A reflection on an ML primer

In recent years, Machine Learning (ML) has grown beyond a simple buzzword. From Google Maps to fraud detection to Netflix, there are countless ways in which ML can be translated into solutions that positively influence and permeate our daily lives.
 
Here at Tensility, we often get questions surrounding ML and whether it’s different from AI. ML, an important branch of AI, can be quite complicated to understand without any background. Machine Learning: A Primer, an article recently written by Lizzie Turner on Medium covering the what, who, when, where, how, and why of ML, condenses the answers in a simple way  - offering valuable insight, tangible examples, and helpful graphics that anyone can understand.
 
ML is composed of algorithms that sift through data in order to reach a conclusion or make a prediction about something. The programmer’s role in ML is not to program the machine to complete a task but rather to teach it how to develop algorithms itself, to learn about the data, and even from its own experience. This process is composed of supervised learning, unsupervised learning, and reinforcement learning. Detailed in Turner’s article, supervised learning deals with labeled data such as sorting spam email, while unsupervised learning deals with unlabeled data often used for big data visualization. Reinforcement learning happens when the machine adapts to ideal behavior in order to maximize performance such as Google’s computer program AlphaGo.
 
How ML works is more complicated. Drawing from mathematics (linear algebra, calculus, and statistics), algorithms types consist of regression, instance-based, decision tree, Bayesian, clustering, deep learning, neural network, and others. Of these, regression algorithms are amongst the most favored due to their fast speeds. Others, like decision tree algorithms, combine weaker learners by branching one to another to form a single stronger algorithm that can make more accurate predictions.
 
We too believe that ML has the power to positively influence people’s lives and the way they work. In areas such as brain and cognition levels, forecasting supply and demand balance and longevity, and broader spaces like healthcare and network security, there is great potential for unique breakthroughs that could change the way in which many of these industries operate today.