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Rapid advancement in artificial intelligence (AI) has drastically changed the way things are moving today. Today, we will speak about this topic by approaching it from three different perspectives.
AI is defined as the science and engineering of developing intelligent machines and intelligent computer programs. Moreso, it is relevant to similar tasks such as utilizing computers to understand the human brain.
With more than 137,000 python libraries available today, choosing the one relevant for your project can be challenging.
Python libraries are critical if you’re looking to start a data science career. However, we will walk you through some of the best libraries that are worth learning this year.
“Deep learning is making a good wave in delivering a solution to difficult problems that have been faced in the field of artificial intelligence (AI) for so many years, as quoted by Yann LeCun, Yoshua Bengio & Geoffrey Hinton.”
For a data scientist to successfully apply deep learning, they must first understand how to apply the mathematics of modeling, choose the right algorithm to fit your model to the data, and come up with the right technique to implement.
Clustering is one of the major techniques used for statistical data analysis.
As the term suggests, “clustering” is defined as the process of gathering similar objects into different groups or distribution of datasets into subsets with a defined distance measure.
Artificial Intelligence (AI) is not a future trend; it is very much a part of our present and is steering our everyday lives. From the posts we see on our social media profiles to the movies we are recommended by Netflix and products Amazon suggests to us, we actively use AI technology.
Further on, with big companies and makers like NVIDIA, Intel, Qualcomm, and others, innovating the underlying technology (semiconductors), AI models are becoming smarter and better. Here we explore a few ways in which AI is changing our world and making it more advanced and simpler.
A data scientist extracts manipulate and generate insights from humongous data. To leverage the power of data science, data scientists apply statistics, programming languages, data visualization, databases, etc.
So, when we observe the required skills for a data scientist in any job description, we understand that data science is mainly associated with Python, SQL, and R. The common skills and knowledge expected from a data scientist in the data science industry includes - Probability, Statistics, Calculus, Algebra, Programming, data visualization, machine learning, deep learning, and cloud computing. Also, they expect non-technical skills like business acumen, communication, and intellectual curiosity.
It’s a data world now. Given the astronomical rise of data being churned out every day, businesses everywhere are interested to glean insights from their extensive data. They are becoming more reliant on data scientists to create business value from data collected.
This has led to an immediate spike in demand for data scientists. A data scientist is a relatively new career trajectory, where organizations hire them at various levels as junior, mid-level, senior, principal data scientist, and director.