Have you wondered what powers the highly personalized recommendations on your mobile device from Amazon? Do you wonder how Uber determines arrival times of your booked app cab? I am sure most of you have wondered how Snapchat uses filters or influences stories at some point in time. Have a look at Introduction to Machine Learning and brush up your skills.
These are everyday examples of machine learning algorithms at work. The common thread is acting quickly on business intelligence for an edge in highly competitive industries.
What is machine learning and why does it matter?
Machine learning is an application of artificial intelligence (AI) which allows computer programs to progressively learn and improvise from its experience with the data. It automates analytics by using algorithms that learn iteratively, to make predictions. Its simple technique of self-learning rather than rule-based programming has found a wide application across multiple scenarios. So this technology has pervaded everyday lives, whether bringing ease of living with navigation recommendations or warning you of market volatility for best investment decisions. Therefore, machine learning matters; as it shapes your ease of living or decision-making. It has integrated so deeply into daily life that you will most likely not notice its application. For instance, the active filtering of your spam messages by Gmail.
How does machine learning bring value to a business?
Machine learning is for both, problem-solving and adding value to a business.
On the marketing front, machine learning analyses historical and real-time data for modifying marketing strategies, instant upsell and cross-sell recommendations, and making predictions of customer behavior. This in turns adds value to marketing and segmentation strategies for personalized recommendations. Machine learning models based on various marketing metrics help predict the prospects of conversions. The unsupervised machine learning is a technique that further identifies buying patterns, by clustering products to make better product recommendations.
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In the financial world, the advantages of machine learning are phenomenal. The most significant use is fraud detection, with its ability to learn from data and spot anomalies and suspicious patterns. Other uses are algorithmic market trading, loan underwriting and regulatory compliance with anti-money laundering laws. In manufacturing and logistics, machine learning helps identify the gaps and weak nodes for devising predictive maintenance. The same ability of learning algorithms to spot patterns can help report security breaches in a database as and when they occur.
The use of machine learning thus spans across industries and applications, enhancing customer experience, and adding business value for higher returns on investments (ROI). Online searches with intuitive results are perfect examples of ML use to cut downtime by making predictions. Algorithms using natural language processing (NLP) are used in AI chatbots, to act as powerful self-learning customer agents. This optimizes resources and builds an additional channel for customer analytics. Upskill yourself with the help of natural language processing projects for beginners.
Real world applications and use cases
Some of the most prolific users are in the banking and financial industry. HDFC Bank has begun rolling out its technology stack with ML and AI. The focus is on enhanced services across the entire spectrum like loan disbursement, transactions, hiring, customer experience and personal banking. Additionally, HDFC has started deploying chatbots for customer engagement. The conversational interface offers a personalized and seamless customer experience.
Major eCommerce platform, Flipkart implements over 60 machine learning models to generate insights – “how a sale is going, which deals are working or not working, at which point customers are dropping off”, and so on.
Nebula, an agro-based company, is leveraging ML to solve problems in Indian agriculture. The testing of agricultural products is done using deep learning and image recognition technologies for speedier and quality results, enabling farmers to get better prices for their products.
In the HR marketplace, Aspiring Minds has an assessment-based job search platform for adding value to merit-based recruitment. Innov4Sight Health and Biomedical Systems is a healthcare start-up that builds intelligent solutions for medical diagnosis using machine learning techniques. SkinCurate powers its diagnostic and therapeutic research for customized treatment in the skin, using state-of-the-art ML techniques.
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Trends and possibilities shaping a machine learning-driven future
Key technological trends powering machine learning – data flywheel, algorithm marketplace, and cloud-hosted intelligence – are expected to shape the future deployment of ML. The advantage of increased user-generated data for flywheel impact will be used by companies for rolling out future products and programs like Tesla is planning for its self-driving cars. Scaled-up machine learning algorithms have created an algorithm marketplace, for reaping benefits of shared algorithmic intelligence. Hosted machine learning platforms are offering pre-trained models as a SaaS delivery, for economies of scale.
Marketing, financial services, and healthcare; are the sectors expected to see prolific and innovative applications of machine learning. It helps structure marketing insight for demand forecasting and predictive recommendations. In banking and finance, ML will be indispensable for the two key challenge areas, fraud detection, and risk management. The field of healthcare will emerge as the most significant application of machine learning innovation, as the results have the power to transform human lives.
The future possibilities of ML are limitless. Imagination, problem-solving and professional expertise in machine learning skills; are expected to drive innovations in business strategies and new product offerings. ML is the future of AI. Just as the future of big data analytics and digital marketing is machine learning.
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