Over the past few years, there has been increasing demand for introductory coursework in artificial intelligence and machine learning. Enrollment in Introduction to Machine Learning classes at universities in the US have grown as much as 12 times in the past decade (https://hai.stanford.edu/sites/default/files/ai_index_2019_report.pdf). These introductory courses play a key role in educating the future of machine learning professionals.
Data science is about extracting knowledge from data. Data science is an important area of study because it is a tool that data scientists leverage to gain insights from data and prepare it for the machine learning modeling phase. By “doing data science”, data scientists actually apply techniques, such as data pre-processing and cleaning, feature engineering and descriptive statistics, to their data in order to understand it and start building AI solutions.
In this sense, data science has become an area of study that universities and companies should look at as a first step to start their machine learning journey:
This article is authored by Francesca Lazzeri, PhD (@frlazzeri)
As we are leveraging data for making significant decisions that affect individual lives in domains such as health care, justice, finance, education, marketing, and employment, it is important to ensure the safe, ethical, and responsible use of AI. …
This post was co-authored by Chenhui Hu, Vanja Paunic, Hong Ooi, Tao Wu, Wee Hyong Tok.
Time series forecasting is one of the most important topics in data science. Imagine that you are a business owner, you might want to predict different sorts of future events to make better decisions and optimize your resource allocation. Typical examples of time series forecasting use cases are retail sales forecasting, package shipment delay forecasting, energy demand forecasting, and financial forecasting. As you can see, forecasting is everywhere!
Given its ubiquitous nature and wide-ranging business applications, we have developed an open-source forecasting repo that puts world-class models and forecasting best practices in the hands of data scientists and industry experts — i.e., …
This past semester we have been collaborating on a machine learning Capstone Project with Columbia University’s Master of Science in Applied Analytics: capstone projects are applied and experimental projects where students take what they have learned throughout the course of their graduate program and apply it to examine a specific area of study.
Capstone projects are specifically designed to encourage students to think critically, solve challenging data science problems, and develop analytical skills.
This post was co-authored by JS Tan, Patrick Buehler, Anupam Sharma and Jun Ki Min.
In recent years, we’ve seen extraordinary growth in Computer Vision, with applications in image understanding, search, mapping, semi-autonomous or autonomous vehicles and many more .
The ability for models to understand actions in a video , a task that was unthinkable just a few years ago , is now something that we can achieve with relatively high accuracy and in near real-time.
However, the field is not particularly welcoming for newcomers. Without prior experience or guidance, building an accurate classifier can easily take weeks. Unless you’re ready to spend a long-time learning computer vision, it’s extremely hard to master the basics, let alone begin to explore some of the cutting-edge technologies in the field. …
This article is authored by Matthew Muccio & Torrey Trahanovsky, Microsoft Student Partners, & Francesca Lazzeri, Microsoft ML Scientist.
In this article, we introduce the concept of Generative Adversarial Networks (GANs), by answering the following simple questions:
1. What are GANs?
2. How do GANs work?
3. How can you remember how GANs work?
4. Why are GANs useful?
5. When and where can GANs be used?
6. How can you get started and implement GANs with Python and Azure?
In our previous article on Deep Learning vs. Machine Learning, you learnt how these two concepts compare and how they fit into the broader category of artificial intelligence. The article also describes how deep learning can be applied to real-world scenarios such as fraud detection, voice and facial recognition, sentiment analytics, and time series forecasting. …
This blog post is authored by Francesca Lazzeri (@frlazzeri)
Automated machine learning is based on a breakthrough from Microsoft’s Research Division. The approach combines ideas from collaborative filtering and Bayesian optimization to search an enormous space of possible machine learning pipelines intelligently and efficiently, serving essentially as a recommender system for machine learning pipelines.
On the other hand, when using an algorithm’s outcomes to make high-stakes decisions, it’s important to know which features it did and did not take into account. …
Moreover, you will learn how to launch an automated machine learning process to allow algorithm selection and hyperparameter tuning. Automated machine learning iterates over many combinations of algorithms and hyperparameters until it finds the best model based on your criterion.
Specifically, you will learn the following tasks:
In this article, I assume you have already downloaded the data from Azure Open Datasets and ran through the data preparation steps in this tutorial for the NYC Taxi data so it could be used to build our machine learning model. …
Artificial Intelligence (AI) has become the hottest topic in tech. Executives and business managers, analysts and engineers, developers, and data scientists, all want to leverage the power of AI to gain better insights to their work and better predictions for accomplishing their goals.
While businesses are beginning to fully realize the potential of machine learning (ML), it requires advanced data science skills that are hard to come by. …