Artificial Neural Networks (ANN) with Keras in Python and R

Artificial Neural Networks (ANN) with Keras in Python and R | Udemy
English | Size: 4.00 GB
Genre: eLearning

What you’ll learn
Get a solid understanding of Artificial Neural Networks (ANN) and Deep Learning
Learn usage of Keras and Tensorflow libraries
Understand the business scenarios where Artificial Neural Networks (ANN) is applicable
Building a Artificial Neural Networks (ANN) in Python and R
Use Artificial Neural Networks (ANN) to make predictions

You’re looking for a complete Course on Deep Learning using Keras and Tensorflow that teaches you everything you need to create a Neural Network model in Python and R, right?

You’ve found the right Neural Networks 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 R using Keras and Tensorflow libraries and analyze their results.

Confidently practice, discuss and understand Deep Learning concepts

How this course will help you?

A Verifiable Certificate of Completion is presented to all students who undertake this Neural networks course.

If you are a business Analyst or an executive, or a student who wants to learn and apply Deep learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the most advanced concepts of Neural networks and their implementation in Python without getting too Mathematical.

Why should you choose this course?

This course covers all the steps that one should take to create a predictive model using Neural Networks.

Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.

What makes us qualified to teach you?

The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep learning techniques and we have used our experience to include the practical aspects of data analysis in this course

We are also the creators of some of the most popular online courses – with over 250,000 enrollments and thousands of 5-star reviews like these ones:

This is very good, i love the fact the all explanation given can be understood by a layman – Joshua

Thank you Author for this wonderful course. You are the best and this course is worth any price. – Daisy

Our Promise

Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.

Download Practice files, take Practice test, and complete Assignments

With each lecture, there are class notes attached for you to follow along. You can also take practice test to check your understanding of concepts. There is a final practical assignment for you to practically implement your learning.

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 and R basics

This part gets you started with Python.

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.

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 single cells or Perceptrons and how Perceptrons 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 start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. We also solve a regression problem in which we try to predict house prices in a location. We will also cover how to create complex ANN architectures using functional API. Lastly we learn how to save and restore models.

We also understand the importance of libraries such as Keras and TensorFlow in this part.

Part 4 – Data Preprocessing

In this part you will learn what actions you need to take to prepare Data for the analysis, these steps are very important for creating a meaningful.

In this section, we will start with the basic theory of decision tree then we cover data pre-processing topics like missing value imputation, variable transformation and Test-Train split.

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:
People pursuing a career in data science
Anyone curious to master ANN from Beginner level in short span of time

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