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) …
There are tons of resources to help you learn the different aspects of R, and as a beginner this can be overwhelming. It's also a dynamic language and rapidly changing, so it's important to keep up with the latest tools and technologies. That's why R-bloggers and DataCamp have worked together to bring you a learning path for R. Each section points you to relevant resources and tools to get you started and keep you engaged to continue learning. Just like R, this learning path is a dynamic resource.
Board to help MOV37 find, develop and nurture the new wave of young talent revolutionizing investment management. New York, March 8, 2018 – MOV37, the research and investment platform for Autonomous Learning Investment Strategies (ALIS), has assembled an Advisory Board to help find, develop and nurture the young talent that will revolutionize investment management. "The Advisory Board's primary role is to push us outside our intellectual comfort zones," says Adil Abdulali, Chief Science Officer and President at MOV37. The Board will partner with MOV37 in exploring how technology is fundamentally changing investment management and identifying and supporting the young ALIS managers at the forefront of that disruption. Raphael Douady earned his math PhD in Hamiltonian systems in Paris and holds the Robert Frey Endowed Chair for Quantitative Finance at Stony Brook, New York.
Ah yes, the debate about which programming language, Python or R, is better for data science. In this series, I am considering machine learning and artificial intelligence as included in the term data science. This is almost the data science equivalent of tabs vs spaces for software engineers, at least at the time of this writing. This series is intended to be a somewhat definitive guide on this topic, including recommendations for languages and packages (aka libraries) applicable to different use cases, including data science in production and big data scenarios. This series is not intended to give side-by-side code comparisons, as there are plenty of other articles covering that. From my experience, which language to use is one of, if not the first question that someone interested in learning data science wants answered.
Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ... Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... Want to Become a Data Scientist?
Logistic Regression was used in the biological sciences in early twentieth century. It was then used in many social science applications. Logistic Regression is used when the dependent variable(target) is categorical. Consider a scenario where we need to classify whether an email is spam or not. If we use linear regression for this problem, there is a need for setting up a threshold based on which classification can be done.
WEBINAR DESCRIPTION This 1 hour session will provide an overview on Multi-layer Artificial Neural Networks (ANNs). Artificial Neural Networks (ANNs) are the building blocks of modern Deep Learning applications, such as image processing, speech recognition, text analytics, driverless cars etc. This session will cover the basics of multi-layer ANNs, discuss forward propagation and backpropagation logic, cost function in a multi-layer ANN and how to achieve convergence. This session will also include demonstration of small python programs which implement such Multi-Layer ANNs. WARNING:- This is an advanced Deep Learning topic.
Consider this toy that we found at the thrift store for under $6.00: This toy house delivers numerous musical and other sound effects that are triggered whenever one of the features in the house is pressed, or moved, or rotated: the phone, the dog, the bird, the doorbell, the refrigerator, the stove, the sunrise over the roof, the squirrel climbing the tree, and even the bathroom plumbing. It is a playland of fun, distraction, discovery, and amusement for young children (and occasional older children). We love our data the same way -- full of interesting features to explore and to learn from: patterns, correlations, trends, associations, novel items, new knowledge, etc. Consequently, the era of Big Data, which generates oceans of feature-rich data with big Variety (not only big Volume), can be an exciting time for all of us as we explore those features for new discoveries and business insights.
These days, organizations are creating and storing massive amounts of data, and in theory this data can be used to drive business decisions through application development, particularly with new techniques such as machine learning. Data is arguably the most important asset, and it is also probably the most difficult thing to manage. It can be structured or unstructured, and it is increasingly scattered in different locations – in on-premises infrastructure, in a public cloud, on a mobile device. It is a challenge to move, thanks to the costs in everything from bandwidth to latency to infrastructure. It has a zillion different formats, sometimes chunks of data are missing, and usually it is unorganized and alarmingly often ungoverned.
Data science is a field of study that involves combining domain expertise, programming skills, and knowledge of math and statistics to extract meaningful insights from data. Data science practitioners apply machine learning algorithms to data (including numbers, text, images, video, audio, etc.) to produce artificial intelligence systems. The post Data Science appeared first on DataRobot.
For the past month, we ranked nearly 250 Machine Learning Open Source Projects to pick the Top 10. We compared projects with new or major release during this period. Mybridge AI ranks projects based on a variety of factors to measure its quality for professionals. Open source projects can be useful for programmers. Hope you find an interesting project that inspires you.