Machine Learning (ML) is a specialized sub-field of Artificial Intelligence (AI) where algorithms can learn and improve themselves by studying high volumes of available data. In this field, traditional programming rules do not operate; very high volumes of data alone can teach the algorithms to create better computing models.
Given the unique attributes of Machine Learning algorithms, they work best with Big Data because the volumes and complexity of such data are so high. In other words, Machine Learning aspires to mimic the human brain – learning by observing.
During the 1930s and 1940s, distinguished AI enthusiasts like Alan Turing began to explore the immense possibilities in “neural networks,” which finally paved the way for the phenomenal growth of Machine Learning as we know it today. You will get an insightful introduction to this fascinating field of Data Science in An Executive’s Guide to Machine Learning. This article warns that if ML algorithms are merely used as quick solutions for finite business problems, then this vastly untapped filed will remain as a bunch of business tools instead of moving forward into currently untapped areas.
With the growing popularity of Artificial Intelligence and AI-enabled smart solutions for businesses, ML is also gaining rapid prominence in the global business world. With the promise of an algorithm economy that is destined to take over the normal business environment in a few years, it is imperative that more AI or ML students, enthusiasts, and practitioners take the time to review the “supplementary literature” available around us to fully understand the scope of Machine Learning applications for the business world. This article takes the readers through a quick review of ML algorithms, use cases, and best practices.
The Business of Machine Learning
Today, the Data Scientists or Machine Learning experts stand between the truckloads of data and the generalist business users by extracting the meaning or intelligence out of the data with proven algorithms. The actual goal of AI practitioners is to bring ML to a point where average business users will not need the help of Data Scientists to use ML algorithms. In an algorithm economy, the pre-made algorithms will be smart enough to study and learn from data to improve the algorithmic models for more effective use later. Some recent examples of such self-teaching ML models may be Facebook’s face recognition tool or IBM Watson’s Oncology tool for predicting cancer cases.
What Does this Mean for Future Businesses?
The Data Science community hopes that one day, ML solutions will aid the shop-floor managers to make quick and accurate decisions on the fly without the intervention of C-Level executives. The top line business leaders will only be called in for exceptional situations. ML has even proven to generate accurate solutions when perfect data did not exist. Now, given the growth of smart algorithms, Business Analytics will move beyond Descriptive and Predictive, and gradually launch Prescriptive Analytics as the sole preoccupation of business leaders.
Machine Learning Algorithms
The power of Machine Learning is hidden in the self-teaching algorithms, which when exposed to huge amounts of data, can study and learn for improved results. In Rules of Machine Learning, the technical experts can discover the modus operandi of ML, but for the most effective way to explore ML is to talk to experts on neural networks or Deep Learning, or unsupervised learning. ML algorithms have been categorized as of the following types of learning models: supervised, unsupervised, and reinforcement. A common ML solution known to everyone is Netflix’s recommender tool that suggests new movies based on a viewer’s past movie selections. If you wish to find out how the three types of algorithms mentioned above differ from each other, then check out the 10 Algorithms that Every Machine Learning Engineer Should Know.
While exploring Machine Learning algorithms, if you ever face a situation where you cannot decide which algorithm is best suited to your problem, then refer to Microsoft’s Choice of Machine Learning Algorithms . Although the answer will depend on a combination of factors like the size and nature of available data, the intended result, and the available time.
Machine Learning Use Cases
This section discusses some common Machine Learning Use Cases. It also helps to skim over the article titled the Top 10 Machine Learning Algorithms, where the use cases mentioned here are explained in details. This article takes each of these algorithms and describes the usage environment with case illustrations. Some of the standard ML Use Cases include Apriori Algorithm, Logistic Regression, Random Forests, K Means Clustering Algorithm, Naïve Bayes Classifier Algorithm, and many more. Also read the Forbes post titled What Are the Top 10 Use Cases for Machine Learning and AI to get a first-hand review of industry applications.
Another post from Forbes, Uses of AI and Machine Learning in Business digs deep into actual AI and ML market applications. The sample cases included here are Expedia that reigns the travel tourism world, by constantly learning from its “BFS” search tool based on ML; Auto Trader that offers useful apps to consumers; or Oacdo that proposes an alternative to barcode scanning.
Machine Learning Best Practices
The article titled Machine Learning Checklist offers the ML enthusiasts a handy checklist that they can use while working with ML projects. The author of this post claims that this checklist helps to “structure the problem” in a manner so that the ML project can “reliably deliver a good solution.” For reference purposes, you can review Machine Learning Quick Ref Best Practices
For all the ML enthusiasts and experts out there, the practical aspects of project execution through utilizing ML are really only just beginning to be understood. The benefits of studying various Machine Learning Use Cases and Best Practices are considerable and will remain important as more enterprises develop and implement Machine Learning throughout the varied Data Management systems.
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