In this era of mounting interest in Machine Learning and Artificial Interest amongst engineers, this is a Techwomaniya initiative to bring people “Back to Basics”.
Recently, there has been an upsurge in the availability of many easy-to-use machine and deep learning packages such as scikit-learn, Weka, Tensorflow, R-caret, etc. A number of courses available online also teaches to use these packages. Still, there is a lack of necessary mathematical intuition and framework to get useful results. The foundation behind ML is a field that touches statistics, probability, computer science, and algorithmic aspects. Which is required to learn iteratively from data and find hidden insights which can be used to build intelligent applications.
Why attend this session?
1. What is Probability and how it is useful in understanding Machine Learning Algorithms
2. Understanding the Random Variables and their underlying distribution used for ML algorithms
3. Picking parameter for a model
4. Practical Application of the above-mentioned concepts
Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results
Check out this video: https://www.youtube.com/watch?v=eIyGuN4VdRM
This series will have 3 parts.
Data Scientist(Independent Consultant)| Founder of TechWomaniya| Ex - IISc Graduate
Winner at Smart Vehicle - IOT workshop in association with IISc Bangalore -TU Clausthal Germany
A representative at UGC-DAAD IOT workshop Germany
Winner at Advanced analytics summit and Inspiring new innovations award at HSBC
to know more about her visit:
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