When Bill Gates said, “A breakthrough in machine learning would be equivalent to ten Microsofts”, everyone wondered why it was so important. Unlike past trends of machine learning that believed the computer can learn to perform tasks without being programmed, today machine learning is all about how computers are exposed to new data and are able to adopt it. Machine learning is present in almost all the activities that a person does, from virtual personal assistants to filtering malware or video surveillance. This proves machine learning is taking over.
The importance of machine learning, increased when industries started to work with multidimensional data which is constantly increasing. It’s been in demand for various business purposes such as forecasting data, predicting trends of market or analyzing risk. Machine learning brings out the power of new emerging data. It provides methods that can be used to work with heterogeneous data sets which seemed impossible earlier. When the engineer feeds a specific amount of data and the machine implements specific algorithms to learn, desired results get improved. For example, Google assistant, it can set an alarm for you, send others a text message, set an appointment at a dentist for you and do the various tasks just by giving one instruction.
The big question arises, how does it work? It first starts by taking data inputs in the machine to a specific algorithm. This data is known as training data. Now the training data is tested with the specific machine learning algorithm and results are checked. If the prediction is not what was anticipated, the algorithm is reinstructed many times until the desired output is obtained. This is the reason why a machine can continually learn on its own and keep producing best-suited results. It further includes neural network, decision trees, self-organizing maps, etc that are being used on a wide scale.
Being a complex theory in itself, it is further classified into two major subcategories, i.e. supervised learning and unsupervised learning. Most of machine learning is supervised (approx 70%). Supervised means the use of labeled data or known data. Algorithms such as polynomial regression, linear regression, naive Bayes, k-nearest neighbours etc. use this to implement while unsupervised means unknown data that has not been processed earlier.
Algorithms such as apriori, clustering, single value decomposition, etc. use this. The machine learning process is comprised of two broad phases: phase one deals with operations on training data, where it is first pre-processed and then classified into supervised or unsupervised and then any error is analyzed. The second phase deals with inputting more new data and predicting trends and patterns and then working on the output received.
As a conclusion, machine learning has gained popularity because of its subsequent rise in the way of using it. Self-driven Google cars, search recommendations on Facebook, Netflix, etc. all have attracted people to study this diverse topic. It has reshaped data extraction and interpretation done by generic methods. Machine learning courses has successfully replaced traditional statistics that was trial and error based. Anyone who desires to start a career can think about taking a course and applying it in their future because a recent study shows “Future is machines understanding humans”.
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