At NanoBNK we build machine learning algorithms that will help you to apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. While artificial intelligence (AI) is the broad science of mimicking human abilities, machine learning is a specific subset of AI that trains a machine how to learn. Machine learning algorithms in cognitive computing for decision making can help out how to achieve significant solutions by generalizing a learned model from environmental pattern instances. machine learning platforms will no doubt speed up the analysis part, helping businesses detect risks and deliver better service.
About Machine learning
Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. A few widely publicized examples of machine learning applications you may be familiar with: The heavily hyped, self-driving Google car? The essence of machine learning. Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life. Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation. Fraud detection? One of the more obvious, important uses in our world today.
Solutions Machine Learning beings to your organisation
1. Manual data entry
Inaccuracy and duplication of data are major business problems for an organization wanting to automate its processes. Machines learning (ML) algorithms and predictive modelling algorithms can significantly improve the situation. ML programs use the discovered data to improve the process as more calculations are made. Thus machines can learn to perform time-intensive documentation and data entry tasks. Also, knowledge workers can now spend more time on higher-value problem-solving tasks.
2. Detecting Spam
Spam detection is the earliest problem solved by ML. Four years ago, email service providers used pre-existing rule-based techniques to remove spam. But now the spam filters create new rules themselves using ML. Thanks to ‘neural networks’ in its spam filters, Google now boasts of 0.1 percent of spam rate. Brain-like “neural networks” in its spam filters can learn to recognize junk mail and phishing messages by analyzing rules across an enormous collection of computers. In addition to spam detection, social media websites are using ML as a way to identify and filter abuse.
3. Product recommendation
Unsupervised learning enables a product based recommendation system. Given a purchase history for a customer and a large inventory of products, ML models can identify those products in which that customer will be interested and likely to purchase. The algorithm identifies hidden pattern among items and focuses on grouping similar products into clusters. A model of this decision process would allow a program to make recommendations to a customer and motivate product purchases. E-Commerce businesses such as Amazon has this capability. Unsupervised learning along with location detail is used by Facebook to recommend users to connect with others users.
4. Medical Diagnosis
Machine Learning in the medical field will improve patient’s health with minimum costs. Use cases of ML are making near perfect diagnoses, recommend best medicines, predict readmissions and identify high-risk patients. These predictions are based on the dataset of anonymized patient records and symptoms exhibited by a patient. Adoption of ML is happening at a rapid pace despite many hurdles, which can be overcome by practitioners and consultants who know the legal, technical, and medical obstacles.
5. Customer segmentation and Lifetime value prediction
Customer segmentation, churn prediction and customer lifetime value (LTV) prediction are the main challenges faced by any marketer. Businesses have a huge amount of marketing relevant data from various sources such as email campaign, website visitors and lead data. Using data mining and machine learning, an accurate prediction for individual marketing offers and incentives can be achieved. Using ML, savvy marketers can eliminate guesswork involved in data-driven marketing. For example, given the pattern of behavior by a user during a trial period and the past behaviors of all users, identifying chances of conversion to paid version can be predicted. A model of this decision problem would allow a program to trigger customer interventions to persuade the customer to convert early or better engage in the trial.
6. Financial analysis
Due to large volume of data, quantitative nature and accurate historical data, machine learning can be used in financial analysis. Present use cases of ML in finance includes algorithmic trading, portfolio management, fraud detection and loan underwriting. According to Ernst and Young report on ‘The future of underwriting’ – Machine learning will enable continual assessments of data for detection and analysis of anomalies and nuances to improve the precision of models and rules. And machines will replace a large no. of underwriting positions. Future applications of ML in finance include chatbots and conversational interfaces for customer service, security and sentiment analysis.
7. Predictive maintenance
Manufacturing industry can use artificial intelligence (AI) and ML to discover meaningful patterns in factory data. Corrective and preventive maintenance practices are costly and inefficient. Whereas predictive maintenance minimizes the risk of unexpected failures and reduces the amount of unnecessary preventive maintenance activities.
8. Image recognition (Computer Vision)
Computer vision produces numerical or symbolic information from images and high-dimensional data. It involves machine learning, data mining, database knowledge discovery and pattern recognition. Potential business uses of image recognition technology are found in healthcare, automobiles – driverless cars, marketing campaigns, etc. Baidu has developed a prototype of DuLight for visually impaired which incorporates computer vision technology to capture surrounding and narrate the interpretation through an earpiece. Image recognition based marketing campaigns such as Makeup Genius by L’Oreal drive social sharing and user engagement.