Learn Artificial Neural Network From Scratch in Python

The MOST in-depth look at neural network theory, and how to code one with pure Python and Numpy

Requirements
Basic math - some essential parts will be covered in the course itself so to make course beginner friendly
Linear and Logistic Regression
Basic understanding of classification and Regression problems
Description
Welcome to the course where we will learn about Artificial Neural Network (ANN) From Scratch!

If you're looking for a complete Course on Deep Learning using ANN that teaches you everything you need to create a Neural Network model in Python?

You've found the right Neural Network course!

After completing this course you will be able to:

Identify the business problem which can be solved using Neural network Models.

Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc.

Create Neural network models in Python and ability to optimize the model tuning hyper parameters

Confidently practice, discuss and understand Deep Learning concepts

This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE.

You should take this course if you are interested in starting your journey toward becoming a master at deep learning, or if you are interested in machine learning and data science in general. We go beyond basic models like logistic regression and linear regression and I show you something that automatically learns features.

What is covered in this course?

This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems.

Below are the course contents of this course on ANN:

Part 1 - Python basics

This part gets you started with Python and learn the brush up the basics like data structures, comprehensions, Object Oriented Programming and so on.

This part will help you set up the python and Jupyter environment on your system and it'll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas, Seaborn and matplotlib libraries.

Part 2 - Theoretical Concepts

This part will give you a solid understanding of concepts involved in Neural Networks.

In this section you will learn about the neurons and how neurons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.

Part 3 - Creating Regression and Classification ANN model in Python and R

In this part you will learn how to create ANN models in Python.

We will learn how to model the neural network in two ways: first we model it from scratch and after that using scikit-learn library.

Part 4 - Tutorial numerical examples on Backpropagation

One of the most important concept of ANN is backpropagation, so in order to apply the theory we learnt in lecture session in the real world neural networks, we are going to execute backpropagation taking one numerical example. We are going to take the help of partial differentiation and update the weights in backpropagation using gradient descent algorithms.

By the end of this course, your confidence in creating a Neural Network model in Python will soar. You'll have a thorough understanding of how to use ANN to create predictive models and solve business problems.



Who this course is for:
Students interested in machine learning - all the concepts are given through three sessions: Lecture, Tutorial and Coding sessions
Students who want to learn the mathematics behind neural networks which in turn will make you ninja in ANN
Students who want to prefer learning the core of neural networks rather than learning how to do it with libraries