Description

Artificial neural networks (ANNs) are the most powerful machine learning algorithms available today. They are capable of learning complex relationships in data, and they have been used to achieve state-of-the-art results in a wide variety of fields, including image recognition, natural language processing, and speech recognition.

The Future of Machine Learning is Here!

This Project on Deep Learning - Artificial Neural Network course will teach you how to build and train ANNs from scratch. You will learn about the different components of an ANN, such as the input layer, hidden layers, and output layer. You will also learn about the different activation functions that can be used in ANNs, and you will see how to optimise ANNs for different tasks.

In addition to the theoretical concepts, you will also get experience with ANNs. You will work on a project where you will build an ANN to classify images. You will use the TensorFlow library to build your ANN, and you will see how to train your ANN on a dataset of images.

By the end of this Project on Deep Learning - Artificial Neural Network course, you will have a deep understanding of ANNs and how to use them. You will be able to build your own ANNs to solve a variety of problems. You will also be able to use the TensorFlow library to build and train ANNs.

So what are you waiting for? Enrol in this course today and start learning about the future of machine learning!

Learning Outcomes:

Through this comprehensive course, you should be able to:

Understand the fundamental concepts of deep learning and artificial neural networks.

Install and configure an artificial neural network framework.

Preprocess and structure data for optimal model performance.

Encode data effectively for neural network training and predictions.

Build and deploy artificial neural networks for real-world applications.

Address data imbalance challenges and optimise model accuracy.

Who is this course for?

This Project on Deep Learning - Artificial Neural Network course is ideal for:

Data scientists and machine learning practitioners seeking to expand their knowledge.

Software engineers interested in leveraging deep learning techniques.

Students pursuing a career in artificial intelligence and machine learning.

Professionals looking to enhance their skills in neural network development.

Individuals with a passion for exploring advanced machine learning techniques.

Career Path

Our course will prepare you for a range of careers, including:

Deep Learning Engineer: £40,000 - £100,000 per year.

Machine Learning Researcher: £45,000 - £120,000 per year.

Data Scientist: £50,000 - £110,000 per year.

Artificial Intelligence Specialist: £55,000 - £130,000 per year.

Software Engineer (specialising in AI): £45,000 - £100,000 per year.

Research Scientist (Machine Learning): £50,000 - £120,000 per year.

AI Consultant: £60,000 - £150,000 per year.

Certification

After studying the course materials of the Project on Deep Learning - Artificial Neural Network (ANNs) there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60.

Prerequisites

This Project on Deep Learning - Artificial Neural Network (ANNs) does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Project on Deep Learning - Artificial Neural Network (ANNs) was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection.

Course Curriculum

Section 01: Introduction
	Introduction of Project	00:03:00
Section 02: ANN Installation
	Setup Environment for ANN	00:11:00
	ANN Installation	00:09:00
Section 03: Data Preprocessing
	Import Libraries and Data Preprocessing	00:11:00
	Data Preprocessing	00:07:00
	Data Preprocessing Continue	00:10:00
Section 04: Data Encoding
	Data Exploration	00:10:00
	Encoding	00:07:00
	Encoding Continue	00:06:00
	Preparation of Dataset for Training	00:04:00
Section 05: Steps to Build ANN
	Steps to Build ANN Part 1	00:06:00
	Steps to Build ANN Part 2	00:06:00
	Steps to Build ANN Part 3	00:06:00
	Steps to Build ANN Part 4	00:09:00
Section 06: Predictions and Imbalance-Learn
	Predictions	00:11:00
	Predictions Continue	00:08:00
	Resampling Data with Imbalance-Learn	00:09:00
	Resampling Data with Imbalance-Learn Continue	00:08:00

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Project on Deep Learning - Artificial Neural Network (ANNs)

By Studyhub UK

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