Machine Learning: What it is and why it matters?
A recent news item went as follows: ‘Apple buys machine learning firm Perceptio Inc., a startup, in an attempt to bring advanced image-classifying artificial intelligence to smartphones by reducing data overhead which is typically required of conventional methods’. Another recent development was that MIT researchers were working on object recognition through flexible machine learning. Yet another tech enthusiast, David Auerbach, claims that, ‘Machine learning is starting to reshape how we live and it’s time we understood what it was and why it matters. So, what IS Machine learning and why has it got everybody talking? Read on to learn all you need to know!
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Machine learning is a core sub-area of artificial intelligence as it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, computer programs, are enabled to learn, grow, change, and develop by themselves.
SAS, the North Carolina-based, American developer of analytics software comes with a definition on it: ‘Machine learning is a method of data analysis that automates analytical model building’. In other words, it allows computers to find insightful information without being programmed into where to look for a particular piece of information. This, it does by using algorithms that iteratively learn from data.
While the concept of machine learning has been around for a long time, (one might be reminded of the notable example here – Alan Turing’s famous Enigma Machine) the ability to automatically apply complex mathematical calculations to big data – iteratively and quickly – is gaining momentum only in recent times.
This emphasizes the iterative aspect of machine learning – the ability to independently adapt to new data.
This is made possible as they learn from previous computations and make “pattern recognitions” in order to produce reliable results.
To understand better about the uses of machine learning, we might want to consider some of the instances where machine learning is applied: the self-driving Google car, cyber fraud detection, online recommendation engines - like friend recommendations on Facebook, movie recommendations on Netflix and offers recommendations from Amazon – are all examples of applied machine learning.
All of this echoes the vitality of the role machine learning can play in today’s data-rich world. A recent report from Mckinsey Global has asserted this fact by claiming that machine learning will be the driving factor behind the big wave of innovation in the coming times. Obviously, if machines can aid in filtering useful pieces of information that help in major advancements, and if machines can learn through programmed algorithms, all by themselves, then the technology is bound to find implementation in a wide variety of industries.
The process flow depicted here in a broader sense, is representative of how machine learning takes effect.
Why Machine Learning?
With the constant evolution of the field, there has been a subsequent raise in the uses, demands, and importance of machine learning. The answer to the question as to why one has to adopt machine learning would be: ‘High-value predictions that can guide better decisions and smart actions in real time without human intervention’ (Source: SAS).
Thus, if big data is gaining all the importance for the contributions it does, machine learning as a technology that helps analyze these large chunks of big data, easing the task of data scientists, in an automated process is equally gaining prominence and recognition. Machine learning has also changed the way data extraction, and interpretation is done by involving automatic sets of generic methods that have replaced traditional statistical techniques.
Uses Of Machine Learning
Some instances of machine learning applicability were mentioned previously. To understand the concept of machine learning better, let’s consider some more examples: web search results, real-time ads on web pages and mobile devices, email spam filtering, network intrusion detection, and pattern and image recognition. All these, are by-products of applying machine learning in the analysis of huge volumes of data.
So, how drastically is machine learning revolutionizing the data analysis avenue?
Traditionally, data analysis has always been characterized by trial-and-error, an approach that becomes impossible when data sets are large and heterogeneous. It is for the very same reason, that big data was criticized as being an overhyped technology. Availability of more data is directly proportional to the difficulty of coming up with predictive models that work accurately. Also, traditional statistical solutions are focused on static analysis that is limited to the analysis of samples that are frozen in time. This could obviously result in inaccurate and unreliable conclusions.
Machine learning comes as the solution to all this chaos. It proposes clever alternatives to analyzing huge volumes of data. It is a step forward from all of statistics, computer science and all other emerging applications in the industry. By developing fast and efficient algorithms and data-driven models for real-time processing of data, machine learning is able to produce accurate results and analysis.
Pro-Tip: For more on Big Data and how it's revolutionizing industries globally, head over here for an interesting article on what it is and why you should care!
Some Popular Machine Learning Methods
We have continually iterated the specificity about machine learning’s ability to produce accurate analysis through efficient algorithms. So, how exactly do machines learn?
Two popularly adopted methods of machine learning are – supervised learning and unsupervised learning. It is estimated that while about 70 percent is supervised learning, unsupervised learning accounts to 10 to 20 percent. Other minor methods that are employed are semi-supervised and reinforcement learning.
This kind of a learning is possible at instances when the inputs and the outputs are clearly identified, and algorithms are trained using labeled examples. To understand this better, let’s consider the following example: an equipment could have data points labeled ‘F’ (failed) or ‘R’ (runs).
The learning algorithm under supervised learning would then receive a set of inputs along with the corresponding correct output to find errors. Based on this, it would further modify the model accordingly. This is a form of pattern recognition, as supervised learning happens through methods like classification, regression, prediction and gradient boosting, supervised learning uses patterns to predict the values of the label on additional unlabeled data.
Supervised learning is hence more appropriate and commonly used in applications where historical data predicts future events. Examples will be: prediction of occurrences of fraudulent credit card transactions.
Unlike supervised learning, unsupervised learning is used against data that has no historical data. The algorithm has to explore the surpassed data and must find the structure. This kind of learning works best in transactional data – for instance, it helps in identifying customer segments and clusters with certain attributes who can be treated similarly to marketing campaigns.
Popular techniques where unsupervised learning is employed, includes: self-organizing maps, nearest neighbor mapping, singular value decomposition, and k-means clustering. Basically, online recommendations, identification of data outliers, segment text topics, are all example of unsupervised learning.
As the name suggests, semi-supervised learning is a bit of both – supervised and unsupervised learning and uses both labeled and unlabeled data for training. In a typical scenario it would use small amount of labeled data with large amount of unlabeled data, the reason being that, unlabeled data is less expensive and takes less effort to acquire.
This type of learning can again be used with methods such as classification, regression, and prediction. Examples of semi-supervised learning would be face and voice recognition techniques.
This is a bit similar to the traditional type of data analysis as the algorithm discovers through trial and error and decides which action results in greater rewards. Three major components can be identified in its functioning – the agent, the environment, and the actions. The agent is the learner or decision-maker, the environment includes everything that the agent interacts with, and the actions are what the agent can do.
Reinforcement learning occurs when the agent chooses actions that maximizes the expected reward over a given time. This is best achieved when the agent has a good policy in hand. Learning the best policy, hence remains to be the goal in reinforcement learning.
Data Mining, Machine Learning and Deep Learning – The Differences Explained
Given the fact that machine learning helps in data analysis, it becomes quite unclear for learners new to the field, about the differences between data mining, machine learning and deep learning. Here’s a brief explanation.
To state in simple terms: machine learning and data mining use the same algorithms and techniques as data mining, except that the kind of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge, and further automatically apply that to data, and also to decision making and actions.
Deep learning on the other hand, uses advanced computing power and special types of neural networks and applies them in large amounts of data to learn, understand and identify complicated patterns. Automatic language translation, medical diagnoses are all instances of deep learning.
Some Machine Learning Algorithms And Processes
Although this might not make sense, at the moment, if you have not been introduced to machine learning previously, some common machine learning algorithms and processes to familiarize oneself with, 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 are: 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, an integrated end-to-end platform for the automation of the data-to-decision process.
McKinsey’s report states, ‘As ever more of the analog world gets digitized, our ability to learn from data by developing and testing algorithms will only become more important for what are now seen as traditional businesses.’ It also quotes Google’s chief economist Hal Varian who calls this “computer kaizen” and further adds by saying, “just as mass production changed the way products were assembled and continuous improvement changed how manufacturing was done,” and “so continuous [and often automatic] experimentation will improve the way we optimize business processes in our organizations.”
It could probably be concluded that machine learning is the new avatar of big data analysis. And while Big Data has already fell off the Gartner’s hype cycle, machine learning is somewhere towards the peak, in 2015.
All this only emphasizes the importance of machine learning’s usability in the current data-driven world. Further, it could be predicted that machine learning will evolve over the years but extinction is not a thing that will be associated with it.
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