What is Machine Learning? – A Basic Introduction

Machine Learning, its types and how it works

Often misunderstood and known by the term artificial intelligence, machine learning is the process of automatically learning from experience without prior programming in the systems. Machine learning focuses on developing computer programs that can get access to data and use it to educate or learn themselves.

The Machine Learning program begins the course of learning with observations or data in regard to the user of the system. The artificial intelligence program makes use of observations such as direct experience and searches for patterns in data to make better decisions in the future based on this data collected. The primary objective of machine learning is to make a computer learn automatically without any form of human intervention or actions. The computers learn on their own and adjust accordingly to the data gathered by them.

Methods of Machine Learning

There are four methods of machine learning implemented by programmers. These are categorized as supervised or unsupervised. The methods include:

Supervised machine learning – According to supervised machine learning algorithms, systems learn and predict future events by using data learned in the past. They analyze data from a known training dataset after which the learning algorithm produces an inferred function to make a prediction about the output values.

Unsupervised machine learning – Unsupervised machine learning algorithms are used when the information is neither classified nor labeled. Unsupervised learning is used to determine how a system determines or describes a hidden structure from unlabeled data.

Semi-supervised machine learning – Semi-supervised learning algorithms are used when a system uses both labeled and unlabeled data for training. Usually, these systems make use of a small amount of labeled data and a large amount of unlabeled data. The systems that make use of such algorithms are able to considerably improve learning accuracy. In most cases, semi-supervised algorithms are used when the acquired label data requires skilled and relevant resources in order to train it.

Reinforcement machine learning – Reinforcement machine learning refers to the method of machine learning which interacts with its environment by producing actions and discovering the errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. The method allows machines to automatically determine the ideal behavior within a specific context.

Machine Learning

Also, Read: Machine Learning is highly demanded job this year

Things That Everyone Should Know About Machine Learning

1. Machine learning refers to the process of a system learning from data and then predicting the output. It has no relation to the term artificial intelligence. Using machine learning a person can solve an incredible number of problems by providing the right algorithms.

2. The term machine learning is mostly about data and algorithms. There are various advances in algorithms which have propelled the study of machine learning far from where it originated. However, algorithms only fulfill a small percentage of requirement which makes machine learning possible. The key ingredient is data. A person can have machine learning without sophisticated algorithms but he cannot have the same without good data.

3. Machine learning trains a system from the patterns in a data. It explores various possible models which are defined by parameters. However, if the parameter is too big, a model will be created which will overfit the training data but will not generalize beyond it. It is natural that a more detailed explanation requires more math, but like other things, the models should be kept as simple as possible.

4. The outputs produced by machine learning are usable outputs only if the data entered is good. The phrase “garbage in, garbage out” is often associated with machine learning as it aptly characterizes a key limitation. Machine learning can discover patterns that are present in the training data. For a supervised format of machine learning, the machine requires a full-fledged, robust collection of correctly labeled data.

5. Machine learning works only when the training data is representative. Machine learning is only guaranteed to work if the data generated is the same as the training data. Programmers are advised to retrain their models frequently so that the training data and the production data don’t become the same.

There is a lot of hype in the market about deep learning. Deep learning earned this hype by delivering advances across a broad range of machine learning application areas. By making use of feature learning, machine learning automates a lot of the work done. However, deep learning is not a silver bullet. There needs to some form of significant investment in the form of effort for data cleansing and transformation.

Also read: Is Machine Learning Going to Take Over Computer Science Jobs?

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