What is Machine Learning?
Introduction
The world is changing at a rate faster than you can imagine. New technologies have been found, some of the old ones lost in oblivion, while others revamped or modified. A technology that has really changed the world is Artificial Intelligence. In fact, merely saying that it has changed the world is an understatement. It is a technology that has touched lives far more stronger than anything in the world. AI has the power to change the workings of every industry.
Machine Learning is a branch of Artificial Intelligence, known fondly as AI, and computer science, and they use data and algorithms to behave almost like humans and make predictions that change the way businesses do their business. It uses plenty of mathematics, artificial intelligence and statistics and the experience of data scientists to increase sales, boost customer relationships and drive business.
Machine Learning can make recommendations or predictions, irrespective of the data that comes in, and has the capability to uncover the key points in the data. It is with this insightful data that businesses make critical moves and take decisions that affect the company’s future drastically. As data grows, so will this requirement for quick and accurate data mining.
For every decision that you make about your business, it is the data that supports them, and machine learning algorithms play an important role in that.
How Machine Learning Works?
In simple words, machine learning works by exploring data, identifying patterns and finding meaningful insights, with minimal human intervention. The concept has been around for sometime, but as data became complex, ML also became complex. It has learnt to apply complex algorithms on big data applications quickly and effectively. This could get sophisticated as the data becomes complex, but sophisticated is good because you stay ahead of competition. With correct data predictions generated through ML, you can ace the marketing strategies and come up with unique scenarios that drive desired results.
ML works by analysing the data patterns and making predictions from these data sets. Eventually, they learn through trial and error, and learn to make predictions with new data sets. The developer teaches the machine in a manner similar to how humans learn how to recognise things and make decisions based on that. Eventually, the machine will be able to self-learn. ML is pure mathematics, the machine learning algorithms are created using various mathematical functions, and this can be modified and changed.
There are four ways in which this can be done:
1. Supervised learning
Supervised learning is almost like human learning, in which the data scientists train the AI system with specific rules and datasets. The datasets will have input data and output data, and the machine will be taught what it should look for in the input data. The algorithms learn by example.
2. Semi-supervised learning
This is a mix of supervised learning and unsupervised learning, which will be discussed further down. In this learning process, the machine will be trained only a little bit. The data will be trained only in a small percentage of the whole, while the larger portion will remain unlabelled. The system learns the rules on its own by observing the dataset patterns. Semi-supervised learning is a great option when the labelling process turns expensive, or when you don’t have enough labelled data
3. Unsupervised learning
In an unsupervised learning system, the data scientists let the AI system learn by itself. Observation is the key. So there will be only input data, and no output data. With this, the machine will be able to discover hidden patterns in the data. The amount of data itself will be really huge and the machine has to mine through all the unlabelled datasets.
4. Reinforcement learning
In Reinforcement learning, the AI system learns through an interactive and trial and error system. The data scientist will create a game-like situation with rewards and penalties and the system receives feedback on its actions. There will be rules, but the system will have to figure out how to solve the game.
How Machine Learning boosts business growth
Adopting ML is the way to move forward as it can touch upon every aspect of business, and grow it. Let’s check out the different business aspects it can touch upon.
Natural Language Processing
Machines can now understand natural language. When you enter a query in the search engine, you no longer have to type the exact keywords to get the desired results. You can just type regular sentences, and the Google search engine would give excellent results. Of course, NLP is still evolving, and the computers are still learning, but ML has certainly taken it several steps forward. Voice activated assistants are a great example of that. They are already beginning to understand human language, and learning from past mistakes.
Logistics
Another area of business that ML seriously influences is logistics. The efficiency of the technology in the shipping, storage and sales process is amazing. Take the example of Amazon. The giant retail company has already made huge leaps with ML, and their delivery system has improved so much more. They have learnt customer needs, provide apt recommendations, and increased the buying capacity of customers.
Manufacturing
The manufacturing industry is reaping huge benefits with ML. The entire manufacturing process becomes transparent and more efficient. Inventory management ensures that there is no break in the supply chain at any time. Every industry goes through peak times of manufacturing, and it is important to know when those times are. Through cutting edge data analytics softwares it is possible to predict these annual manufacturing peaks and normal times. The software can even predict equipment breakdown with the help of AI.
Consumer data
When it comes to consumers, businesses would always want to know their shopping habits, their demographics, lifestyle, interests, income, etc. Imagine the amount of data that will be churned out as a result. With the help of AI and ML, you can figure out useful patterns in consumer behaviour, make accurate predictions and deliver customised shopping experiences.
Machine Learning Vs Artificial Intelligence
As explained earlier, ML is a branch of AI. However, these are two different technologies, but correlated too. And both these systems work with one another to create intelligent systems.
While AI can create intelligent machines and stimulate human behaviour and thinking, ML is an application within it to learn from the data. The term, Artificial Intelligence, is pretty self-explanatory – with human-like thinking capacity, and they work with their own intelligence.
Machine Learning on the other hand, derives knowledge from this data, and makes predictions, and accurate results on what the user types.
Conclusion
The importance of Machine Learning keeps growing by the day. With key insights derived by data predictions, businesses can make accurate business predictions, expand and grow and relate to their customers through intelligent marketing strategies. The intelligence in ML is directed by data, and not human intelligence, and the amount of data you have to train it with is also a factor.
ML is so critical to the success of AI, and main applications include Facebook’s auto friend tagging suggestion, Google search algorithm, online commander system etc. Applications of AI include Chatbots, Siri, intelligent humanoid robot, Online game playing, etc.
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The author: Sascha Thattil works at Software-Developer-India.com which is a part of the YUHIRO Group. YUHIRO is a German-Indian enterprise which provides programmers to IT companies, agencies and IT departments.
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