What is Machine Learning (ML)?
Machine learning teaches computers how to perform tasks by learning from data, instead of being told by the program / programmer.
A quick primer on machine learning
Machine learning uses algorithms to “learn” from volumes of data (what is knowing today as Big Data). The more data the algorithm is fed, the more it can learn, and the more accurate it becomes. A good example is Google Maps’ route suggestion and estimated time of arrival.
There are several buzz words that you will hear associated with ML:
- Neural Networks – Also known as artificial neural networks, are a type of machine learning that is based on how neurons work in the human brain. They are computer programs that use multiple layers of nodes (neurons) operating in parallel to learn things, make decisions, and recognize patterns in a similar way a human does.
- Deep Learning – A neural network that includes many layers of neurons a huge volume of data. Confused? Hold on. This is sort of multi layers of neurons + volumes of data. With this power and data, we can solve complex non-linear problems. This is where we see the breakthrough of AI, such as natural language processing, self driving, digital assistants, etc.
- Supervised vs. Unsupervised Learning – Supervised learning algorithms are trained using data that includes correct answers. They build models that map data to answers and then use models for future processing. Unsupervised algorithms learn from data without being pointed to the correct answers. They work on large and diverse datasets to self-learn and improve.
How Will Machine Learning Help My Business?
- Faster Decisions – Automate prioritize and automate decision making. They can flag opportunities and suggest actions that should be taken so that you and achieve better outcomes / results.
- Adaptability – Process real-time inputs so that you can adjust on the fly. Thinking of how a Tesla can veer away from an incoming collision.
- Deeper Insights – Analyze big, complex, and live data. Find actionable insights that a human cannot.
- Efficiency – Decide on the most efficient action, speed up business processes, and decrease lead time. Plan and forecasts accurately, automate manual tasks, reduce redundancy, and eliminate human error.
- Algorithmic Business – Increase automation, change your business model, introduce new products and services, and reduce cost, time, and errors.
- Better Outcomes – Trigger smart actions based on real-time information, address unexpected risks based on your data, predict problems and opportunities, and adjust your status quo, in turn improve business outcomes.
Machine Learning Use Case Examples
- Manufacturing – Manufacturers collect a huge amount of data from plant sensors – which is perfect for machine learning. Computer vision and anomaly detection algorithms are used for quality control – and others are used for predictive maintenance, demand forecasting, and asset utilization reports.
- Finance – Given its high data volumes and historical records, finance is a user of ML. Algorithms are used for trading stocks, approving loans, detecting fraud, assessing risks, and underwriting insurance. They have even created robo-advisors to provide self-service financial advice and portfolio management.
- Healthcare – Machine learning algorithms can process more data and spot more patterns than any team of researchers or doctors, no matter how many hours they put in. Algorithms don’t need to rest. ML is now helping drive a spectrum of possibilities including: medical image analysis, early cancer detection, drug development, robot-assisted surgery, medical interactions, and genome analysis.
