The internet is overflowing with resources for learning new programming skills. For thriving disciplines like natural-language processing (NLP), you can find plenty of tutorials, video series, and university lectures online. All of these formats can be great ways to get you started. But when you want to get a truly deep understanding of a new topic, nothing beats a good book. For this article, we’ve compiled a list of our all-time favorite books that you should have in your pack before embarking on your NLP journey.
Robotic Process Automation (RPA) is the latest in futuristic innovation sweeping the tech industry and redefining business operations. Gartner analysts have predicted that RPA will be adopted by up to 50% of the Gulf Cooperation Council in the next two years. And with its growth, comes future-proof career opportunities for you across industries.
RPA is technology that will automate repetitive, monotonous, and error-prone tasks normally done by humans, freeing them up to do more innovative work that is stimulating, involves a higher level of thinking, and more impactful to the business.
In the past few years, more data has been produced than in the millennia of human history before. This data represents a gold mine in terms of commercial value and also important reference material for policy makers. But much of this value will stay untapped — or, worse, be misinterpreted — as long as the tools necessary for processing the staggering amount of information remain unavailable.
In this article, we’ll look at how machine learning can give us insight into patterns in this sea of big data and extract key pieces of information hidden in it.
Over the course of an hour, an unsolicited email skips your inbox and goes straight to spam, a car next to you auto-stops when a pedestrian runs in front of it, and an ad for the product you were thinking about yesterday pops up on your social media feed. What do these events all have in common? It’s artificial intelligence that has guided all these decisions. And the force behind them all is machine-learning algorithms that use data to predict outcomes.
Now, before we look at how machine learning aids data analysis, let’s explore the fundamentals of each.
Machine learning is no longer a sci-fi concept, but an actual application of AI technology we use every day. Machine learning engineers focus on developing computer programs that can access data and use it to learn themselves.
Their daily work involves helping machines learn by creating and fine-tuning training datasets, developing machine learning models, and testing these datasets and models on machines. The goal is for the machine to be able to make informed decisions without the direct instruction of a human.
Bioinformatics sounds like a futurist-type of occupation that could only be found in the not-too-distant future, the discipline is here and growing fast. Bioinformatics is the combination of computer science, data analytics, and biology.
Basically, it is the process of collecting, storing, and processing massive amounts of data using powerful computing programs, but the data that is collected and analyzed is biological data.
Bioinformatics has been used for cutting-edge, scientific studies like DNA sequencing, analyzing biological networks in systems biology, and simulating biomolecular interactions.