Mathematics of Machine Learning (Part 1)

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What this talk is about:

1. A brief introduction to ML - Types of Algorithms, Training and Test datasets, Cost Functions

2. Deflating the hype surrounding maths in ML

3. Explaining basic Linear Algebra, Probability and Calculus portions that are required

4. Exploring the under-the-hood working of simple ML algorithms with theory, readings and code for :

a. Linear Regression

b. K-means clustering

c. Naive Bayes classifier

What this talk isn’t about:

1. Learning ML in Python

2. A tutorial on ML/DL libraries such as Tensorflow, etc to make an ML application

3. A theoretical maths class