Machine Learning tutorial

Intuitive Introduction to Gaussian Processes

Gaussian Process is a non-parametric model that can be used to represent a distribution over functions.

Understand Neural Networks intuitively

Neural Networks act as a ‘black box’ that takes inputs and predicts an output and it learns complex non-linear mappings to produce far more accurate output classification results.

machine learning

Logistic Regression using Scikit Learn

In this article, we will explore how to implement Logistic Regression in Python using Scikit Learn and create a real demo.

machine learning

Linear regression in Python using Scikit Learn

In this article, we will implement linear regression in Python using scikit-learn and create a real demo and get insights from the results.

machine learning

Hyper Parameter Tuning

Hyperparameter tuning is one of the features that come to the fore to conquer the battle of maximizing the performance of the model or maximizing the model's predictive accuracy.

software engineering

Developing a Live Sketching app using OpenCV and Python

We will develop an application which will show a live sketch of your webcam feed. In this project we'll be using NumPy and OpenCV

machine learning

Various Techniques used for Face Recognition

Did you know that everytime we upload an image to a site like Facebook they use facial recognition to recognize faces in it? Learn its various techniques

machine learning

Introduction to Q Learning and Reinforcement Learning

Read on to learn the basics of reinforcement learning and Q-Learning through an intuitive explanation, and a TensorFlow implementation!

machine learning

K nearest neighbors (KNN) algorithm

K nearest neighbors (K-NN) is an algorithm which is used for classification and regression and is based on the idea of considering the nearest K data points for calculations

machine learning

Beginner's Guide to Generative Adversarial Networks with a demo

Generative Adversarial Network is a network with two opposite components which train to eventually reach the target. This was developed in 2014.

machine learning

Understanding Recurrent Neural Networks with an example of assigning an emoji to a sentence

In this article, we explored the basic ideas of Recurrent Neural Networks with an example to assign an emoji to a sentence based on the emotion

machine learning

Understanding Convolutional Neural Networks through Image Classification

In this article, we explored the ideas involved in Convolutional Neural Networks (CNN) through Image Classification

machine learning

Feature detection as in 1999: SIFT explained with Python implementation

SIFT (Scale Invariant Feature Transform) is a feature detection algorithm in computer vision to detect and describe local features in images designed in 1999

machine learning

Document Clustering using K Means

In this article, we use ideas from TF IDF and similarity metrics to use K Means clustering algorithm to cluster documents.

natural language processing

Find similarity between documents using TF IDF

We will follow NLP techniques like TF IDF to achieve document similarity in this article. Did you know that even today 4 out of 5 systems use NLP techniques to deal with document similarity?

machine learning

Face detection as done in 2001: Viola Jones Algorithm

Viola Jones algorithm is one face detection algorithm which was in use back in 2001 when such applications where not so cool

machine learning

Ensemble methods in Machine Learning: Bagging, Boosting and Stacking

We will see what an ensemble method is, why they are trendy, and what are the different types of ensemble methods and how to implement these methods using scikit-learn and mlxtend in Python.

machine learning

Image to Image Translation using CycleGANs with Keras implementation

Want to know how to generate Monet style paintings from any photograph of any scenery around the world? Enter CycleGANs. Read on to know more about CycleGANs and how they can be used in Image-to-Image Translation.

machine learning

Importance of Loss Function in Machine Learning

A loss function is a measure of how good your prediction model does in terms of being able to predict the expected outcome(or value).

machine learning

Face Aging using Conditional GANs with Keras implementation

Felt intrigued when the FaceApp generated realistic photos of you at an older age? Read on to know how conditional GANs can be used for face aging, and how to implement it on your own using Keras!

machine learning

Basics of Image Classification Techniques in Machine Learning

You will get n idea about What is Image Classification?, pipeline of an image classification task including data preprocessing techniques, performance of different Machine Learning techniques like Artificial Neural Network, CNN, K nearest neighbor, Decision tree and Support Vector Machines

machine learning

Converting colored images (RGB) to grayscale using Autoencoder

We built an autoencoder from scratch in TensorFlow to generate the grayscale images from colored images.

machine learning

Understanding Support Vector Machines (SVMs) in depth

Support vector machines (SVMs) are a particularly powerful and flexible class of supervised algorithms for both classification and regression.

machine learning

Generate new MNIST digits using Autoencoder

In this article, we will learn how autoencoders can be used to generate the popular MNIST dataset and we can use the result to enhance the original dataset.

machine learning

Understanding Deep Convolutional GANs with a PyTorch implementation

In this article, we will briefly describe how GANs work, what are some of their use cases, then go on to a modification of GANs, called Deep Convolutional GANs and see how they are implemented using the PyTorch framework.