The first wave of Machine Learning and AI is already here. All of us engage it with it on a daily basis from voice recognition features like Siri, Cortana and Alexa , recommendations on E- commerce like Amazon & Flipkart, tagging options in Facebook to estimated arrival in Uber, most of the time we use it without thinking of the remarkable technology behind it. Ironically this is its defining feature; machine learning has blended in so seamlessly that it touches every sphere of our life without making us hyper conscious. Can you think of anything that a machine cannot do? With the advancing possibilities of Machine learning in AI, there will be very little that can be done exclusively by humans in the future.
Looking Into Machine Learning
Machine learning is defined as the ability of computers to learn by themselves without explicit programming. In other words, machine learning algorithms can self teach from large data sets and evolve. A program built with it can update itself or extend its own code without any human help. Supervised machine learning, Unsupervised machine & Reinforcement learning are the popular learning approaches to machine learning.
In Supervised learning, machine learning algorithms learn from well labelled data that is data sets tagged with correct answers. After that the machine is given new datasets which prompts the machine learning algorithm to analyse the training data and produce a correct outcome. In Unsupervised machine learning, the machine is programmed to identify patterns from complex datasets and predict answers. The aim in Unsupervised learning is to find structure in the data. It works best on transactional kind of data. In Reinforcement learning, ML algorithm through trial and error learns by itself which actions yield greatest reward. In simple terms, the aim is to learn the best policy. It has three components: the agent( the decision maker), the environment (everything the agent interacts with) and the actions( what the agent can accomplish).
As the field of Machine Learning is still evolving, we can only guess at its potential. Prediction is one area that can benefit largely with advancements in ML areas of Deep learning and neural networks.. Predictive Analytics is a field that applies ML to analyse past and predict future events. Some of the fields where ML based predictive analytics can be used are:
- It can be used in E- commerce to predict fraudulent transaction and even the product the customer is likely to choose
- ML can be used in the field of Medicine. It can scan the patient’s medical record and based on the symptoms exhibited by the patient predict the likelihood of a future illness.
- It can used by enterprises in B2B Marketing. ML can scan existing clients and identify prospects with the same attributes. With ML, it will also be possible to prioritize prospects, leads and accounts according to the their possibility to take action
6 Things You Should Know About Machine Learning
Machine Learning focuses on developing intelligent machines that are capable of teaching and correcting themselves without being explicitly programmed. The fundamental goal of machine learning is to develop machines that are able to learn automatically without human assistance or interference.
Machine learning is not monolithic
Machine Learning has different types of techniques and learning models within it. Regression & Neural nets are some examples of machine learning techniques and Supervised, Unsupervised & Reinforced Learning are some examples of learning models. They are used in different combination with each other according to the situation.
Data is at the centre of Machine Learning
Machine learning is made possible by data. With correct training data and learning algorithm it is possible to find solution to an end range of problems. ML is possible even without sophisticated algorithms but not without proper datasets. As it involves finding patterns from training data sets, the data used should be relevant and labelled correctly. There should also be consistency between the training data and the production data, ie the data should be representative.
Machine Learning is all about generalizing
A common mistake of beginners in machine learning is the illusion of success after they test on the training data. It is very easy to do well on the training set . It is also very unlikely that the the exact examples will appear during the the test time even if you have a large data.The primary aim of Machine Learning is to generalize beyond the training set examples.
Machine learning systems are highly vulnerable to operator error
Rarely a machine learning system fail because of faulty machine learning algorithms. In almost all cases it is the when humans make errors in the training data that leads to biases or systematic error. Thus, it is necessary to be alert when it comes to machine learning like software engineering.
Machine learning can possibly create a self-fulfilling prophecy
Biases which are introduced into machine learning models can get embedded and continue creating biased training data. In this way the decision that you make today can have an effect on the training data you collect tomorrow. The biases that are embedded in your machine learning model will continue to generate new training data with that bias. Thus, it is critical to ensure that developers of the machine learning model exercise caution as not to create a self- fulfilling prophecy.
Machine learning will have a major impact on future products
Machine Learning should be seen as a way to enhance the desired outcome. Organisations using machine learning should focus on giving insights that can bring about a desired action. For eg. User interfaces should recommend an action instead of providing multiple choices to make decision making more effective. This way the organisations can make a shift from enabling the customers to manipulate data for examining it to concentrating on recommendation and strategy.
Facebook’s recommended tagging that immediately crops up every time you upload a picture rather than the previous option which asks you whether you want to tag illustrates this point.
Machine Learning is thus a field that can improve efficiency, productivity and help organisations to make critical judgement. Investing in it now can enable organisations to hone their business edge in the future. SpringPeople offers one of the best Machine Learning course. Check it to explore the vast possibilities of this field.