Introduction to Machine Learning
Machine learning is a branch of computer science that allows the computers to automatically learn and improve from its previous experience without any requirement of being explicitly programmed. It focuses on computer programs development capable of accessing data and using it for learning purposes.
The learning process begins with the assessment of data, looking for patterns within data in order to make better decisions in future. The main target behind machine learning is to let the computers automatically learn without any need of human assistance and modify its actions in accordance.
Multiple machine learning methods have been enlisted. Two of the most widely adopted methods for machine learning are supervised and unsupervised learning. Around 70 per cent of machine learning is carried out through the supervised learning method whereas, the unsupervised learning method accounts for 10 to 20 per cent of machine learning. Two other methods that are sometimes used are semi-supervised and reinforcement learning methods.
Supervised algorithm for machine learning- It can be applied to assessments ranging from previous learning to new data with the usage of labeled examples for the prediction of future events. With the carry out of analysis of a known training dataset, the learning algorithm creates an inferred function in order to make predictions for the input values. The system provides targets for some new input after proper training. The learning algorithm also compares its output with the intended output and finds error to modify the model in accordance.
Unsupervised algorithm for machine learning- This is used in case of used information for training being neither labeled nor classified. It studies how the system can infer any function for describing any hidden structure from a set of unlabeled data. The machine learning system does not identify the correct output produced; instead it makes the assessment of the data and then draws inferences from the available datasets in order to describe the hidden information from the unlabeled data.
Semi-supervised algorithm for machine learning- It lies somewhere in-between the supervised and unsupervised algorithm learning, owing to the fact that they make use of labeled as well as unlabeled data for the information extraction, learning and training process- especially in case of a small set of labeled data and large set of unlabeled data. The systems using this method are capable of improving their learning accuracy considerably. Generally, the semi-supervised machine learning method is selected in case the acquired labeled data demands some relevant skilled resources for the purpose of training or learning from it. Otherwise, unlabeled data acquisition doesn’t demand additional resources generally.
Reinforcement algorithm for machine learning- It can be defined as a learning method, which interacts with its surroundings with actions in order to discover rewards and errors. The method of trial and error search along with the delayed reward method is some of the most relevant characteristics defining reinforcement learning. The method lets the machines as well as the software agents to determine the ideal behavior automatically within some specific context for performance maximization. In order to let the agent recognize the best action, simple reward feedback is required. This process is known as reinforcement signal.
The machine learning methods allow the analysis of massive data quantity. Generally, it delivers quicker and more accurate results to allow the identification of profitable opportunities and potential risks for which it may demand additional resources and time for training it properly. The combination of machine with artificial intelligence may prove to be even more effective for the processing of large information volumes.
The relevant machine learning benefits considered important are:
Simplification of product marketing followed by assistance in accurate forecasts for sales- machine learning allows the businesses for promotion of their products in a better way along side making accurate forecasts for product and services sales in multiple ways. It offers various advantages to the marketing and sales sector such as consumption of unlimited amount of data from unlimited sources and using for outlining the strategies based on consumer behavior patterns, quick analysis, prediction & processing along with interpretation of past consumer behaviors to make better predictions of consumer behaviors.
Accurate diagnosis and prediction medically- In the healthcare industry, the algorithms of machine learning offer convenience in the identification of patients who are at high risk, correct diagnosis, recommendation of best available medicines and prediction of required readmissions (if required). All these are done of the basis of available patient record datasets and the exhibited symptoms.
Improvement in precision for financial models and rules- The machine learning algorithms place significant impact on the financial sector. The major benefits of machine learning in the finance sector are algorithmic trading, portfolio management, loan underwriting and above all fraud detection. Additionally, machine learning processes also facilitate continual assessment of data for the purpose of detection and analysis of anomalies and nuances targeted at the improvement in precision of financial rules and models.
Easier spam detection- One of the earliest problems solved by machine learning is spam detection. The advent of machine learning, the spam filters have made new rules for the elimination of spam mails and other content with the evaluation of rules across a large network of computers.
Increased efficiency of the manufacturing industry through predictive maintenance- Manufacturing organizations carry out corrective as well as preventive maintenance practices which are costly and inefficient. Machine learning allows the creation of predictive maintenance plans, which minimize the chance of unexpected failures in turn reducing unnecessary maintenance activities.
Customer segmentation and accurate customer lifetime value prediction- Customer segmentation and their lifetime value prediction are the major challenges faced by the businesses today. Machine learning allows the enterprises to for the accurate prediction of individual marketing and incentives. It also facilitates the elimination of the guessing process, which is often associated with data-driven marketing.
Right product recommendation- Machine learning models analyze a customer’s purchase history and based of that identification they accurately identify the products the customer may be interested in. The algorithm identifies hidden patterns and groups similar products into clusters. This process in classified as unsupervised learning, defined earlier
All the applications of machine learning described above machine it a high value application that produces digital innovation trend. Also, machine learning allows the businesses to discover new trends effortlessly from large data sets. The enterprises can enjoy automated analysis for the interpretation of business interactions, done by humans traditionally. This empowers the businesses to deliver new, differentiated products and set of services. Therefore, machine learning is often being considered as a lucrative business decision. However, certain risks need to be thought upon before stepping forward. Thus, investment decision must be taken with utmost care.
