Machine Learning

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

Types of machine learning:

Just as there are nearly limitless uses of machine learning, there is no shortage of machine learning algorithms. They range from the fairly simple to the highly complex. Here are a few of the most commonly used models.

This class of machine learning algorithm involves identifying a correlation generally between two variables and using that correlation to make predictions about future data points.

1.Decision trees:

These models use observations about certain actions and identify an optimal path for arriving at a desired outcome.

2.K-means clustering:

This model groups a specified number of data points into a specific number of groupings based on like characteristics.

3.Neural networks:

These deep learning models utilize large amounts of training data to identify correlations between many variables to learn to process incoming data in the future.

4.Supervised machine learning algorithms:

Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. The system is able to provide targets for any new input after sufficient training. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly.

5.unsupervised machine learning algorithms:

when the information used to train is neither classified nor labeled. Unsupervised learning studies how systems can infer a function to describe a hidden structure from unlabeled data. The system doesn’t figure out the right output, but it explores the data and can draw inferences from datasets to describe hidden structures from unlabeled data.

6.Semi-supervised machine learning algorithms:

Fall somewhere in between supervised and unsupervised learning, since they use both labeled and unlabeled data for training typically a small amount of labeled data and a large amount of unlabeled data. The systems that use this method are able to considerably improve learning accuracy. Usually, semi-supervised learning is chosen when the acquired labeled data requires skilled. Otherwise, acquiringunlabeled data generally doesnot require additional resources.

7.Reinforcement machine learning algorithms:

It is a learning method that interacts with its environment by producing actions and discovers errors or rewards. Trial and error search and delayed reward are the most relevant characteristics of reinforcement learning. This method allows machines and software agents to automatically determine the ideal behaviour within a specific context in order to maximize its performance.

Future of machine learning:

Machine learning algorithms have been around for decades, they've attained new popularity as artificial intelligence has grown in prominence. Deep learning models in particular power today's most advanced AI applications Machine platforms are among enterprise technology's most competitive realms, with most major vendors, racing to sign customers up for platform services that cover the spectrum of machine learning activities, including data collection, data preparation, model building, training and application deployment. As machine learning continues to increase in importance to business operations and AI becomes ever more practical in enterprise settings, the machine learning platform wars will only intensify.


Its all asking the right question, and that acts as a beginning to machine learning process. After that, we need the right and structured data to answer the question, and this is the part which takes most of the time in a complete machine learning process. Then, the process with a number of iterations starts, until we get a desired predictive model. That model is updated from time to time, to adapt the changes that happen periodically, and finally the model is deployed. In the next article, well focus on some terminologies and look at the machine learning process more closely.