can be used to study patient histories which can allow for more specific treatments. Moreover, these ML systems can be used to automate processes to remove human error which could save countless lives.

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