I am working on an emotion recognition using OpenCV and machine learning and in the process tried this out today. This is a landmark detector and shape predictor. It makes the use of haarcascades and 68 defined face points to detect the exact coordinates of the shapes. This facilitates us to learn and observe the changes in facial characters by training a model of different emotions and then comparing the change observed in the video feed to the trained model. I will be using Support Vector Machines (SVM) for the first part of it and will later include more complex training models like Deep-Belief Networks to do the same. Small things like these give you a boost to study and work in the area of machine learning.
Web Scraping is a technique which is used to obtain information from web pages, which saves a lot of time and provides you with abundant data. We will be making a Web Scraper in Python using Beautiful Soup 4, which is a python library for getting data from HTML pages and saves days of work on the code. We will be parsing AccuWeather for getting the weather for Chennai, India.
We will start by installing the packages required
pip install beautifulsoup4
The focus of this post is Image Data Augmentation. When we work with image classification projects, the input which a user will give can vary in many aspects like angles, zoom and stability while clicking the picture. So we should train our model to accept and make sense of almost all types of inputs.
This can be done by training the model for all possibilities. But we can’t go around clicking the same training picture in every possible angles and imagine that when the training set is as big as 10000 pictures!
This can be easily be solved by a technique called Image Data Augmentation, which takes an image, converts it and save it all the possible forms we specify. We will be using Keras for this, which is a deep learning library for Theano and Tensorflow.
While working with datasets, a machine learning algorithm works in two stages — the testing and the training stage. Normally the data split between test-train is 20%-80%.
In order to successfully implement a ML algo, you need to be clear about how to split the data into testing and training, and this short post talks exactly about that.
We will start by installing packages needed.
I love everything that’s old, — old friends, old times, old manners, old books, old wine. — Oliver Goldsmith
Having read that, let us start with our short Machine Learning project on wine quality prediction using scikit-learn’s Decision Tree Classifier.
First of all, we need to install a bunch of packages that would come handy in the construction and execution of our code. Write the following commands in terminal or command prompt (if you are using Windows) of your laptop. Continue reading Wine Quality Prediction Using Machine Learning