The Introduction

By:Ashraf Jaffrey May 23/2019

We are living in the era of technological transformation that is bringing about changes in the way we take decisions. As big data is becoming pervasive across all the industries, use of machines to find patterns and predict future is gaining a lot of prominence in the market. Machine learning is a method of data analysis which automates the process of model building. The algorithms use computational techniques to generate insights that help organizations make better decisions. Machine learning is used in various fields in real-time business situations. Here are a few widely used examples of machine learning applications you must be familiar with:

• • Online recommendations and the advertisements you see on websites are all backed by machine learning algorithms.
• • Self-driving cars run on machine learning algorithms.
• • Natural language processing and image recognition software use these algorithms.
• • In the financial world, stock trading and fraud detection are carried out by using machine learning algorithms.

How Machine Learning Works?

Machine learning uses three types of techniques. These techniques train a model and predict outputs based on the following:
Supervised Machine Learning:
It consists of input variables (x) and an output variable (Y). A supervised algorithm then uses the training data to map inputs to the desired output. It is called supervised learning because it requires human interference in making predictions on the training data. The algorithm makes several iterations to get the acceptable level of output. Supervised machine learning is used in classification and regression problems. The technique is commonly used in the following applications:

• • Linear/ Logistic regression
• • Random forest
• • Support vector machines (SVM)
• • Discriminant Analysis
• • Naive Bayes
• • k-Nearest Neighbors

Unsupervised Machine Learning:
In unsupervised machine learning there is no outcome variable. The algorithm models the data using input variables and presents the structure based on the same. Unsupervised machine learning is used in forming classification (clusters) and associations in data. The technique is commonly used in the following:

• • K-means and Hierarchical clustering
• • Neural Networks
• • Gaussian Mixture
• • Apriori algorithm for association rule mining.

Reinforcement Learning:
In this technique, the machine produces programs, called agents, through a process of learning and evolving. The agent learns from past consequences of its actions and selects the best possible solution through trial and error learning. This technique is applied inHidden Markov models.

When to Get Started with Machine Learning?

Machine learning helps in solving business problems which involves a large amount of data. In order to use machine learning, organizations need to have scalable data preparation capabilities. The machine learning algorithms quickly produce models that can analyze complex data and deliver faster and accurate insights. With these models, datascientists and analysts can surely identify profitable opportunities and mitigate potential risks. However, each and every organizations who have invested in Data and Analytics should first need to choose the right technique and algorithm to make the best use of machine learning.

Machine Learning by Industries

Machine learning is proving its worth in many industries globally. It significantly drives efficiency; deliver customer value and helps in gaining actionable insights. Some of the key sectors which has inducted machine learning in their operations includes:

Financial Services:
The financial services industry was one of the first sectors to implement Artificial intelligence (AI) in business decision-making. Fraud detection, face recognition, compliance is carried out meticulously through machine learning with a large amount of structured and unstructured data.

Healthcare:
Machine learning offers an array of benefits to patients and healthcare service providers. Machine learning is extremely crucial in clinical trials as it helps to know if the treatment would be safe and effective. It is used in establishing a correlation between patient’s behaviour and the disease. Use of biometric sensors is saving lives of millions of patients globally.

Retail:
Machine learning helps retailers to increase sales and empowers customer engagement through predictive analytics such as market basket analysis, item recommendations, analysing buyer sentiment, ad scoring, and identifying new markets, among others.

Conclusion

Machine learning heralds significant potential for the growth of humans and the economy. AI and ML model human procedures, and while humans with experience can start to make predictions, those predictions will still have accuracy issues. With people, usually the more experience and the more they can look at contributing factors, their predictions become more accurate. That accuracy, however, likely won’t reach into the 90 percent range even in the best scenarios. Where Machine Learning can eventually perform better is through the ability to take on a much larger history and set of contributing factors. In the end, the adjustments can be partially automated, but they often still require some human input. Almost every business industry can benefit from Machine Learning. ML-powered systems can provide customer service, forecasting, fraud detection, and even recruitment.

About the Author

Ashraf Jaffrey –
Senior Consultant- Big Data, Advance Analytics & Artificial Intelligence

Having more than 20 years of experience in Big Data, Advanced Analytics, Artificial Intelligence and Machine Learning, Ashraf is currently part of the Senior team focusing on helping clients in Advance Analytics and Artificial Intelligence using Machine Learning. He supports the practice in managing AI-based engagements and delivering end to end Big Data and AI based solutions. He also supports development of in-house solution. His main areas of interest are solutions that enable organisations become more nimble and agile in this competitive world.