Machine Learning Deep Learning for Interviewees & Researcher

Machine Learning, Linear Regression, PCA, Neural Networks, Hyperparameters, Deep Learning, Keras

What you'll learn
Fundamentals of machine learning and deep learning with respect to big data applications.
Machine learning and deep learning concepts required to give data science interviews.
Suite of tools for exploratory data analysis and machine learning modeling.
Coding-based case studies

Requirements
Basic knowledge of programming is required.
No prior data science experience required. You will learn everything you need to know in the course.
Description
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

Forward Propagation

Backward Propagation

Activation functions

Hyperparameters

Overfitting

Dropout

4.) Training Deep Neural Networks

Deep Neural Networks

Convolutional Neural Networks

Recurrent Neural Networks (GRU and LSTM)

5.) Unsupervised Learning

Clustering (k-Means)

6.) Implementation and Case Studies

Getting started with Python and Machine Learning

Case Study - Keras Digit Classifier

Case Study - Load Forecasting

So what are you waiting for? Learn Machine Learning, and Deep Learning in a way that will enhance your knowledge and improve your career!

Thanks for joining the course. I am looking forward to seeing you. let's get started!

Who this course is for
Machine learning enthusiasts, scholars or anyone seeking to hone the data science skills necessary
Beginner and intermediate developers interested in data science.