In this new world of artificial intelligence and data management, it's easy to get confused by some of the terms that are most commonly used in the IT world. For example, data science and machine learning have a lot to do with each other. It's not surprising that many people with only a passing knowledge of these disciplines would have trouble figuring out how they differ from one another. Join nearly 200,000 subscribers who receive actionable tech insights from Techopedia. First of all, data science is really a broad, overarching category of technology that encompasses many different types of projects and creations.
Nvidia has been more than a hardware company for a long time. As its GPUs are broadly used to run machine learning workloads, machine learning has become a key priority for Nvidia. In its GTC event this week, Nvidia made a number of related points, aiming to build on machine learning and extend to data science and analytics. Nvidia wants to "couple software and hardware to deliver the advances in computing power needed to transform data into insights and intelligence." Jensen Huang, Nvidia CEO, emphasized the collaborative aspect between chip architecture, systems, algorithms and applications.
In the marketplace for artificial intelligence technology, giant companies like Google, Amazon, and Microsoft offer a powerful, centralized approach: They sell access to platforms for machine learning that hoover up vast amounts of users' personal and proprietary information and use that data to train AI models. A new development called federated learning offers an alternative to the centralized model. It promises to distribute the power of machine learning to mobile phones, IoT devices, and other equipment on the network edge. The payoff: Better performance and enhanced data security. By distributing AI training to the edge, "you speed up the training process significantly, and you get better accuracy," says Marcin Rojek, co‑founder at byteLAKE, a Poland‑based company working on federated learning solutions using Internet of Things (IoT) devices.
I've been a student of Machine Learning for the past two years, but this past year was when I finally got to apply what I learned and solidify my understanding of it. So I decided to share 7 lessons I learned during my "first" year of Machine Learning and hopefully make this article an annual tradition. Nowadays, it is relatively easy to learn about Machine Learning thanks to the vast selection of learning resources that exist online. Unfortunately, many of them tend to gloss over the data collection and cleaning steps. During my first serious Machine learning project, my team and I run into the BIG question of where do we get our data from?
As a recent graduate of the Flatiron School's Data Science Bootcamp, I've been inundated with advice on how to ace technical interviews. A soft skill that keeps coming to the forefront is the ability to explain complex machine learning algorithms to a non-technical person. This series of posts is me sharing with the world how I would explain all the machine learning topics I come across on a regular basis...to my grandma. Some get a bit in-depth, others less so, but all I believe are useful to a non-Data Scientist. In the upcoming parts of this series, I'll be going over: To summarize, an algorithm is the mathematical life force behind a model.
Daniel D. Gutierrez is a practicing data scientist who's been working with data long before the field came in vogue. As a technology journalist, he enjoys keeping a pulse on this fast-paced industry. Daniel is also an educator having taught data science, machine learning and R classes at the university level. He has authored four computer industry books on database and data science technology, including his most recent title, "Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R." Daniel holds a BS in Mathematics and Computer Science from UCLA.
CarveNiche Technologies on Friday announced a collaboration with tech major Microsoft for a new Artificial Intelligence (AI)-based Math learning programme to supplement classroom learning for school students. Called beGalileo, the programme uses AI to collect and analyse performance information and customise learning to serve each student. Meant for students from class 1 to 10, the programme supports beginners and offers them challenging questions as they advance. "Our product'beGalileo' is a highly personalised Math learning programme for K12 education, and our motto has been to help every child fall in love with Math," Avneet Makkar, CEO, CarveNiche Technologies Pvt Ltd said in a statement. "This association would help us reach a wider network and would be an ideal combination of Microsoft's advanced cloud infrastructure and CarveNiche's rich academic content and technology," Makkar added.
H2O WORLD SAN FRANCISCO – H2O.ai, the open source leader in AI and ML, today announced new and innovative capabilities for its data science and machine learning platforms, H2O, AutoML and H2O Driverless AI, to address the critical scalability and performance needs of all organizations. As part of these new capabilities, and to further the company's mission to democratize AI, H2O.ai has added several new algorithms that address common use cases that customers need today. In addition, H2O Driverless AI is a winner of InfoWorld's 2019 Technology of the Year for the second year in a row. The award honors and recognizes the best in software development, cloud computing, big data analytics, and machine learning tools. This year's judging panel recognized H2O Driverless AI for outpacing all other vendors with "automated simplicity" of its algorithms that do the heavy lifting of feature engineering, model selection, training and optimization – enabling even non-AI experts to uncover hidden patterns using both supervised and unsupervised machine learning.
Although we said'in a weekend' we will give you a week to complete starting this weekend It is also associated with a diverse range of people including Golf (Ben Hogan), Shaolin Monks, Benjamin Franklin etc. This means we don't need any installation (it's completely web-based) We will guide you through two end-to-end machine learning problems that can be taken over one weekend. We will introduce you to important machine learning concepts, such as machine learning workflow, defining the problem statement, pre-processing and understanding our data, building baseline and more sophisticated models, and evaluating models. We will also introduce to keep machine learning libraries in python and demonstrate code that can be used on your own problems. We will cover data exploration in pandas, look at how to evaluate performance in numpy, plot our findings in Matplotlib, and build our models in sci-kit learn.
Here is one of the most viral videos about data science posted in the last few months, with over 500,000 views. I could not locate the original copy; I found it in a re-tweet by Kirk Borne. See link to the video below the picture. However, I was able to find who created it (Welcome.ai) To search these videos by keyword, click here.