55c785f1580c3f055253708d786721d3

Abstract

-Introduction
-Python basic of machine learning libraries
-What is Machine Learning? Differences between Machine learning/Deep Learning/Neural Networks/AI
-Types of ML: Supervised, unsupervised
-What are the features? Getting an idea for Feature Extraction.
-Linear regression
cost function
gradient descent
-Locally Weighted regression
-logistic regression
-Softmax regression
-Naïve Bayes hands-on Digit Recognizing with MNIS
-classification
images(Dog and Cat)
-Neural Network, Types of NN, The Perceptron
Writing first NN using Python

-Machine Learning for Email and SMS Spam detection.
-Machine Learning for Malware Analysis
-Machine Learning for Credit card fraud detection.
-Machine Learning for phishing detection.

Speaker

Aniket Sangohi

Timing

Starts at Saturday July 27 2019, 10:00 AM. The sessions runs for 1 day.

Resources