Machine Learning and Deep Learning for Interviews & Research

Interested in Machine Learning, and Deep Learning and preparing for your interviews or research? Then, this course is for you!

The course is designed to provide the fundamentals of machine learning and deep learning. It is targeted toward newbies, scholars, students preparing for interviews, or anyone seeking to hone the data science skills necessary. In this course, we will cover the basics of machine learning, and deep learning and cover a few case studies.

This short course provides a broad introduction to machine learning, and deep learning. We will present a suite of tools for exploratory data analysis and machine learning modeling. We will get started with python and machine learning and provide case studies using keras and sklearn.

### MACHINE LEARNING ###

1.) Advanced Statistics and Machine Learning

Covariance

Eigen Value Decomposition

Principal Component Analysis

Central Limit Theorem

Gaussian Distribution

Types of Machine Learning

Parametric Models

Non–parametric Models

2.) Training Machine Learning Models

Supervised Machine Learning

Regression

Classification

Linear Regression

Gradient Descent

Normal Equations

Locally Weighted Linear Regression

Ridge Regression

Lasso Regression

Other classifier models in sklearn

Logistic Regression

Mapping non–linear functions using linear techniques

Overfitting and Regularization

Support Vector Machines

Decision Trees

3.) Artificial Neural Networks