Intelligense: Machine Learning

Machine Learning

Machine learning is a practice of data analysis that mechanises analytical model building. A learning machine which regularly keeps learning to keep its actions right and insights fresh, is a true machine learning system. The data is fed into the learning machine with every action and non-action feeds and then the task gets automated without constantly requiring human or manual interference. Machine learning has allowed computers to find hidden insights, using algorithms that repeatedly acquire from data that is provided to them, without being programmed explicitly where to look. Machine learning could unveil new strategies and productivities in different systems including IT, healthcare, logistics, energy and even education. Unprecedented levels of efficiency would be reached in the business systems by the self-learning algo’s, and on a personal level the smart gadgets would actually direct us for everything and help ease our lives. Machine learning is a science that’s not at all new but is gaining fresh momentum now.

Machine learning impacting business

A challenge that revolves round the corner is whether these machines can actually deal with the unstructured and structured data and upon the availability of quality algorithms. And if they do, results will be unimaginable. We can just predict the level of changes that would take place, the real transformation would be noteworthy. But all of this definitely demands a considerable amount of time to take place.

Machine learning today is not at all like it was in the past, thanks to the new computing technologies. As models are exposed to new data, the iterative aspect of machine learning is quite important as they are able to adapt independently. They produce repeatable, reliable results and decisions by learning from previous computations provided to them. Machine learning was born from the idea that computers have the ability to learn without being actually programmed for any specific task to work, that's pattern recognition and researchers are devising ways to see if computers could learn from data through artificial intelligence. People have revived interest in machine learning just like Bayesian analysis and data mining for few factors like affordable data storage, more powerful and cheaper computational processing and growing varieties and volumes of available data. All of these things have made it possible to automatically and quickly build models that can analyse more complex and bigger data and deliver more accurate and faster results on a large scale if needed. By creating precise models, the businesses and organisations have a good chance of recognising profitable and successful opportunities and minimising risks thus making machine learning a significant element in core industries.

Machine Learning is the 21st century’s Industrial Revolution

Machine learning facilitates cognitive systems to engage, reason and learn with us in a personalised and natural way. Think of stock trades, Netflix movie recommendations, Internet ads that show up based on our browsing habits — these are all examples of how machine learning is helping us explore the world in powerful and creative ways. Earlier, the turning point in the history of humanity was the industrial revolution which enabled industries to create more jobs by being more productive and thus raising the overall standard of living. Today, machine learning is another such revolution that the world is going to face. We are on the verge of automation and artificial intelligence being the key player and if things are done right, machine learning would help companies grow their businesses and develop insights instantly. Like for the Industrial Revolution, the key component of machine learning is collaboration- we would need a smarter workforce together working for a successful process giving just the right output. The workforce that's being talked here would have data engineers, IT architects, business users, data scientists, data mining experts, system administrators, executives, developers, etc.

We are well aware of the machine learning applications that are plying in our lives today. For a long time, the algorithms of machine learning have been around but what recently developed was the ability to automatically apply complex mathematical calculations to big data, faster and over and over again. One of the examples that we already are familiar with is the self-driving Google car, that was heavily hyped and is based on machine learning. It has all the features of a modern car combined like adaptive cruise control, parking and navigator assistants, speech recognition and lane assistant that makes it close to a completely independent operating vehicle. Also online recommendation offers like those from Netflix and Amazon, fraud detection and combining machine learning with linguistic rule creation to know what customers are saying about you on Twitter, Nanotronics, that automates optical microscopes for improved inspections, Rethink Robotics using it to improve their production speeds and train their robotic arms, increasing customer segmentation accuracy, predicting a customer’s lifetime value, optimizing a user’s in-app experience, detecting customer shopping patterns, assessing health risks, improving personalized care, and diagnosing diseases more accurately are all everyday illustrations of machine learning.

A good machine learning system is created by basic and advanced algorithms, scalability, data preparation capabilities, ensemble modelling and automation and iterative processes. Machine learning is recently a lot in news because of its advances in "deep learning" that includes its much popular AlphaGo’s defeat of Go grandmaster Lee Sedol and other new impressive products around machine translation and image recognition. Machine learning consumes large amounts of data, is more forgiving of changing data points or parameters and supports greater complexity and variability. The generated output with these processes can be applied seamlessly across multiple different platforms, like analytics systems, cloud computing, edge networks and embedded systems. A step change from an era where insights were mainly technology platform driven to a cognitive era, that enables business driven insights. Machine learning, IoT and AI are kind of linked with each other. IoT beautifully complements artificial intelligence when it comes to real-time computing. Mankind would soon get completely replaced with walking machines who would be far more intelligent than we are. The machines have already begun to ply in the businesses for various purposes and in the coming time, we would see a wave of mechanistic transformation transmute our daily lives too. These human dynamos will change our ways of looking towards life by building perception from data they receive and in methods that humans never could. This would mean the machines will actually outdo human force in almost everything resulting in process change, cost savings and bigger and bolder levels of automation. Image and voice recognition systems would recognise individuals across various channels and according to a survey, the fastest growing companies will have more smart machines than the employees.

Models of Machine Learning

Primarily there are three different types of machine learning that are supervised, unsupervised, and reinforcement learning. These are chosen depending on the task to complete and simplicity of the process.


  • In Supervised learning, the learning algorithm is already given the answer while reading the data that is the correct outcome for each data point is explicitly labelled when training the model. It intends to find the relationship rather than finding the answer so that it may correctly predict or classify the data points when the unassigned data points are initiated. 
  • In Unsupervised learning, the learning algorithm is not given the answer during training and the value lies in locating correlations and patterns. It aims to find meaningful relationships between the data points. 
  • The last type is the Reinforcement learning which is a blend between supervised and unsupervised learning. It requires linkage to the environment and is used to solve more complex problems than the former two. Few famous examples are backgammon, poker, and Go, that are the logic games and data is provided by the environment to let the agent respond and learn by itself.

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