With the ODSC West bootcamp, students will become proficient at creating these types of super programs. Machine learning builds on each of the above superpowers and unleashes the potential for human and computer collaboration in data processing and understanding. Students train in highly sought-after areas of ML with the ODSC West bootcamp, including reinforcement learning, supervised learning, and unsupervised learning.
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The subject itself requires knowledge of mathematical equations, programming, and data workflows. Notable subjects include sentiment analysis, BERT, and transformers. ODSC’s participants will learn key concepts in NLP as well as tools. These things all require natural language processing. 40% of households use voice search every day. Industries like healthcare use NLP to make health record transcriptions easier. Chatbots answer our customer service questions. Natural Language ProcessingĬomputers understanding and speaking human language the way people would is still one of the most sought-after capabilities in AI. These tools provide automation pipelines and workflows to make developing machine learning initiatives faster and leaner. Participants train in Airflow, MLOps, Spark, and Kubeflow, among others.
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ODSC introduces the most popular data science and machine learning tools with hands-on instruction so you know how to use each one. Machine Learning Toolsįrom wrangling to processing to storage to visualization - a firm grasp of tools makes any data scientist a machine learning champion.
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These cover a range of data science specializations. Students will learn common data science frameworks, including PyTorch, TensorFlow, Keras, and scikit-learn. It also reduces the time students spend troubleshooting. These frameworks support projects, and a knowledge of their functions helps make data science projects more efficient. Machine Learning FrameworksĪ framework allows data scientists to engineer data pipelines that can be reused or optimized for other iterations. Tools include SQL, Spark, Pandas, and Tableau.
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Students will master the tools that make data wrangling more efficient and ensure that their results and builds are the highest quality and as free of bias as possible. A majority of data scientists spend nearly half their time on data wrangling alone, according to a 2020 survey from Anaconda.
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Data wranglingīefore any projects happen, data analysts and scientists have to find data and ensure it’s of sufficient quality to yield trustworthy results.
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Julia is a specialized language designed to write fast code for scientific calculations and optimize research execution. Jupyter notebooks are open-source web applications that allow data scientists to create and share documents with projects and documentation in one single document. Some highlights include Jupyter notebooks and Julia. Plus, you’ll also learn some of the tools data scientists use for their projects. At ODSC, you receive training in programming fundamentals in two in-demand data science languages - Python and R programming. You must understand how to write programs and build algorithms before you can take your first machine learning project steps. The foundation of machine l earning lies in programming. And when you attend the ODSC West bootcamp this November 16th-18th, you’ll gain eight superpowers in this innovative field. It turns out machine learning can fill those buckets completely, along with providing desirable job skills. It gives you the chance to build cool things, answer complex questions in a variety of fields, and remain on the bleeding edge of technology.
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It will be live on zoom, and recorded sessions will be made available to participants afterwards. The bootcamp will run daily from 9:00am - 5:00pm (EDT). Day 4 will include hand-on exercises on how to use the AFLOW database online. Scientists will also demonstrate how they performed recently published research, from loading and preprocessing data to analyzing and visualizing results, all in Jupyter notebooks. Hands-on exercises will include practical use of machine learning tools on real materials experimental data (scalar values, spectra, micrographs, etc.) Identifying the most informative experiment to perform next.Quantifying similarities between materials using complex/high dimensional data.Identifying the 'descriptors' that best predict variance in functional properties.Visualizing high dimensional data to facilitate user analysis.Identifying important features in complex/high dimensional data.Four days of lectures and hands-on exercises covering a range of data analysis topics from introduction to python and data pre-processing to advanced machine learning analysis techniques.