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13
Sep

0

How Should You Start To Learn Machine Learning Using Java?

When you talk about the domain of AI (Artificial Intelligence) and ML (Machine Learning), most experts would suggest you learn Python and R programming languages. Java is seldom talked about and yet, you can use it for AI, ML, etc. According to some 2017 studies, it’s the front-end web developers who leverage their familiarity with JavaScript to machine learning. It was found that 16% prioritized Java for the purpose, while 8% were found to avoid the cumbersome C/C++. It was noticed that front-end desktop application developers prioritized Java more than others (21%), which was in line with Java’s frequent use in enterprise-focused applications. The studies found that enterprise developers tend to use Java in all projects, which included machine learning as well. Though Python and R have their own advantages, you can also use Java for machine learning, AI, and other areas of data science if you’re already adept in it.

13
Sep

0

What Is Generalization In Machine Learning?

Before talking about generalization in machine learning, it’s important to first understand what supervised learning is. To answer, supervised learning in the domain of machine learning refers to a way for the model to learn and understand data. With supervised learning, a set of labeled training data is given to a model. Based on this training data, the model learns to make predictions. The more training data is made accessible to the model, the better it becomes at making predictions. When you’re working with training data, you already know the outcome. Thus, the known outcomes and the predictions from the model are compared, and the model’s parameters are altered until the two line up. The aim of the training is to develop the model’s ability to generalize successfully.

13
Sep

0

Why Is Python A Language Of Choice For Data Scientists?

According to various job advertisements for different data science positions, both Python and R belong to the most commonly mentioned and preferred skills. But a lot of studies have revealed that Python programming language is being used more by data scientists. But what exactly makes this language a preferred one for data scientists? In this post, we’ve tried to find out the answer.

13
Sep

0

How We Use Big Data Analytics Tools?

While the concept of big data isn’t new, most businesses have recently realized that if they can capture all the data which streams into their operations, analytics can be applied and significant value can be derived from that. Now, the massive amounts of data only become useful when big data analytics is performed to identify patterns and insights that would be left undiscovered otherwise. As a result, businesses are increasingly looking for professionals who’re familiar with various big data analytics tools to get help in attaining their goals. Here’s an overview of some popular big data analytics tools.

13
Sep

0

What Are Real-life Examples Of The Application Of Big Data Analytics?

These days, as the world is getting more and more connected through different types of digital devices, a massive volume of data is getting emanated from a huge number of digital sources. Businesses and organizations from across the globe are leveraging the power of this data and putting it to their advantages. Big data analytics is performed to identify correlations, hidden patterns, and to derive actionable insights that can help businesses make informed decisions.

13
Sep

0

7 Awesome Difference Between Data Science Vs Data Mining

As organizations and businesses have started to realize that there’s a huge value hiding in the massive amount of data they capture on a regular basis, they’ve been trying to employ different techniques to realize that value. While the ultimate goal is to produce actionable insights from that data, the tech world is getting filled with a significant number of technical terms. And among all these terms, probably the most talked-about terms are data science and data mining. Though some people use them interchangeably, they come with significant differences. Here’re seven most prominent differences between data science and data mining.

13
Sep

0

What Exactly Does Data Science Mean? Is It Really Going To Revolutionize The Industry?

You’ve probably heard the fact that the role of data scientists has been declared as the hottest job of the 21st century. If you’re not much interested in knowing about the latest advancements or buzzing technologies that are taking the tech world by storm, it’s quite normal to wonder why businesses, regardless of their volume and industry, are emphasizing so much to hire these data science professionals. In this post, we’re going to learn what data science actually stands for and how it can benefit the industry.

10
Aug

0

How Do You Build A Data Science Portfolio?

There’re a lot of people trying to step into the field of data science. Unfortunately, many of them often overlook one of the most critical aspects of landing up a good job in the field – the importance of building a strong data science portfolio. While having enough knowledge about different data science techniques and a good number of certifications are surely critical, unless you have a strong data science portfolio, your chances of coming under the radar of recruiters aren’t extremely high. Here, we’ve jotted down the key aspects of building a solid data science portfolio that would make your journey a tad easier.

10
Aug

0

The Most Powerful Idea In Data Science

In the tech fields these days, there’re a huge number of people trying to embark on different types of new paths that eventually lead to having a career in the field of data science. Undeniably, the goal is a worthy one, but it’s also important to have a clear idea about the key goal of data science. In this post, we’ll be trying to explore it. Let’s start the discussion.

10
Aug

0

What Are The Advantages And Disadvantages Of Big Data?

In today’s business landscape, big data has become the most valuable asset for any business. The more a business can harness big data, the better its position becomes from where it can carry out analysis that helps to develop useful business decisions. Across every industry, big data is being heavily used to predict future trends, recognize patterns, and draw new conclusions. However, like every technological advancement, big data also comes with equal shares of advantages and disadvantages. Let’s have a look at them.

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