If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The insurance industry is regarded as one of the most competitive and less predictable business spheres. It is instantly related to risk. Therefore, it has always been dependent on statistics. Nowadays, data science has changed this dependence forever. Now, insurance companies have a wider range of information sources for the relevant risk assessment.
It needs a mix of problem solving, structured thinking, coding and various technical skills among others to be truly successful. If you are from a non-technical and non-mathematical background, there's a good chance a lot of your learning happens through books and video courses. Most of these resources don't teach you what the industry is looking for in a data scientist. In this article I have discussed some of the top mistakes amateur data scientists make ( I have made some of them myself too). And we will also look at steps you should take to avoid those pitfalls in your journey. Many beginners fall into the trap of spending too much time on theory, whether it be math related (linear algebra, statistics, etc.) or machine learning related (algorithms, derivations, etc.).
Artificial intelligence (AI) and machine learning (ML) are the most widely chosen domains for reskilling among working tech professionals in India, according to the findings of education technology company Simplilearn. The firm's'Career Impact Survey 2018' which was aimed at analyzing the impact of professional certifications and reskilling among working professionals revealed that AI and ML domains were chosen by 25% of respondents. This was followed by big data and data science domains chosen by 20% of the participants. Other new age categories such as'digital marketing, cloud computing, cybersecurity, DevOps and Agile and Scrum' together saw 55% uptake in reskilling among professionals. The certification courses helped 31% of professionals to enhance their performance, gain manager and peer appreciation, according to the survey.
Big data and data science are set to bring in a digital revolution with groundbreaking technologies like artificial intelligence (AI), machine learning (ML), and deep learning. The essence of data science is to dive into massive datasets to extract meaningful information from them. The insights that data scientists and data analysts obtain from large volumes of data is the secret sauce that's rapidly transforming everything around us. Institutions and organizations across various sectors of the industry are now leveraging data science technologies to power innovation and technology-driven change. In fact, nearly 53 percent of companies have adopted big data analytics in 2017, which is an enormous growth from the 17 percent in 2015.
It has been said that this new wave of exponential technologies will threaten a lot of jobs, both blue and white-collar ones. But if from one hand many roles will disappear, from the other hand in the very short-term we are observing new people coming out from the crowd to lead this revolution and set the pace. These are the people who really understand both the technicalities of the problems as well as have a clear view of the business implications of the new technologies and can easily plan how to embed those new capabilities in enterprise contexts. Hence, I am going to briefly present three of them, i.e., the Chief Data Officer (CDO), the Chief Artificial Intelligence Officer (CAIO) and the Chief Robotics Officer (CRO). Sad to be said, I never heard about a'Chief of Data Science', but for some strange reasons, the role is usually called either'Head of Data Science' or'Chief Analytics Officer' (as if data scientist won't deserve someone at C-level to lead their efforts).
We are going to build a neural network from scratch in Python without the use of a library. The iris data is going to be used to train our model and obtain a high accuracy. We would not be getting into the mathematical background of neural networks, as there are a lot of amazing medium articles covering it (Article 1, Article 2). The iris data is the most commonly used data set for testing machine learning algorithms. The data contains four features -- sepal length, sepal width, petal length, and petal width for the different species (versicolor, virginica and setosa) of the flower, iris.
The data scientist remains one of the most in-demand jobs, and there are a growing number of data science programs across the country to teach techies the necessary skills. But by the time you gain the skills you need, the job and the data science tools may look much different than they do today. Data scientist skills vary depending on the company, but they generally involve the ability to identify business needs and formulate problem statements based on business requirements. They can prepare the right data for analysis to help meet business needs and assist company leaders to help make decisions, according to Adrian Bowles, industry analyst and founder of Storm Insights, in a Dataversity webinar called "The Disappearing Data Scientist." "They can find the right tools, find the right approach to the data, analyze it in a way that makes sense and interpret that," Bowles said in the webinar.
Most of existing Personal Finance applications are boring, because they are all dependent on manual data input, then follwing the right segments of costs, income or balance, for each of them to be placed on right category, sub-category, type etc… just boooring! In addition, you have to consider manual input fails together with the impossibility of live update your financial status, to make it even worst experience. These and many other reasons make the existing Personal Finance applications nearly useless. Lately, in the era of #Fintech revolution, there are indications that many startups are providing or will provide Financial data as a service. Even, EU came with new regulation called PSD2 that will make financial data more accesible in a format of Open data concept.
Video: What programming languages do you need to know to earn more? Arguing about which programming language is the best one is a favorite pastime among software developers. The tricky part, of course, is defining a set of criteria for "best." With software development being redefined to work in a data science and machine learning context, this timeless question is gaining new relevance. Let's look at some options and their pros and cons, with commentary from domain experts.