Computers have become adept at extracting patterns from very large collections of data. For example, shopping transactions can reveal consumers' preferences and message traffic on social networks can reveal political trends.
We've compiled a list of the hottest events and conferences from the world of Data Science, Machine Learning and Artificial Intelligence happening in 2018. Below are all the links you need to get yourself to these great events! Please get in touch if there are any great events or conferences you think should be added!
It feels good to be a data geek in 2017. Last year, we asked "Is Big Data Still a Thing?", observing that since Big Data is largely "plumbing", it has been subject to enterprise adoption cycles that are much slower than the hype cycle. As a result, it took several years for Big Data to evolve from cool new technologies to core enterprise systems actually deployed in production. In 2017, we're now well into this deployment phase. The term "Big Data" continues to gradually fade away, but the Big Data space itself is booming.
Joyeeta Das is founder and chief executive of GyanaAI, a self-service data science platform that enables insights around locations and places using big data and artificial intelligence technologies. Her early career began working in engineering, programme management, having risen through the leadership ranks in large international tech companies including Cisco and Wipro. Joyeeta holds two undergraduate degrees, in electronics engineering from the West Bengal University of Technology as well as physics, and an MBA from the Saїd Business School at the University of Oxford, where she received a Fellowship from their Entrepreneurship Centre. "…Artificial intelligence and big data are part of a revolution that has already started … There never has been a more empowered age…" I always enjoyed tech – even as a tiny girl. My mother is a scientist / teacher, my dad was an architect and all my grandparents are into science as well.
In this course you will get a complete understanding of Machine Learning concepts. The industry standard best practices for formulating, applying and maintaining data driven products. It starts off with basic explanation of Machine Learning concepts and how to setup your environment. Next we take up data wrangling and EDA with Pandas. We step into Machine Learning algorithms linear and logistic regression and build real world solutions with them.
AI is seeping into different industries, slowly remolding the global competitive landscape. However, most business leaders still don't know how machine intelligence will impact their businesses. EY recently published a brief, which focuses the current state of AI. We interviewed Nigel Duffy, EY Global Innovation AI leader who co-authored the document with Chris Mazzei, EY Global Innovation Technologies Leader and Global Chief Analytics Officer. The brief frames the current state of AI well: "Most organizations aren't exploiting the potential of AI; they are just at the beginnings of their AI journeys.
Data is everywhere, everyone is running after data, collecting and storing it as if data is a philosopher's stone (Paras Stone) which has the potential to turn businesses into revenue factories. However, it will only be possible if the data is used correctly, on time and with good velocity which is on the rise every day. Today, we have billions of terabytes of data available this is equivalent to more than 2.7 zettabytes of data that exists in today's digital universe, and it is projected to grow to 180 zettabytes in 2025 . To analyze and process data models, machine learning is very important. It involves large dynamic datasets to train itself, test and perform predictive and prescriptive analysis.
This is a "hands-on" business analytics, or data analytics course teaching how to use the popular, no-cost R software to perform dozens of data mining tasks using real data and data mining cases. It teaches critical data analysis, data mining, and predictive analytics skills, including data exploration, data visualization, and data mining skills using one of the most popular business analytics software suites used in industry and government today. The course is structured as a series of dozens of demonstrations of how to perform classification and predictive data mining tasks, including building classification trees, building and training decision trees, using random forests, linear modeling, regression, generalized linear modeling, logistic regression, and many different cluster analysis techniques. The course also trains and instructs on "best practices" for using R software, teaching and demonstrating how to install R software and RStudio, the characteristics of the basic data types and structures in R, as well as how to input data into an R session from the keyboard, from user prompts, or by importing files stored on a computer's hard drive. All software, slides, data, and R scripts that are performed in the dozens of case-based demonstration video lessons are included in the course materials so students can "take them home" and apply them to their own unique data analysis and mining cases.
R is an open source programming language and software environment for statistical computing and graphics. R language is widely used among statisticians and data miners for developing statistical software and data analysis. R is open source and allows integration with other applications and systems. Compared to other data analysis platforms, R has an extensive set of data products. Problems faced with data like optimization and analyzation are cleared with R's excellent data visualization feature.
When we don't experience immediate success -- in any task, not just data science -- we have three options: While option three is the best choice on an individual and community level, it takes the most courage to implement. I can selectively choose ranges when my model delivers a handsome profit, or I can throw it away and pretend I never spent hours working on it. We advance by repeatedly failing and learning rather than by only promoting our success. Moreover, Python code written for a difficult task is not Python code written in vain! This post documents the prediction capabilities of Stocker, the "stock explorer" tool I developed in Python.
The Japanese investment trust market is at a turning point as popular monthly-distribution trusts have begun to see a net outflow of funds while artificial intelligence- and big data-oriented funds are attracting investors expecting high growth. Long-term investment trusts are also in a firm state in line with a growing trend toward asset-building and away from savings in Japan. In 2017, open-type investment trusts, excluding exchange-traded funds, witnessed a new inflow of funds totaling 2.71 trillion yen ($24.34 billion). With their outstanding balance continuing to increase, investment trusts are taking hold as a key asset-building tool for individuals. According to data compiled by QUICK Asset Management Research Center on fund flows for investment trust management companies, individual investors are being lured to trusts that focus on AI and other cutting-edge technologies.