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What is Machine Learning ? Machine Learning for Beginners

What is Machine Learning ? Machine Learning for Beginners

What is Machine Learning ? Machine Learning for Beginners
Machine Learning refers to the area of Artificial Intelligence that uses statistical techniques to enable computers to interpret and analyze data and ‘learn’ from data patterns. This empowers them to leverage knowledge to program themselves progressively rather than be explicitly programmed by humans reducing the need for human intervention. This endows them with almost human-like learning and improving capabilities. This ultimately leads to an increase in efficiency, accuracy, speed and reduces overall overheads and costs.

Today Machine Learning is at the forefront of artificial intelligence ,signaling the beginning of an era where machines evolve and perform intelligent human-like tasks with ease. While the potential of Artificial Intelligence has inspired countless sci-fi movies and appears fascinating from the surface there are many aspects of AI that need to be understood and taken into consideration by potential students before they jump head-first into a niche AI technology like Machine Learning.

Underlying Principle of Machine Learning
Machine Learning is inspired by the human brain , neuro-science and cognitive science all of which give humans superior learning and decision-making powers compared to other living-beings.  The summary of the Machine Learning principle is that just like the brain observes, understands and decodes data and utilizes it to take intelligent decisions, in the same way computers are programmed to analyze , interpret and utilize data to produce dynamic intelligent programs. Machine Learning is nothing but scalable programming.  This assumes significance because of the huge volumes of data that flow through systems today and the necessity to process this data through pattern recognition and interpretation.

Machine Learning – How it works
The Machine Learning technology is built around machine learning algorithms . Today there are a vast number of machine learning algorithms and this number is steadily rising with the rising applications and with the cross-domain popularity of this technology . Any machine learning algorithm comprises of the following essential components

  1. Representation – Models used to represent knowledge . Eg Decision trees , rule-sets, graphical models
  2. Evaluation – Hypothesis evaluation Eg .Accuracy , prediction and recall
  3. Optimization – Generation of candidate programs Eg . Combinatorial Optimization, convex optimization etc

Machine learning algorithms are trained using a sample dataset and a representation model is created. When new datasets are introduced this model will process the incoming datasets based on the training model already created. The prediction is then evaluated for various characteristics like accuracy and recall. If the result is acceptable then the algorithm is deployed else it goes through the training process in a cyclic manner until the results are sufficiently accurate.

Machine learning algorithms can be largely classified into the following groups

  1. Supervised machine learning algorithms – The data for Supervised machine learning algorithms includes desirable result output in the form of training data . The algorithm is repeatedly trained until the accuracy of the result is acceptable
  2. Unsupervised machine learning algorithms – In unsupervised machine learning algorithms there are no training datasets, expected outputs or a benchmark for measuring the accuracy of the predictions. Eg Clustering
  3. Semi-supervised machine learning algorithms Here the algorithm is provided data that is both labeled and unlabeled hence the training happens in both ways
  4. Reinforcement machine learning algorithms – Here the training of algorithms happens through a system of rewards and punishments wherein the system is rewarded for correct predictions and punished for inaccurate ones.

With the advent of big data that is multi-dimensional in nature, programming for ML algorithms has become complex with several challenges and factors to consider. Machine learning shifts the responsibility for complex programming from humans to machines reducing error margins, inaccuracies and overheads.

Machine Learning has found its relevance and application in different verticals and domains and will continue to grow at an exponential pace. It finds its use in myriad applications like web search engines, social network applications , robotics , e-commerce etc. Some of the largest technical organizations are investing billions in this cutting-edge technology because of its enormous potential.  Training in Machine Learning will  most definitely offer a wide variety of interesting and promising career opportunities to students who are passionate about AI and Machine Learning.

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