In this era of mounting interest in Machine Learning and Artificial Intelligence amongst engineers, this is a Techwomaniya initiative to bring people “Back to Basics”.
Concepts covered in session 1:
Linear Algebra, Probability, Statistics and Random Variable
Saturday, May 4, 2019
10:00 AM to 1:00 PM
Further Details: Meetup
Do Register: Form
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?
You learn: What is Probability and how it is useful in understanding Machine Learning Algorithms; Understanding the Random Variables and their underlying distribution used for ML algorithms; Picking parameter in a model; 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.