Course Information
- Available
- *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Course Overview
A Practical Hands-on Data Science Guided Project on Covid-19 Pneumonia Classification through X-rays using Deep Learning
Would you like to learn how to Predict if someone has a Coronavirus infection through the X-ray of their lungs?
Would you like to build a Convolutional Neural Network model using Deep learning to detect Covid-19?
If the answer to any of the above questions is "YES", then this course is for you.
Enroll Now in this course and learn how to detect Coronavirus in a patient through the X-Ray reports of their lungs. This is the
You might be wondering if it is really possible to detect Coronavirus in a patient through the X-Ray reports of their lungs.
YES, IT IS POSSIBLE THROUGH DEEP LEARNING AND CONVOLUTIONAL NEURAL NETWORKS.
As we know, Coronavirus affects the lungs of the victims and causes Pneumonia especially termed as COVID Pneumonia. Through Deep learning technologies and Convolutional Neural Networks, we can analyze the X-Ray reports of lungs to the Pixel level. Without any PCR or RDT test, Coronavirus can be detected if the virus has infected the lungs of the patients through Convolutional Neural Network with approximate 98 percent accuracy.
This is a hands-on Data Science guided project on Covid-19 Pneumonia Classification. No unnecessary lectures. As our students like to say :
"Short, sweet, to the point course"
The same techniques can be used in :
Skin cancer detection
Normal pneumonia detection
Brain defect analysis
Retinal Image Analysis
And any other diseases that use image-based reporting, like X-ray reports.
Enroll now and You will receive a CERTIFICATE OF COMPLETION and we encourage you to add this project to your resume. At a time when the entire world is troubled by Coronavirus, this project can catapult your career to another level.
So bring your laptop and start building, training and testing the Data Science Covid 19 Convolutional Neural Network model right now.
You will learn:
How to detect Coronavirus infection using the Xray Report of the lungs of Patients
Classify COVID 19 based on x-ray images using deep learning
Learn to Build and train a Convolutional neural network
Make a prediction on new data using CNN Model
We will be completing the following tasks:
Task 1: Getting Introduced to Google Colab Environment & importing necessary libraries
Task 2: Importing, Cloning & Exploring Dataset
Task :3 Data visualization (Image Visualization)
Task 4: Data augmentation & Normalization
Task 5: Building Convolutional neural network model
Task 6: Compiling & Training CNN Model
Task 7: Performance evaluation & Testing the model & saving the model for future use
So, grab a coffee, turn on your laptop, click on the ENROLL NOW button, and start learning right now.
Course Content
- 8 section(s)
- 9 lecture(s)
- Section 1 Introduction
- Section 2 Introduction to the Platform
- Section 3 Clone & Explore Dataset
- Section 4 Data Visualization
- Section 5 Data Augmentation & Normalization
- Section 6 Build Convolutional Neural Network Model
- Section 7 Compile & Train CNN Model
- Section 8 Performance evaluation & Testing the Model
What You’ll Learn
- Get Hands-On Practice to classify COVID-19 based on X-ray images using Deep Learning
- Learn to Build and Train Convolutional Neural Network Model
- Make Predictive Analysis on COVID-19 through new data using CNN Model
- Learn to Test CNN models and analyze their performances
- Learn how to predict Coronavirus in patients through the X ray of their lungs
Skills covered in this course
Reviews
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MMilaine Sergine Seuneu Tchamga
very nice. Simple and easy to follow. thank you.
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CChethani Charmi Jayanga
tutorial how to to export this results into an interface would be great as an ending for the course... Thanks you...
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EEnes Asana
The course is excellent, well-explained, short, essence :) but the speaker talks fast sometimes.
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OOluwatobi Akinlade
The tutor knows how to explain the concept very and it was easy to understand.