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Generative AI for Data Scientists
Duration: 2 Days Code: BDT294 Category:Creative and innovative thinking is important to find unique solutions to the This course focuses on leveraging ChatGPT to make the whole data science workflow easier and faster.
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Kickstart Using GPT-4, DALL-E, Whisper With Python
Duration: 1 Day Code: BDT281 Category:Building AI models to generate text, images and translating or transcribing audio is a daunting task for any organization.
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Data Wrangling, Modeling, And Model Maintenance In Python
Duration: 3 Days Code: BDT112 Category:This Data Science Quick Start course will expose you to real-world applications of data science.
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Non-Techie Starter Series – AI & Data Science
Duration: Half Day Code: BDT273 Category:This course is designed for non-technical professionals who want to gain a foundational understanding of Artificial Intelligence (AI) and Data Science.
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ChatGPT Basics: Practical Applications
Duration: 1 Day Code: BDT268 Category:ChatGPT has disrupted just about every task that requires the generation of text. It is influencing tasks from research and brainstorming to email composition and creative writing.
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Creative Applications for Artificial Intelligence
Duration: 1 Day Code: BDT261 Category:In advance of each session, Tech Training will provide you with a Zoom link to your class, along with any required class materials.
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AI Toolkit Scikit-learn, Tensor Flow and Keras
Duration: 3 Days Code: BDT260 Category:TensorFlow has become the standard for building machine learning and AI models.
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Deep Learning & Artificial Intelligence with Apache Spark
Duration: 3 Days Code: BDT253 Category:This course teaches doing Deep Learning and Artificial Intelligence at Scale with the popular Apache Spark framework.
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Probability & Statistics
Duration: 1 Day Code: BDT250 Category:Probability and statistics are the backbone for Data Science. The job of a data scientist is to glean knowledge from complex and noisy datasets.
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Advanced Data Handling
Duration: 1 Day Code: BDT249 Category:As it is said, garbage in leads to garbage out. This applies to not only data cleansing but overall, how we handle data using governance, best practices and tools to implement it.i]9