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Friday, 22 Jan @ 5:00 PM


In this third session in machine learning series, we will study the supervised learning algorithms. We will explore the classification problems and the nature of business problems. Various use cases of the same will also be discussed along with pragmatic real-world implementations.The session will focus on logistic regression, decision tree, random forest, boosting etc. The concepts of these algorithms will be discussed, the mathematics behind them will be explored and Python implementation will be examined too. The process to select the best algorithm and to measure the accuracy of the solution will also be discussed. The KPI of confusion matrix, AUC, ROC, AIC/BIC values will be discussed.
Key points of discussion.


– What are classification supervised learning
– Use cases of supervised learning
– Logistic regression
– Decision tree and ensemble learning
– Bias vs variance tradeoff
– Confusion matrix, ROC, AUC, AIC/BIC
– Implementation using Python
– Choose the best model


Vaibhav Verdhan is a seasoned data science professional with rich experience spanning across geographies and retail, telecom, manufacturing, health-care and utilities domain. He is a hands-on technical expert and has led multiple engagements in Machine Learning and Artificial Intelligence. He is a leading industry expert, is a regular speaker at conferences and meet-ups and mentors students and professionals. Currently he resides in Ireland and is working as a Principal Data Scientist. 

Webinar/Workshop organized by: TECHGIG

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