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Upcoming events (4+)
Data wrangling is the art of bringing data together and preparing it for analysis. It is often the most time-consuming aspect of an analytics project. A survey of analysts found that almost half of their time is spent wrangling data. Part of the complication is that analysts have several tools and languages at their disposal, making it difficult to understand which one is the right one for their data-wrangling needs.
Though this crash course focuses on data wrangling with SQL, it will build a foundation for transforming data as you develop your analytics skills. It is intended for beginners with little to no prior experience in SQL. By the end of the session, you will know:
- How to query a dataset
- How to join the data
- How to append data
- How to apply a filter to your datasets
- How to create new data fields
A deployed application can help you showcase your work in several places. Nowadays, along with coding machine learning models, it is also necessary for a person to know how to deploy those in production
In this webinar, we will focus on the deployment of a machine-learning app on Azure. We will also be discussing the step-by-step code required to develop the app. We will explore the capabilities of python packages such as Streamlit, Numpy, Pandas, and scikit-learn. This webinar is designed in such a way that people with no prior experience in web app development can also understand the concepts.
By the end of the session, you will know how to:
1. Develop a machine learning application that you can interact with
2. Deploy the ML app on Azure
3. Redeploy the app with some changes done
Data Leaders must create a compelling narrative to evangelize their Data Management programs and secure long-term support from enterprise stakeholders and business leadership. Data leaders who seek to improve soft skills and execute simple storytelling techniques will be more likely to gain a rightful place for their initiatives on their organization’s strategic plan.
- Why you need a Data Management narrative vs other Data Storytelling and Data Literacy Efforts
- Why Data Management is Macro-Trend agnostic
- Leveraging the 3Vs: Vocabulary, Voice, and Vision
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In December 2015, a published paper rocked the deep learning world. This paper is widely regarded as one of the most influential papers in modern deep learning and has been cited over 110,000 times. The name of this paper was Deep Residual Learning for Image Recognition (aka, the ResNet paper). In this session, we’ll take a brief tour through the history of computer vision, into the anatomy of a convolutional neural network, understand their limitations, and learn how the ResNet paper changed deep learning forever.
By the end of the session, you’ll know:
• What computer vision was like before convolutional neural networks (CNNs)
• The anatomy of CNNs
• The limitations of CNNs
• Residual networks and the skip connection
• How to perform image classification with ResNet with code