1.2 Project Components
1.3 Installing Python
1.4 Install OpenCV
2. Image Processing with OpenCV
2.2 Understanding Image
2.3 Display and Depth
2.4 Image Pixels
2.5. Image Pixels part2
2.6 Image Resizing
2.7 Object Detection
2.8 Working on Videos
3. Machine Learning
3.2 Machine Learning Pipeline Architecture
3.3 Data Understanding
3.4 Crop Faces from Image Data
3.5 Dealing with Unstructured Data
3.6 Data Analysis
3.7 Data Preprocessing
3.8 Data Preprocessing part2
3.9 Eigen Faces with PCA
3.10 Train Machine Learning Model
3.11 Model Evaluation
3.12 Tuning Machine Learning Model
3.12 Tuning Machine Learning Model Part2
3.13 Make Pipeline Model
This course is divided into five modules those are 1. Setting up your project 2. Image Processing with opencv 3. Data Analysis and training machine learning model in python 4. Flask 5. Building web app in Flask and integrate machine learning model to the web app.
Module -1 : Setting up Your Project
In this module you will do installations that are necessary for the project like installing Python, libraries that are required for the project and also we will see how to create virtual environment and programming in virutal environment.
click here for “requirements.txt”
Module-2: Image Processing with OpenCV
In this module, you will learn about images and pixels also you will understand how they are created. You will also learn programming in opencv python in jupyter notebook like reading, indexing, spliting, grayscale, croping, reshape, resize, object detection (detecting face) and finally apply all the techniques to video
Module-3: Train Machine Learning model for Face Recognition
Here you will work on real data of images and will train the machine learning for classifiying gender (male or female). In order to train the accurate model you need to understand the dynamics of image like shape, features in image etc. For that you will learn data analysis for images and extrating features in face using Principal Component Analysis (PCA) also know as Eigen Image. After that we train the machine leanring model in our case we will use Support Vector Machine (SVM). Also we will see parameter tuning in SVM. Finally will create pipeline function. All these analysis, preprocessing and modelling will be seen in Python.
In the previous lectures you have put into practise on data anslysis and machine learning. Now it is time to build an web app for the user experience. In order to that we will learn about one of the popular and easiest web framework in Python is Flask. Flask is an web server gateway interface (WSGI) where you will written in Python in server side and in client site you will render HTML, CSS and JS. Here we will touch on concepts related to web framework.
Module-5: Web App, Machine Learning in Server Side.
Now we will build the website in Flask using HTML, CSS, Bootstrap and machine learning pipeline model in backend. Here you will learn to create beautiful website with navigating pages and also we will you learn to integrate pipeline model to our App. And finally your Machine Learning Web App is Ready.