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Since 2015, convolutional neural networks (CNNs) have revolutionized the field of Deep Learning. They form the basis of multiple computer vision algorithms and can be used for various tasks including object detection, segmentation, image classification, and much more.

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More than 70% of corporate AI and analytics projects fail! Even after these corporations have spent very large investments, in time, talent, and technology.  These expenditures were made with anticipation of great returns on investment. 

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The webinar will teach you how to detect silent ML model failure without accessing the target data. We will cover the most likely causes for ML failure, like data and concept drift. 

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Too often, AI and BI are seen as rival teams within an organization - even though they share the same problem: trying to get business value from data. In this webinar, you'll learn how to combine the best of both worlds. 

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Delivering an effective data-driven presentation to a nontechnical live audience isn’t the same as discussing technical details with peers or delivering a written document. You must be purposeful and diligent if you want to develop a presentation that conveys a compelling story while simultaneously avoiding myriad traps that undercut your credibility and limit your impact.

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Telling a story with your data is more important than ever. The best insights and machine learning models will not create impact unless you are able to effectively communicate with your stakeholders.

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Reddit is a social network and has been in existence for 15 years. In contrast to Facebook, Instagram, and Twitter, Reddit is organized in communities (subreddits). Recently, it has gained considerable popularity. Reddit allows downloading data with a liberal license and offers a public API. The data can be easily parsed with Python. This is a big difference from other social networks. The webinar will show how to acquire the data and analyze it with unsupervised methods (topic models). Afterward, we will calculate timelines and dive into trend prediction.

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After training a model, data scientists might want other members of their team including their manager to try out their model. However, their team members might have different operating systems from them and might be not familiar with their programming language. Thus, it is necessary for data scientists to containerize their model and create an intuitive API endpoint for their teammates to interact with. However, data scientists might not have all the time and skills to do all of the things above. Luckily, BentoML allows data scientists to put their models into production with ease. This presentation will show how data practitioners can containerize and share their local machine learning model with their teammates in a couple of lines of Python code.

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In this talk, Jimmy will be telling the story of his journey to, then through Data Science, starting from his finance/accounting days (Symantec, 2013), all the way until the present day (2022, Sr. Data Scientist at LinkedIn). He will touch on highs, lows, moments of triumph and setbacks, and working full-time/doing a Masters part-time for 7 years. He hopes to share with the audience some wisdom that he has gained on this journey and leave them with some words of encouragement (that persistence and a good attitude are some of the best tools you can have in any pursuit of life).

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The process we follow when working with data is just as important as the tools we're using. In this presentation, Ben Jones of Data Literacy will give you an overview of a new tool-agnostic framework that powers his company's recently launched training program, Data Literacy Level 2: Working Effectively with Data, and the accompanying book "Read, Write, Think Data" (coming soon)! By the end of the webinar, you'll be equipped with an understanding of The WISDOM Data-Working Flow, which you can use to pose suitable questions about your data, form hypotheses that can be tested, and convert raw data into the wisdom you need to make sound decisions at work and in life. This new framework was developed by Ben Jones, CEO of Data Literacy, award-winning data visualization designer, best-selling data author, and Professor of Data Visualization at the University of Washington. This new framework is applicable to all experience levels and useful to anyone who interacts with data and must use data to make decisions.

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Recent deep learning architectures have brought us solutions to previously unsolved problems. Nevertheless, for most tools, you still need to overcome the coding barrier. In the first part of this webinar, you will learn about the main concepts behind deep learning: from the artificial neuron to back-propagation. In the second part, you will get a basic introduction to Convolutional Neural Networks and their application in Computer Vision.

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This talk will introduce the foundational business skills you'll need to deliver business value and grow your career as an analyst or data scientist. Drawing on best practices, published research, case studies, and personal anecdotes from two decades of industry experience, David Stephenson will give an overview of foundational skills related to Company, Colleagues, Storytelling, Expectations, Results, and Careers--emphasizing how each topic relates to your unique position as an analytics professional within a larger corporation. 

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One of the most common data science questions is what language beginners should learn, R or Python. This has led to a rivalry between the two languages, termed the "Language War". The purpose of this talk is to announce that this rivalry is over, and we are entering a new era. We'll go through the main defining features of both languages (influenced by their history) and how they compare between different workflows in data science (i.e., data visualization, machine learning) and data types (i.e., text, image, or time series). As a final element, I'll show what methods are available for combining both in the same workspace and demonstrate this with a case study. At the end of the talk, you'll be able to appreciate why being bilingual is essential for a modern data scientist and what are the best ways to get started.

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Skin cancer is a serious problem worldwide but luckily treatment in the early stage can lead to recovery. JavaScript together with a machine learning model can help Medical Doctors increase the accuracy of melanoma detection. During the presentation, Karol will show how to use Tensorflow.js, Keras, and React Native to build a solution that can recognize skin moles and detect if they are melanoma or benign mole. He will also show issues that they have faced during development. In summary, the session includes the pros and cons of JavaScript used for machine learning projects.

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In this session, you’ll learn about the main concepts behind Time Series: preprocessing, alignment, missing value imputation, forecasting, and evaluation. Together we will build a demand prediction application: first with (S)ARIMA models and then with machine learning models. The codeless examples are built in the KNIME Analytics Platform using the Time Series components provided for preprocessing, transforming, aggregating, forecasting, and inspecting time series data. You will also be provided example workflows to use later in your own projects.

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Language-Integrated Query (LINQ) is a feature of the C# programming language that lets you work with data with SQL-like syntax. This presentation starts with the basic syntax of a LINQ query. You'll also learn about core features like filters, joins, and grouping. Additionally, you'll see how LINQ has a comprehensive set of standard operators for whatever you need to do with data. After this presentation, you'll understand how LINQ can help with data manipulation tasks.

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Many people believe that you have to either be a specialist or a generalist. An expert or a jack-or-jill of all trades. But those who can do both, who can balance these seemingly contradictory beliefs, consistently outperform. Matt Coatney, a C-level technology executive, and AI practitioner talk about his own journey and missteps, and how a multi-disciplinary approach including deep technical expertise and core skills led him to personal and professional success.

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This webinar will cover an end-to-end study of attrition – the dwindling number of the workforce – in a company. The end goal of an attrition recommendation system is to develop retention strategies to prevent employee churn. In this session, we will discuss the process starting from data collection & preprocessing to model development and deployment. It will be presented by an award-winning HR consultant with over 17+ years of consulting in HR transformation and system implementation projects across the globe.

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Naive Bayes is a technique from machine learning, useful for making classifications. Naive Bayes has all sorts of applications ranging from facial recognition to weather prediction to medical diagnoses to news classifications among others. In this webinar, we provide an introduction to Naive Bayes methods through theory and coding examples. By the end of the webinar, students should acquire a strong understanding of this technique.