Technology giants like - Google, Target, Amazon, Netflix, Facebook - they all use it. The 'it' here refers to machine learning, the 21st century's most revolutionary technology that is radically transforming the business dynamics, the way enterprises do businesses worldwide.
Simply put, machine learning is the ability of computer programs to seamlessly analyze big data, obtain information automatically, and to learn from it to deliver precise predictions.
Let's take the example of Amazon, the world's biggest online retail platform with hundreds and thousands of products and over 250 million customers globally.
With machine learning, Amazon can make specific product recommendations to customers almost instantly, based on their previous browsing history and purchasing behavior. This increases the sale, enhances customers' shopping experiences, and improves customer engagements as well.
Target, the second-largest discount store retailer in the United States, brings into play machine learning to foretell the offline buying patterns of shoppers. An interesting case study reveals how Target knew a young shopper was pregnant much before her parents did!
Besides retail domain, Google is implementing machine learning in its autonomous, self-driven car, while IBM's Watson is dramatically revolutionizing the healthcare industry with its cognitive analytics and machine learning algorithms.
In today's digital era, 'Big data' is being created faster than ever before. Manually processing and analyzing this huge data influx is practically impossible. This has, in fact, spurred the rise of machine learning, which utilizes the ability of computer programs to automatically extract information and analyze it accurately.
The sole purpose of applying machine learning is to generate more positive business outcomes through precise prediction capabilities. The outcomes are generally determined by what means most to a particular company, however, the machine learning basics are usually same for every organization. The main intention of most enterprises is to heighten efficiency, performance, and profitability.
Think of Google. Each time you enter keywords, you are supplying invaluable data to Google’s machine learning algorithm. This increasingly helps Google to present more refined search results and relevant rankings that improve the search experience.
There are already millions of machine learning algorithms, and thousands of new algorithms are being developed across the globe as we talk.
Machine learning algorithms have three basic elements, which include -
Representation - This essentially means, how we represent knowledge. Examples of representation include decision trees, graphical models, sets of rules, model ensembles, neural networks, and support vector machines.
Evaluation - It is the way candidate programs are evaluated. Examples of evaluation can be squared error, entropy k-L divergence, prediction and recall, accuracy, posterior probability, likelihood, cost, margin, and many others.
Optimization - Also known as the search process, it is the way candidate programs are generated. Examples of optimization are constrained optimization, convex optimization, and combinatorial optimization.
It must be noted that all machine learning algorithms are a combination of these three elements.
There are four models of machine learning. They are -
1. Inductive learning - This learning is supervised. Training data comprises the desired outputs.
2. Unsupervised learning - Training data does not incorporate the desired outputs. An example of this is clustering.
3. Semi-supervised learning - In semi-supervised learning, the training data includes only a few desired outputs.
4. Reinforcement learning - This is the most ambitious kind of learning from a series of actions.
Out of all four, supervised learning is the most researched, most mature learning, adopted by almost all machine learning algorithms.
Machine learning can be deployed in a variety of applications, such as -
1. Web search - Produces most relevant search results
2. E-commerce - Identifies fraudulent transactions
3. Finance - Evaluates risks, and determines potential areas to invest
4. Space exploration - Radio astronomy and space probes
5. Robotics - Driverless cars
6. Social media - Machine learning extracts value from data on customer preferences and relationships.
Interested in Introducing the Power of Machine Learning to Your Organization?
You might be wrong, in case you are under the impression that your organization doesn't have enough data for machine learning. There are an enormous amount of floating data than you can even think of!
Here are some great recommendations that will help you unleash the tremendous potential of machine learning -
To start with, organize all data that you have collected in the past. This data should comprise information from all the silos of your company, for example, customer information, product inventory, online and offline sales data, accounting, etc.
Next, focus on data generated from data points like social networks or ad clicks. The more data you gather, the better it is, because these bulk, unstructured data will provide you near real-time insights that you would profit from the most.
Finally, formulate a robust strategy with clear objectives to produce the desired outcome. The target outcome can be diverse, in varying fields like predicting customer behavioral patterns, security and fraud protection, or increasing the sales and ROI.
Today, digital technologies and machine learning are transforming every business. According to Gartner, "Smart machines will enter mainstream adoption by 2021." Early adoption can put you ahead of the game, well prepared for the disruption.
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