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.
Why machine learning is worth knowing about?
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.
Understanding basic ML algorithms
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 secret sauce of machine learning success is ‘data’
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. In fact, most successful ML development project teams spend a major proportion of their time in data cleansing and feature engineering (transforming basic features into advanced features that represent the signals in the data).
Getting programmers on-boarded for ML projects
Now here’s where our machine learning guide gets down to the nitty-gritty. For anybody looking to really understand the theory behind ML algorithms, it makes a lot of sense to invest time in understanding the basics of probability and statistics, apart from linear algebra and calculus. Plus, some basic understanding of algorithms and data structures goes a long way in helping programmers quickly come to terms with the art of coding ML algorithms.
Python is regarded by many as the preferred programming language for implementing ML algorithms without dealing with complex code structures and classes. This, in fact, is the basic stage of machine learning based developments, where programmers and data scientists can begin to think about the intricate mechanics of machine learning.
Building a team geared to drive ML applications home
This is the time for enterprises to take initiative and start investing in building strong machine learning capabilities. To do so, it’s important to build cross-functional teams comprising data science and business domain experience and expertise.
Plus, it’s super important that infrastructure is made available for the team to embark on ambitious projects that can help the enterprise create cutting-edge process automation, and data-driven applications to drive business advantages. It makes sense for enterprises to consider hiring at least one external expert to get the machine learning engine going and use internal promotions to fill the roles of domain experts in the team.
Deep learning is an enabler, and not an alternative
Among the many approaches your development teams can adopt to make machine learning-powered applications, deep learning is one. This approach focuses on designing algorithms that replicate the kind of complex decision making processes operative in the human brain. It’s a bit of a catalyst as far as machine learning is concerned.
Already, deep learning has enabled advanced across many ML application areas. Some of the work traditionally performed using time-intensive feature engineering can now be done easily via deep learning algorithms. Specifically for image and video data processing, deep learning has proven very effective and provided a boost for machine learning applications. However, deep learning itself requires a lot of effort in data transformations, which means it’s definitely not a magic wand that can make ML successful for enterprises.
Machines won’t take over the earth
It’s springtime for conspiracy theorists — there’s so little knowledge about AI and machine learning in the market that all kinds of rumors and scares sound believable. The idea of machine learning and the resulting AI forces deciding to eliminate humans sounds “oh so sci-fi,” and it actually is so! Now’s the time for enterprises to invest resources in ML and drive home the advantage this technology has to offer.
Vital aspects
It’s pretty simple really — if you trust data, you must trust machine learning. The kind of knowledge and wisdom it can bring out of years of your safely kept data can speed track your business growth like nothing else.
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