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Machine Learning Basics

Machine Learning which is in short called ML, is a new evolving field and category of algorithms which makes applications more precise in their functionality and predicting the output expected without being programmed explicitly. It is an Applied artificial Intelligence (AI) field whose algorithms helps or provides the ability to learn, develop as well as improve themselves automatically from the experience, and data it takes as input. Machine Learning is the most exclusive computer science and technology field which focuses on computer program development that can access different forms of data and train themselves or learn by analyzing or experiencing.

The program's self learning process starts with observation, raw data that binds with direct experience or instructions, recognizing of patterns in commands, data or decisions and analyse an alternate solution for the future or even predict from that data. One trending application of Machine learning which is coming in the upcoming mobile phones is: now your smart phone can identify which applications (apps) you use on a daily basis, and which apps you open frequently, it will learn your using pattern and will make loading of such frequently used apps faster as well as sync its associated data in background. This feature will be there in the android operating system. There are a wide variety of Machine Learning applications which are used in the fields of medical science, engineering, learning, research, arts etc. The repetitive characteristic of Machine Learning (ML) becomes significant since different models are getting uncovered to new data, and from these new forms of data, machines (using ML algorithms) can adapt independently. So you can say Machine Learning algorithms have the power to learn from prior computation and UX producing steadfast, repeatable choices and outcome.

Some wide ranges of Machine learning implementations are:

  1. Self-driving cars (developing by Tesla, Google and other large companies)

  2. Online recommendation on different products from Amazon or Netflix

  3. Fraud detection and spam detection features which is common and obvious; ML helps reduce and mitigate such issues.

For creating an excellent machine learning system, you required the following-

  • Well tuned data training capabilities

  • Efficient algorithms to write their functionalities

  • Automation and repetitive processes.

  • Scalability – adopt with the change of size

  • Collective modelling

Some Machine Learning Methods:

Machine learning algorithms and learning techniques have the following types of learning mechanisms. These are –

  1. Supervised machine learning algorithms: that basically deal with those situations when a machine has learned from the past experience and data and using labelled models, patterns and cases predict the future actions.

  2. Unsupervised machine learning algorithms: deals with situations where data needed for training a machine is neither labelled nor classified.

  3. Semi-supervised machine learning algorithms: are a hybrid combination of the above two, as it uses both labelled as well as unlabeled data to train the machine.

  4. Reinforcement machine learning algorithms: are different forms of learning technique which cooperates with its situation and environment by producing situations where it tests through errors or rewards (if the feedback or outcome is good – it adds that learning action as reward, if bad or not suitable – treats the action as error).

Like Machine Learning, Natural Language Processing (NLP), Deep Learning, Neural Network, Robotics are some of the other branches of AI, which will be discussed in upcoming articles.

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