Tutorials

In this talk, we will discuss the practice of collecting and annotating data for your computer vision models and making sure the dataset you are using is representative and free of harmful biases.

Tutorials

In this talk, we'll introduce discrete choice models of agent behaviors with a focus on consumer demand modeling. We will talk about two different ways of modeling consumer heterogeneity (discrete vs. continuous), as well as how this individual-level model (i.e. varying parameters at the individual level) can be estimated by using simulated individuals when you only have aggregate sales data. 

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All the other STEM arts have well-established methodologies. Is there such a set of methodologies for data science and machine learning? YES! Join Thom and Ghaith as they walk you through a high-level coverage of these methods.

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The Online Data Science Dojo platform is carefully designed to cater the needs of aspiring data scientists who are only beginning to learn about developing and deploy predictive models on the cloud applications. In the video, we will walk you through creating an account, uploading data, and importing the data.

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PyTorch Lightning reduces the engineering boilerplate and resources required to implement state-of-the-art AI. Organizing PyTorch code with Lightning enables seamless training on multiple GPUs, TPUs, CPUs as well as the use of difficult to implement best practices such as model sharding, 16-bit precision, and more, without any code changes.

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we'll show you how to build an accurate sales forecast using Prophet and Python. The Prophet package is built by Facebook and makes it ridiculously simple to get up and running with Time Series forecasting.

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We'll review the challenges in data collection and monitoring as well as the tools and solutions that can be leveraged to get up to speed and protect against cyber threats.

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we’ll explore how concepts used to build trustworthy human relationships can be applied to AI systems. We’ll also discuss guidelines and examples for human-AI interaction, including approaches such as communicating clear intent, interpreting models, providing contextual information, mitigating biases, and more.

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We'll walk through an advanced Python tutorial where we will be coding a Linear Regression algorithm from scratch and make it usable in a manner not so different from scikit-learn.

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We'll take a first-principles approach in defining a Real-Time AI Platform. Drawing from various examples across the industry, and looking at emerging architectures of some early pioneers.

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We'll be outlining some of the various real-time database tools and some of their pros and cons as well as discuss some of the methods developers use to implement real-time solutions. 

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Zia Hydari will present his research, investigating gamification, health wearables in combination with gamification, enable interventions that have the potential to increase physical activity-a key determinant of health.

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We'll understand how to use machine learning tools for signal processing. We'll discuss Principal Component Analysis (PCA) in particular: data compression and noise removal. 

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Azure Custom Vision is an image recognition service that lets you build, deploy, and improve your own computer vision models in the cloud

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Overview of various storage systems from Redshift, MySQL, and Hadoop to a list of other data systems used for analytical and operational purposes to take better decisions.

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Discussion on real use-cases of NLP in today's world focusing on three major topics in data science: Language Models, Part of Speech, and Word Embeddings.

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Discussion on the best practices to follow with the goal of documenting experiments to help reproducibility. The tools startups are working on to fix the gaping issues.

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A detailed discussion on what to expect in an interview for a position in the data science job market. Guidelines on how to tackle some unexpected, tricky questions in the interviews.

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Everything about the role and responsibilities of a Data Engineer. From data pipelines and storage systems to managing metrics, models, and dashboards, a Data Engineer does it all.

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Introduction to standard web scraping commands in R using rvest and guidelines to set up a web-scraping script so as to analyze frequently updated data in a periodic manner.