Image Processing (Digital Halftoning, Compression, ML & DL Models)

In Machine Learning, Classifiers  learns from the training data, and models some decision making framework. With that knowledge it classifies new test data. For example, if we train a certain classifier on different kinds of fruits by providing some information like shape, color, taste and so on, given any new fruit with the following details it can predict what would be the exact or close match. Similar concept is used here to classify handwritten digits, and here it is performed with Random Forest Classifier. Random Forest Classifier -A random forest consist of combination of uncorrelated decision trees (Fig.1) and each works on various sub samples of the dataset. As each tree have different error instances averaging them all can provide better predictive accuracy and control over-fitting. This tutorial can provide brief idea on the same https://www.youtube.com/watch?v=loNcrMjYh64 MNIST Dataset consist of handwritten characters for training and testing. The .csv format ...

Python Code -- Regression Training and Testing. ( https://www.youtube.com/watch?v=r4mwkS2T9aI&index=4&list=PLQVvvaa0QuDfKTOs3Keq_kaG2P55YRn5v ) In addition to this video, some implementation techniques are added. Initially i tried with Python27, but it ended up with errors( Discussed below), so its recommended to use Python3 Open Command Window and install the following C:\Python27\Scripts\pip install sklearn C:\Python27\Scripts\pip install quandl C:\Python27\Scripts\pip install scipy C:\Python27\Scripts\pip install pyparsing If not installed properly try this link and download right version of scipy depends on your Pyton27/34 Make sure you have installed this, (But using python 27 is not preferable as it doesnt have timespace() function) But it is better to install Anaconda3, (as it take care of installing all libraries no need to install manually ) https://www.continuum.io/downloads after installing it, open cmd windows  type: conda install q...