Machine Learning

Machine learning is a variant of artificial intelligence (AI) that makes the systems for self-learning from the data enrolled without being specially programmed. ML aims at the improvement of computer programs that makes the systems to learn for themselves with the accessed data.

The overall learning process initiates with observations from the data accessed such as searches, instructions to find out a particular data. The primary scenario is to allow computers to learn artificially to perform according to our needs. Machine learning is highly related to computational statistics, which aims at prediction-making through the use of computers.

Machine learning allows analysis of big quantities of data with some statistical techniques. Even though it helps to deliver more accurate results to identify business opportunities or dangerous drawbacks, it may also demand extra time and resources to manage it properly. Combining machine learning with Artificial Intelligence and progressive technologies we can make it even more effective in processing large volumes of information.

 

 

Machine learning methods

Machine learning algorithms are usually categorized as supervised or unsupervised.

Supervised machine learning algorithms

In Supervised machine learning algorithms, the computer is trained with some example inputs and their desired outputs, as a learning process and the goal is to learn a particular method that maps inputs to outputs. The system will be able to provide targets for any new input after sufficient training. The learning algorithm makes a comparison of its output with the correct, expected output and finds errors in order to modify the model accordingly.

Unsupervised machine learning algorithms

In this, no classification or label is given to train the systems. Unsupervised learning studies how

systems can feed a function to present 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.

Semi-supervised machine learning algorithms

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 and relevant resources in order to train it / learn from it. Otherwise, acquiring unlabeled data generally doesn’t require additional resources.

Reinforcement machine learning algorithms

Reinforcement machine learning algorithms 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 behavior within a specific context in order to maximize its performance. Simple reward feedback is required for the agent to learn which action is best; this is known as the reinforcement signal.

Machine Learning in predictive analytics

By implementing the right set of tools, the data can be pulled out and analysed from a big data system and can optimize the business.

Nowadays, Machine learning and Artificial Intelligence algorithms implement this for the predictive analysis. Over 60% business leaders are using predictive analytics capabilities for the business growth.

 

Some important areas using ML-based predictive analytics are:

E-commerce – Using ML businesses, it is able to predict customers desire and fraudulent actions. Thereby the business can be optimized to have better growth.

Marketing – In many areas of marketing ML is used. Commonly used for identifying and collecting prospects with attributes similar to existing customers. Thereby the companies can give priority for the most demanded products.

Customer service – Support handling platform like Zendesk uses a machine learning algorithm to process some satisfaction surveys. It analyses the data such as total time to resolve a ticket, response delay, and the specific wording of tickets cross-referenced with customer satisfaction ratings.

Medical Diagnosis – Medical professionals use programs modeled using ML to predict the likeliness of a particular illness. The model will use a database of patient records and will make predictions based on symptoms exhibited by the patient.

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