It is amusing how just a few years ago everything was about degree and qualification, and now training and certification have changed the way career and professions are dealt with. They don’t have to necessarily go through a four- or five-year process to attain a degree. Machine learning is a rapidly growing field divided into computer science and statistics related to discovering patterns in data.

Through applied machine learning training, technology enthusiasts can be introduced to broad key ideas. This data science training emphasizes on practical work more than it does on theoretical. It also teaches its learners to follow their intuition. We really hope your algebra is good for this training though. There will be a variety of lectures and examples for trainees to learn from.

Through project programming, learners will be able to apply new and modern machine learning techniques to modern world problems. Not just that, how to run evaluations, interpret outcomes and scaling from a thousand data points to billions will all be a part of this data science training, and that is why we insist this is what should be in your 2019 resolution.

Applied Machine Learning and Technological Advancement

Over the past few years, we have observed that there have been many technological advances to make machine learning accessible to technology experts all over the world. The most important part is the emergence of online courses and well-written books making it easier for learners to enhance their skills in applied machine learning.

The research made in this field has been translated into the form of textbooks for learners to be able to digest the framework. In most cases, the advancement has made it possible for technologists the understanding of algorithm and few lines of code by implementing models into the basic application.

Many people term machine learning to be difficult. There is no doubt it is a difficult science. It involves a lot of those technicalities many of us would run from in our school days. It can be difficult to implement machine learning models and algorithms to your new applications. However, those engineers and technologists with an edge over machine learning continue to climb up the ladder in their career at the same time get paid with a really high salary. They have the edge over other technologists in the market.

So let’s just be clear math is not the problem here, in fact, the frameworks in machine learning do not require any intense form of mathematics. The difficult part here just like our lives is decision making. The intuition which we discussed above that the training would teach you, is the most important factor of this data science training, this intuition would guide you which tool should be utilized to solve which problem. This, on the other hand, would require learners to have knowledge of their algorithms and their trade-offs. So how is this skill learned? It is attained through exposure to these models. And these models can be exposed to in the data science training.

More About Machine Learning Training

So yes data science does require from its practitioners to build their knowledge in its specific fields, most commonly, applied machine learning. Another challenge of applied machine learning that one can prepare themselves for through data science training is the debugging problem. As we have been talking about intuition from the very start of this article, debugging in machine learning can have two scenarios. Either your algorithm would work or in the second scenario, it won’t.

So, it can be a bit tricky to find out what actually went wrong and that is the real challenge. For which experts always suggest learners opt for applied machine learning training. The process of machine learning initiates with observations made or data that already exists. The purpose is to search for patterns in the data. Through these patterns, technologists can make a better decision both in the present and future. In training, students of technology are provided with examples to practice better decision making in applied machine learning.

Some really brilliant examples of machine learning exist out there. For examples, Hello Barbie has been programmed through machine learning to not just listen but also respond to a child. This is a classic example of the use of Artificial Intelligence in applied machine learning. How does this type of programming work? Well, there is a microphone in the necklace of the doll that records whatever is said to it and transmits it to a server. Through this server, where an approx of 8000 dialogues exist, an appropriate response is generated. 

Let’s take the example of Coca-Cola a product globally loved. Now the company operates around the world, we cannot even come to imagine the kind of data the company holds. The data is of extreme importance to the company, and at the same time, they also do need to utilize their new data in order to make new advancements. With the help of applied machine learning, they utilized artificial intelligence and augmented reality in various tasks.


From all that we discussed above, we can see the importance of applied machine learning in today’s world, but the trick here is to train for it so we can better equip ourselves and enhance those skills that set us apart in the market.

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