Machine Learning

Overview

The Terms Machine Learning and Artificial Intelligence (AI) are closely related and we can say that the abstraction level between these two words is fairly thin line and they can be interchangeably used but when we say Machine learning in the past it was misunderstood thinking that in future it replaces programmers and many such things creating panic, which is not true.

Though we say Machine Learning and AI are closely related, we can say that Machine learning is even more closely related to Datamining which is been used quiet a lot, small example would be a spam folder in your inbox where programs are written to identify the mail received should land in the inbox or spam.

Machine learning enables analysis of massive quantities of data. While it generally delivers faster, more accurate results in order to identify profitable opportunities or dangerous risks.

What is Machine Learning? A definition

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. The basic premise of machine learning is to build algorithms that can receive input data, use it learn for themselves and use statistical analysis to predict an output value within an acceptable range.

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The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

The processes involved in machine learning are similar to that of data mining and predictive modelling. Both require searching through data to look for patterns and adjusting program actions accordingly. Many people are familiar with machine learning from shopping on the internet and being served ads related to their purchase. This happens because recommendation engines use machine learning to personalize online ad delivery in almost real time. Beyond personalized marketing, other common machine learning use cases include fraud detection, spam filtering, network security threat detection, predictive maintenance and building news feeds.

Machine Learning Methods

Machine learning algorithms are often categorized as supervised or unsupervised.

Supervised algorithms require humans to provide both input and desired output, in addition to furnishing feedback about the accuracy of predictions during training. Once training is complete, the algorithm will apply what was learned to new data to provide targets for any new input. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly.

Through methods like classification, regression, prediction and gradient boosting, supervised learning uses patterns to predict the values of the label on additional unlabelled data. Supervised learning is commonly used in applications where historical data predicts likely future events. For example, it can anticipate when credit card transactions are likely to be fraudulent or which insurance customer is likely to file a claim.

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Unsupervised algorithms do not need to be trained with desired outcome data. Instead, they use an iterative approach called deep learning to review data and arrive at conclusions. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabelled data. Unsupervised learning algorithms are used for more complex processing tasks than supervised learning systems.

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Some other machine learning methods

Semi-supervised machine learning algorithms fall somewhere in between supervised and unsupervised learning, since they use both labelled and unlabelled data for training – typically a small amount of labelled data and a large amount of unlabelled data. The systems that use this method are able to considerably improve learning accuracy. Usually, semi-supervised learning is chosen when the acquired labelled data requires skilled and relevant resources in order to train it / learn from it. Otherwise, acquiring unlabelled data generally doesn’t require additional resources.

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Reinforcement machine learning algorithms is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behaviour within a specific context in order to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal.

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Evolution of machine learning

Because of new computing technologies, machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum.

While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development.

Why Machine Learning?

Resurging interest in machine learning is due to the same factors that have made data mining and analysis more popular than ever. Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage.

All of these things mean it’s possible to quickly and automatically produce models that can analyse bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.

To better understand the uses of machine learning, consider some of the instances where machine learning is applied: the self-driving Google car, cyber fraud detection, online recommendation engines—like friend suggestions on Facebook, Netflix showcasing the movies etc—are all examples of applied machine learning.

All these examples echo the vital role machine learning has begun to take in today’s data-rich world. Machines can aid in filtering useful pieces of information that help in major advancements, and we are already seeing how this technology is being implemented in a wide variety of industries.

Some Machine Learning Algorithms and Processes

Some of the common machine learning algorithms and processes are neural networks, decision trees, random forests, associations and sequence discovery, gradient boosting and bagging, support vector machines, self-organizing maps, k-means clustering, Bayesian networks, Gaussian mixture models, and more.

Other tools and processes that pair up with the best algorithms to aid in deriving the most value from big data include:

  • Comprehensive data quality and management
  • GUIs for building models and process flows
  • Interactive data exploration and visualization of model results
  • Comparisons of different machine learning models to quickly identify the best one
  • Automated ensemble model evaluation to identify the best performers
  • Easy model deployment so you can get repeatable, reliable results quickly
  • Integrated end-to-end platform for the automation of the data-to-decision process

Who’s using it?

Most industries working with large amounts of data have recognized the value of machine learning technology. By gleaning insights from this data – often in real time – organizations are able to work more efficiently or gain an advantage over competitors.

Few examples are

Financial Services

Banks and other businesses in the financial industry use machine learning technology for two key purposes: to identify important insights in data, and prevent fraud. The insights can identify investment opportunities, or help investors know when to trade. Data mining can also identify clients with high-risk profiles, or use cybersurveillance to pinpoint warning signs of fraud

Health Care

Machine learning is a fast-growing trend in the health care industry, thanks to the advent of wearable devices and sensors that can use data to assess a patient’s health in real time. The technology can also help medical experts analyse data to identify trends or red flags that may lead to improved diagnoses and treatment.

Marketing and Sales

Websites recommending items you might like based on previous purchases are using machine learning to analyse your buying history – and promote other items you’d be interested in. This ability to capture data, analyse it and use it to personalize a shopping experience (or implement a marketing campaign) is the future of retail.

What is the future of machine learning?

Is machine learning approaching its end? Far from it. What we are seeing now is just the humble beginning.
Here are some trends that is predicted that we will see in machine learning in the future:

  • Our understanding of neural networks will improve greatly. 
Neural networks are arguably the most impressive learning algorithms we have at our disposal at present. Yet, we don’t really understand how or why they work, which is believed that it will change.
  • Natural Language Processing will begin to make sense.
So far, ML-based NLP is still in its infancy. The main problem is that words have different meanings in different contexts. Algorithms that recognize those contexts and understand linguistic concepts on a higher level have not yet been successfully implemented, but there’s no reason why they can’t be.
Collaborative learning will emerge.

Talking about different computational entities collaborating to produce better learning results than they would have achieved on their own. This could be robots or it could be the nodes of an IoT sensor network, or what some would call edge analytics.p

Reinforcement learning will gain widespread industry adoption.

You can achieve awesome things with reinforcement learning. So far, industry ML is mostly concerned with supervised learning, gaining insights from data. The adoption of intelligent agents will revolutionize many industries in the future.

Machine learning pipelines will have increased levels of automation.

There are many data scientists and machine learning engineers out there that have implemented very efficient pipelines to automate and abstract away low-level implementation. However, the current tools are still sort of low level. And the ones that are not have taken away all control of what’s actually happening. We need tools that are high-level, yet allow fine-grained algorithm control when needed.

Machine learning will be embedded everywhere.

So far, machine learning is usually reserved for research, ad hoc analyses or top-level systems. In the future, tons of little devices and software components will be embedded with some sort of artificial intelligence

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