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
Eigen Value Decomposition
Principal Component Analysis
Central Limit Theorem
Types of Machine Learning
2.) Training Machine Learning Models
Supervised Machine Learning
Locally Weighted Linear Regression
Other classifier models in sklearn
Mapping non–linear functions using linear techniques
Overfitting and Regularization
Support Vector Machines
3.) Artificial Neural Networks
Specification: Machine Learning and Deep Learning for Interviews & Research