The machine learning guide you can’t afford to ignore
There’s a reason why Amazon suggests for you the books you end up adding to your favorites. Both these services, along with hundreds of others, use machine learning algorithms to sift through hundreds (and more) of gigabytes of generic and specific data to reveal highly actionable and specific insights. Machine learning is a programming approach that aims at building programs where the algorithm becomes better with time (in a way, learning to perform better). This betterment is driven by the exposure of the algorithm to large amounts of data, called the training set. A machine learning algorithm can be coded using one of the few popular programming languages (Python, and R, primarily). These programs, when in play, are what the marketing world likes calling artificial intelligence. Before your market is swamped by startups that are more agile than you, and backed by the almost super-power of machine learning, it makes sense to learn the ropes of this technological shift. And that’s what we’ll help you do in this machine learning guide.
The answer is pretty simple — machine learning applications have the potential to disrupt industries, take pioneers miles ahead of competitors, and even create new revenue channels. Because cloud-based analytics solutions have become affordable for startups, we already have hundreds of business success stories that have been written using the power of machine learning.
Machine learning algorithms can be categorized into three groups — supervised learning, unsupervised learning, reinforcement learning. Supervised learning algorithms come into play in scenarios where you have attributes and labels available for a dataset and need to predict the values of these labels for other instances. Unsupervised ML algorithms are required in cases where you need to uncover implicit relations in given unlabeled datasets.
In reinforcement learning, there is feedback for each action of step, but no specific error messages or precise labels. It’s strongly advisable that you read more about the most commonly used supervised and unsupervised ML algorithms; that’s bound to enhance your understanding of machine learning on a fundamental basis.
The biggest misconception that’s blinding and misguiding enterprises’ approach toward machine learning is that it needs hefty investments in advanced bid data and data analytics solutions. The only true part of this misconception is that data is crucial for ML success; algorithms, tools, methodologies come next. At its core, machine learning is the art of understanding your enterprise data so well that you can leverage a “trainable” algorithm to create super-smart business applications.
Great data capabilities drive machine learning success, even if the tools used and the algorithms used are basic in nature.
