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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
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Advance Data Visualization
Duration: 1 Day Code: BDT248 Category:This course is designed to help understand best practices for data visualization with no prior experience. You will view examples from real world research cases and business cases.
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Data Science for Leader
Duration: 1 Day Code: BDT247 Category:Data Driven organizations has completive advantage but building an effective Data Science Organization can seem complex and challenging.
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Kickstart PyTorch in a Day
Duration: 1 Day Code: BDT214 Category:TensorFlow has become integral part of Machine and Deep Learning techniques.
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Byte-Sized ML Series: Data Exploration and Analysis
Duration: 90 Minutes Code: BDT175 Category:This brief session will provide exposure to both data exploration and data analysis and their contributions to effective machine learning.
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Byte-Sized ML Basic Series: Machine Learning Model Optimization
Duration: 90 Minutes Code: BDT183 Category:A short session to review techniques for optimizing machine learning model performance.
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Byte-Sized ML Basic Series: Machine Learning Model Deployment
Duration: 90 Minutes Code: BDT182 Category:A short session exploring different ways to deploy machine learning models and some of the tools involved.
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Byte-Sized ML Basic Series: Recommendation Systems
Duration: 90 Minutes Code: BDT181 Category:A short course on understanding what recommendation systems are. Understand the different types of recommendation systems and the concept of collaborative filtering.
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Byte-Sized ML Basic Series: Linear Regression Model
Duration: 90 Minutes Code: BDT179 Category:In this session, we’ll explore the machine learning process and discuss how to train a machine learning model to make predictions and classify data.

