Machine Learning

MachineX: Evaluation Metrics for Classification Models

Reading Time: 5 minutes In our last blog post, we have looked at various evaluation metrics for the regression model. Continuing on this we will take a look at the evaluation metrics used for classification models. Classification is about predicting class labels given input data. In binary classification, there are two possible output classes whereas in Multi-class classification we have more than two possible output classes. We are going Continue Reading

Boosting medical diagnosis with Klickare

Reading Time: 4 minutes In this blog, we are going to see how KlicKare can boost up medical diagnosis by using deep learning. Medical diagnostics are a category of medical tests designed to detect infections, conditions, and diseases. These medical diagnostics fall under the category of in-vitro medical diagnostics (IVD) which be purchased by consumers or used in laboratory settings. Biological samples are isolated from the human body such Continue Reading

MachineX: Top 10 data Science use cases in Retail

Reading Time: 8 minutes In this blog, we will see some of the data science use cases in Retail industries and how it is transforming the customer experience. We are all aware of the troves of data, retail businesses generate on a daily basis. However, this repository of critical data is worthless if it cannot be translated into valuable insights into the consumer’s minds or market trends. While all Continue Reading

MachineX: Alphabets of PyTorch (Part 1)

Reading Time: 6 minutes Overview In this blog, you’ll get an introduction to deep learning using the PyTorch framework, we will see some basics of PyTorch. Introduction to PyTorch PyTorch is a Python machine learning package based on Torch, which is an open-source machine learning package based on the programming language Lua. Two main features: Tensor computation (like NumPy) with strong GPU acceleration Automatic differentiation for building and training Continue Reading

MachineX: k-Nearest Neighbors(KNN) for Regression

Reading Time: 5 minutes Introduction K Nearest Neighbor Regression (KNN) works in much the same way as KNN for classification. The difference lies in the characteristics of the dependent variable. With classification KNN the dependent variable is categorical. With regression KNN the dependent variable is continuous. Both involve the use neighboring examples to predict the class or value of other examples. In this blog we will understand the basics Continue Reading

MachineX: Genetic Algorithm

Reading Time: 2 minutes Genetic algorithm is based on the Charles Darwin famous principle of survival of the fittest, where the fittest of the individuals are given higher importance and are chosen for reproduction in order to produce children for the new generation. The process starts by selecting the fittest individuals from a population, who then produce offspring which inherit the characteristics of the parents. Since the parents already Continue Reading

MachineX: Evaluation Metrics for a Regression ML Model

Reading Time: 3 minutes In this blog post, we will quickly look at the various metrics to evaluate our regression models. But first, let us briefly discuss one of the best-known model evaluation approach we use which is Train-Test or also known as Train-Validation split. Train-Test Split: In this approach, we split the data into two parts known as Training set and Test set. The model is then trained Continue Reading

TensorFlow for deep learning Part 1

Reading Time: 3 minutes TensorFlow is a free and Open-Source Software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library and is also used for machine learning applications such as neural networks. It is used for both research and production at Google. TensorFlow was developed by the Google Brain team for internal Google use. Deep learning is a particular kind of Continue Reading

MachineX: What is K-Fold Cross Validation?

Reading Time: 3 minutes In this blog, we are going to explore and learn about K-Fold Cross Validation. K-Fold Cross Validation is a statistical method to evaluate a Machine Learning model’s performance. So, to understand what K-Fold Cross Validation is, we first need to understand what evaluating a model means, and why do we need to do that.

MachineX: Logistic Regression with KSAI

Reading Time: 2 minutes Logistic Regression, a predictive analysis, is mostly used with binary variables for classification and can be extended to use with multiple classes as results also. We have already studied the algorithm in deep with this blog. Today we will be using KSAI library to build our logistic regression model. Setup

MachineX: Association Rule Learning with KSAI

Reading Time: 2 minutes In many of my previous blogs, I have posted about Association Rule Learning, what it’s about and how it is performed. In this blog, we are going to use Association Rule Learning to actually see it in action, and for this purpose, we are going to use KSAI, a machine learning library purely written in Scala. So, let’s begin. Adding KSAI to your project You Continue Reading

MachineX: A tour to KSAI – Neural Networks

Reading Time: 4 minutes In this blog we would look into how we can use KSAI; A machine learning library purely written in Scala using most of its feature and functional aspects of programming, you can read more about the library at KSAI Wiki, alternatively you can even fork the project from here, KSAI has a rich set of algorithms that address some of the vital problems in classification, Continue Reading