# A Mathematical and Programming Course on Machine Learning

In this course the mathematical concepts of machine learning will be taught to learners, with python in Google Colab

## What you will learn

### In depth knowledge of mathematics behind building ML models

### How to prepare data for feeding into models

### In depth analysis of support vector machines and their kernels

### Concepts of Ensemble methods in machine learning

### Building Recommendation system by using concepts of machine learning

### Building Recommendation System

### Implementation of CNN models

### Implementation of Fashion MNIST

### Recurrent Neural network

### Quiz at the end of each Section to test the concepts you have learned

### Natural Language Processing

### Active Learning

### Implementation of Cost Estimation functions using TensorFlow from scratch

## Description

This course of "A Comprehensive Course on Machine Learning using python" is a very comprehensive and unique course in itself. Machine Learning is a revolution now days but we cannot master machine learning without getting the mathematical insight, and this course is designed for the same. Our course starts from very basic to advance concepts of machine learning. We have divided the course into different modules which start from the introduction of python its programming basic and important programming constructs which are extensively used in ML programming.

The mathematics involved in Machine learning is normally being not discussed and being left out in , but in our course we have put lot of emphasis in mathematical formulation of algorithms used in ML. We have also designed modules of pandas, sklearn, scipy, seaborn and matplotlib for gearing the students with all important tools which are needed in dealing with data and building the model. The machine learning module focuses on the mathematical derivation on white board through video lectures because we believe that white box view of every concept is very important for becoming an efficient ML expert.

In Machine Learning the cost estimation function also called loss functions are very important to understand and in our course we have explained Cross Categorical Entropy, Sparse Categorical Cross Entropy, and other important cost functions using TensorFlow.

Concepts like gradient descent algorithm, Restricted Boltzmann Algorithm, Perceptron, Multiple Layer Perceptron, Support Vector Machine, Radial Basis Function , Naïve Bayes Classifier, Ensemble Methods, recommendation system and many more are being implemented with examples using Google Colab.

Further I wish best of luck to learners for their sincere efforts in advance…

Use of various components of statistics in analyzing data

Graphical representation of data to get deep insight of the patterns

Mathematical analysis of algorithms to remove the black box view

Practical implementation of all important ML Algorithms

Building various models from scratch using advance algorithms

Understanding the use of ML in research

Quiz at the end of each section