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Power of Recursive Algorithms: A Guide for Data Scientists in Data Structures & Problem Solving
Recursive algorithms are essential tools in computer science, especially for data scientists working with complex problems and data structures....
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Demystifying Data Science
You may have already heard that data science is a fast-moving, exciting field that pays really well and a numerous number of aspiring candidates are trying to step into this field. All the hypes revolving around this field can trigger some common questions like “What is data science?”, “What are the skills required to enter data science?” etc. To help you establish context, here’s our effort toward demystifying data science. The following discussion should help you make an informed decision.
How Do I Use Machine Learning To Gain Profit?
Undoubtedly, you’ve observed the massive buzz going around machine learning since last few years. While a lot of venture investments are being made, conferences are being organized on how to leverage the power of this technology, small businesses too can get benefitted by using machine learning. In this post, we’re going to explore some of the most common ways through which machine learning helps you gain profit.
What Programming Languages Are Suitable For Natural Language Processing?
Probably you already know that natural languages, which are used by humans for communications, are difficult to define with a specific set of rules. By using the combined power of computer science, computational linguistics, and artificial intelligence, NLP or natural language processing helps machines to understand that natural language. The technique uses machine-based algorithms that have the ability to obtain meaning from communication both verbal and written. Natural language processing is widely used in language translation, recognizing human speech, information retrieval etc.
Neural Networks And Deep Learning
In recent years, artificial intelligence and big data have offered a significant number of advantages to businesses together with some new terminologies that every aspiring tech enthusiast should have a clear understanding of. Deep learning and neural networks are two such terms which are often interchangeably used by many people. But in reality, they’re not the same thing. In this post, we’re going to take a closer look at these two to help you develop a proper understanding of them.
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.
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.