Last Updated on March 27, 2024 by GeeksGod

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

Free Udemy Coupon for Artificial Neural Networks using Keras in Python and R

If 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, you’ve found the right place!

After completing this course, you will be able to:

• Identify the business problems 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 will this course help you?

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

If you are a Business Analyst, an executive, or a student who wants to learn and apply Deep learning to real-world problems in 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. After running the analysis, one should be able to judge how good the model is and interpret the results to actually help the business.

What makes us qualified to teach you?

The course is taught by Abhishek and Pukhraj. As managers in a Global Analytics Consulting firm, we have helped businesses solve their business problems 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:

• “This is very good, I love the fact that all explanations 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 tests, and complete assignments

With each lecture, there are class notes attached for you to follow along. You can also take practice tests 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 Artificial Neural Network:

Part 1 – Python and R Basics

This part gets you started with Python. It helps you set up the Python and Jupyter environment on your system and teaches you how to perform some basic operations in Python. You will also understand the importance of different libraries such as Numpy, Pandas, and Seaborn.

Part 2 – Theoretical Concepts

This part will give you a solid understanding of concepts involved in Neural Networks. You will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once the 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 the 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 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

This part covers the steps you need to take to prepare data for analysis. These steps are very important for creating a meaningful analysis. We start with the basic theory of a decision tree and 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.

Go ahead and click the enroll button, and we’ll see you in lesson 1!

Cheers,

Start-Tech Academy

Frequently Asked Questions (FAQ)

Below are some popular FAQs of students who want to start their Deep learning journey:

Why use Python for Deep Learning?

Understanding Python is one of the valuable skills needed for a career in Deep Learning.

Python is the programming language of choice for data science. It has been widely adopted by data scientists and analytics professionals. Deep Learning experts expect this trend to continue with increasing development in the Python ecosystem. Employment opportunities are abundant and growing in this field.

What is the difference between Data Mining, Machine Learning, and Deep Learning?

Data Mining, Machine Learning, and Deep Learning use similar algorithms and techniques, but the kinds of predictions they make vary.

Data Mining discovers previously unknown patterns and knowledge, while Machine Learning reproduces known patterns and knowledge and further applies that information to data, decision-making, and actions.

Deep Learning uses advanced computing power and special types of neural networks to learn, understand, and identify complicated patterns. It is used in applications like automatic language translation and medical diagnoses.

**Note: Don’t forget to grab our Udemy Coupon for a free registration!