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DTSTART;TZID=America/New_York:20220311T020000
DTEND;TZID=America/New_York:20220311T170000
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SUMMARY:Machine Learning with Python (Webinar)
DESCRIPTION:Hands-on Training – Machine Learning with Python and Jupyter \n\n\n\n\nEVENT RECORDING\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\nIn the last few years\, both industry and academia witnessed the rise of Machine Learning (ML) methods being applied in finance\, marketing\, retails\, science\, engineering\, healthcare\, and humanities. Learning how to apply ML methods to a domain-specific application does not require detailed knowledge about the inner machinery of these methods; however\, one needs to learn the best practices and recommendations followed by the community. \n\n\n\nIn this workshop\, after a brief overview of machine learning\, we will focus on doing the hands-on training in applying ML models on various data types including image\, text\, and time series. We will work through the use cases of classification and regression problems and discuss where to apply supervised or unsupervised methods. \n\n\n\nObjective of the workshop \n\n\n\nUnderstand supervised and unsupervised methodsChoose correct metrics and sampling methods for classification vs regression problemsFind out which features are important in a given datasetLearn to apply ML models such as Decision Trees\, Random Forest\, and Support Vector MachinesPerform clustering and dimensionality reductions (PCA\, t-SNE\, K-means\, etc.)Search the parameter space – hyperparameter optimization\n\n\n\nWhat is needed? Laptop/ Desktop with Internet Connection \n\n\n\nDuration: 3 hours \n\n\n\nLevel: Intermediate \n\n\n\nProgramming Platform: On-line resource or Laptop. Instructions for on-line resources will be given in the workshop. \n\n\n\nPrerequisite: Basic laptop usage. Basic knowledge of Python is helpful for doing the hands-on session. \n\n\n\nSlides and materials: Will be provided in the workshop \n\n\n\nRegistration: Required (Webinar link will be sent after the registration in the confirmation email along with the order details) \n\n\n\nThis is a virtual event\, and it is free. Here is the registration link: https://www.eventbrite.com/e/machine-learning-with-python-webinar-tickets-289926908187
URL:https://ern.ci/event/workshop-on-machine-learning-with-python-webinar/
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DTSTART;TZID=America/New_York:20220325T020000
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CREATED:20220303T191953Z
LAST-MODIFIED:20220330T200532Z
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SUMMARY:Deep Learning with Python (Webinar)
DESCRIPTION:Hands-on Training – Deep Learning with Python and Jupyter \n\n\n\n\nEVENT RECORDING\n\n\n\n\n\n\n\n\nDeep Learning (DL) outperforms Machine Learning (ML) in many of the applications related to Computer Vision (CV) and Natural Language Processing (NLP). One of the biggest advantages of DL over ML is that they can automatically extract the important features from the data. With sufficient data and compute power\, DL methods\, in particular the supervised learning methods\, can achieve the prediction accuracies that were not seen before with any other statistical methods in applications related to CV and NLP. \n\n\n\nIn this workshop\, we will go through the basics of artificial neural networks (ANN)\, Convolutional Neural Networks (CNN)\, and Recurrent Neural Networks (RNN)\, and do hands-on training with these DL models to build predictive analytics for image and text data. \n\n\n\nObjective of the workshop \n\n\n\nUnderstand the basics of Artificial Neural Networks (ANN)Prepare image and text data suitable for the neural networksLearn how to apply various DL models such as ANN\, CNN\, and RNNImprove the accuracy of the model with Hyperparameter Optimization\n\n\n\nWhat is needed? Laptop/Desktop with Internet connection \n\n\n\nDuration: 3 hours \n\n\n\nLevel: Intermediate \n\n\n\nProgramming Platform: On-line resource or Laptop. Instructions for on-line resources will be given in the workshop. \n\n\n\nPrerequisite: Basic laptop usage. Basic knowledge of Python is helpful for doing the hands-on session. \n\n\n\nSlides and materials: Will be provided in the workshop
URL:https://ern.ci/event/deep-learning-with-python-webinar/
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