Simple implementation of convolutional neural networks, deep neural networks, recurrent neural networks, and linear regression

Understanding of keras syntax

Understanding of different deep learning algorithms

## Requirements

- Basic python knowledge
- Familiarity with data science and numpy

## Description

When I started learning deep learning, I had a hard time figuring out how everything worked. What library was the best for me? Which algorithms worked best for which data set? How could I know my model was accurate? I spent a lot of time on tutorials, courses and reading to try and answer these questions. In the end, I felt like the process I took to learn deep learning was too inefficient. That is why I created this course.

*Learn Keras: Build 4 Deep Learning Applications* is a course that I designed to solve the problems my past self had. This course is designed to get you up and running with deep learning as quickly as possible. We use keras in this course because it is one of the easiest libraries to learn for deep learning. Each video, we go over a different machine learning algorithm and its use cases. The four algorithms we focus on the most are:

1. Linear Regression

2. Dense Neural Networks

3. Convolutional Neural Networks

4. Recurrent Neural Networks

In conclusion, if you are looking at a quick intro into deep learning, this course is for you.

So what are you waiting for? Let’s get started!

## Who this course is for:

- Someone who wants to get into machine learning but feels overwhelmed by other tutorials
- Someone who is interested in machine learning but doesn’t know where to start