Data Science- CNN OpenCV - Chest XRAY-Pneumonia Detection

Data Science: CNN & OpenCV : Chest XRAY-Pneumonia Detection, available at $44.99, has an average rating of 4.1, with 44 lectures, based on 14 reviews, and has 71 subscribers.

You will learn about Data Analysis and Understanding Data Augumentation Data Generators Model Checkpoints CNN and OpenCV Pretrained Models like MobileNetV2 Compiling and Fitting a customized pretrained model Model Evaluation Model Serialization Classification Metrics Model Evaluation Using trained model to detect Pneumonia using Chest XRays This course is ideal for individuals who are Anyone who is interested in Deep Learning. or Someone who want to learn Deep Learning, Tensorflow, CNN, OpenCV, and also using and customizing pretrained models for image classification. or Someone who wants to use AI to detect the presence of Pneumonia using Chest XRays. It is particularly useful for Anyone who is interested in Deep Learning. or Someone who want to learn Deep Learning, Tensorflow, CNN, OpenCV, and also using and customizing pretrained models for image classification. or Someone who wants to use AI to detect the presence of Pneumonia using Chest XRays.

Enroll now: Data Science: CNN & OpenCV : Chest XRAY-Pneumonia Detection

Summary

Title: Data Science: CNN & OpenCV : Chest XRAY-Pneumonia Detection

Price: $44.99

Average Rating: 4.1

Number of Lectures: 44

Number of Published Lectures: 44

Number of Curriculum Items: 44

Number of Published Curriculum Objects: 44

Original Price: ?999

Quality Status: approved

Status: Live

What You Will Learn

Data Analysis and Understanding

Data Augumentation

Data Generators

Model Checkpoints

CNN and OpenCV

Pretrained Models like MobileNetV2

Compiling and Fitting a customized pretrained model

Model Evaluation

Model Serialization

Classification Metrics

Model Evaluation

Using trained model to detect Pneumonia using Chest XRays

Who Should Attend

Anyone who is interested in Deep Learning.

Someone who want to learn Deep Learning, Tensorflow, CNN, OpenCV, and also using and customizing pretrained models for image classification.

Someone who wants to use AI to detect the presence of Pneumonia using Chest XRays.

Target Audiences

Anyone who is interested in Deep Learning.

Someone who want to learn Deep Learning, Tensorflow, CNN, OpenCV, and also using and customizing pretrained models for image classification.

Someone who wants to use AI to detect the presence of Pneumonia using Chest XRays.

If you want to learn the process to detect whether a person is having Pneumonia using Chest XRays with the help of AI and Machine Learning algorithms then this course is for you.

In this course I will cover, how to build a model to predict whether an X-ray scan shows presence of pneumonia with very high accuracy using Deep Learning Models. This is a hands on project where I will teach you the step by step process in creating and evaluating a deep learning model using Tensorflow, CNN and OpenCV.

This course will walk you through the initial data exploration and understanding, Data Augumentation,Data Generators,customizing pretrained Models like MobileNetV2, Model Checkpoints, model building and evaluation.Then using the trained model to detect the presence of Pneumonia using Chest XRays.

I have splitted and segregated the entire course in Tasks below, for ease of understanding of what will be covered.

Task 1  :  Project Overview.

Task 2  :  Introduction to Google Colab.

Task 3  :  Understanding the project folder structure.

Task 4  :  Understanding the dataset and the folder structure.

Task 5  :  Setting up the project in Google Colab_Part1

Task 6  : Setting up the project in Google Colab_Part2

Task 7  :  About Config and Create_Dataset File

Task 8  :  Importing the Libraries.

Task 9  :  Plotting the count of data against each class in each directory

Task 10  :  Plotting some samples from both the classes

Task 11  :  Creating a common method to get the number of files from a directory

Task 12  :  Defining a method to plot training and validation accuracy and loss

Task 13  :  Calculating the class weights in train directory

Task 14 :  About Data Augmentation.

Task 15 :  Implementing Data Augmentation techniques.

Task 16 :  About Data Generators.

Task 17 :  Implementing Data Generators.

Task 18 :  About Convolutional Neural Network (CNN).

Task 19 :  About OpenCV.

Task 20 :  Understanding pre-trained models.

Task 21 :  About MobileNetV2 model.

Task 22 :  Loading the MobileNetV2 classifier.

Task 23 :  Building a new fully-connected (FC) head.

Task 24 :  Building the final MobileNetV2 model.

Task 25 :  Understanding Conv2D, Filters, Relu activation, Batch Normalization, MaxPooling2D, Dropout, Flatten, Dense

Task 26 :  Building a custom CNN network architecture.

Task 27 :  Role of Optimizer in Deep Learning.

Task 28 :  About Adam Optimizer.

Task 29 :  About binary cross entropy loss function.

Task 30 :  Putting all together for MobileNetV2.

Task 31 :  Putting all together for Custom CNN Model.

Task 32 :  About Model Checkpoint

Task 33 :  Implementing Model Checkpoint

Task 34 :  About Epoch and Batch Size.

Task 35 :  MobileNetV2 and Custom CNN Model Fitting.

Task 36 :  Predicting on the test data using both MobileNetV2 and Custom CNN Model

Task 37 :  About Classification Report.

Task 38 :  Classification Report in action for both MobileNetV2 and Custom CNN Model.

Task 39 :  Computing the confusion matrix and and using the same to derive the accuracy, sensitivity and specificity.

Task 40 :  Plot training and validation accuracy and loss

Task 41 :  Serialize/Writing the mode to disk

Task 42 :  Loading the final model from drive

Task 43 :  Loading an image and predicting using the model whether the person has Pneumonia.

Machine learning has a phenomenal range of applications, including in health and diagnostics. This course will explain the complete pipeline from loading data to predicting results on cloud, and it will explain how to build an X-ray image classification model from scratch to predict whether an X-ray scan shows presence of pneumonia. This is especially useful during these current times as COVID-19 is known to cause pneumonia.

Take the course now, and have a much stronger grasp of Deep learning in just a few hours!

You will receive :

1. Certificate of completion from AutomationGig.

2. The Jupyter notebook and other project files are provided at the end of the course in the resource section.

So what are you waiting for?

Grab a cup of coffee, click on the ENROLL NOW Button and start learning the most demanded skill of the 21st century. We’ll see you inside the course!

Happy Learning !!

[Please note that this course and its related contents are for educational purpose only]

[Music : bensound]

Course Curriculum

Chapter 1: Introduction and Getting Started

Lecture 1: Project Overview

Lecture 2: Introduction to Google Colab

Lecture 3: Understanding the project folder structure

Chapter 2: Data Understanding & Importing Libraries

Lecture 1: Understanding the dataset and the folder structure

Lecture 2: Setting up the project in Google Colab_Part1

Lecture 3: Setting up the project in Google Colab_Part2

Lecture 4: About Config and Create_Dataset File

Lecture 5: Importing the Libraries

Lecture 6: Plotting the count of data against each class in each directory

Lecture 7: Plotting some samples from both the classes

Chapter 3: Common Methods for plotting and class weight calculation

Lecture 1: Creating a common method to get the number of files from a directory

Lecture 2: Defining a method to plot training and validation accuracy and loss

Lecture 3: Calculating the class weights in train directory

Chapter 4: Data Augmentation

Lecture 1: About Data Augmentation

Lecture 2: Implementing Data Augmentation techniques

Chapter 5: Data Generators

Lecture 1: About Data Generators

Lecture 2: Implementing Data Generators

Chapter 6: Model Building

Lecture 1: About Convolutional Neural Network (CNN)

Lecture 2: About OpenCV

Lecture 3: Understanding pre-trained models

Lecture 4: About MobileNetV2 model

Lecture 5: Loading the MobileNetV2 classifier

Lecture 6: Building a new fully-connected (FC) head

Lecture 7: Building the final MobileNetV2 model

Lecture 8: Understanding Conv2D, Filters, Relu activation, Batch Normalization, MaxPooling2

Lecture 9: Building a custom CNN network architecture

Chapter 7: Compiling the Model

Lecture 1: Role of Optimizer in Deep Learning

Lecture 2: About Adam Optimizer

Lecture 3: About binary cross entropy loss function.

Lecture 4: Putting all together for MobileNetV2

Lecture 5: Putting all together for Custom CNN Model

Chapter 8: ModelCheckpoint

Lecture 1: About Model Checkpoint

Lecture 2: Implementing Model Checkpoint

Chapter 9: Fitting the Model

Lecture 1: About Epoch and Batch Size

Lecture 2: MobileNetV2 and Custom CNN Model Fitting

Chapter 10: Model Evaluation

Lecture 1: Predicting on the test data using both MobileNetV2 and Custom CNN Model

Lecture 2: About Classification Report

Lecture 3: Classification Report in action for both MobileNetV2 and Custom CNN Model

Lecture 4: Computing the confusion matrix and using the same to derive the accuracy, sensit

Lecture 5: Plot training and validation accuracy and loss

Lecture 6: Serialize/Writing the model to disk

Chapter 11: Using trained model to predict whether a person has Pneumonia

Lecture 1: Loading the final model from drive

Lecture 2: Loading an image and predicting using the model whether the person has Pneumonia

Chapter 12: Project Files and Code

Lecture 1: Full Project Code

Instructors

AutomationGig .
ELEARNING HUB

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2 stars: 0 votes

3 stars: 4 votes

4 stars: 6 votes

5 stars: 3 votes

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