Setup collaborative MLflow with PostgreSQL as Tracking Server and MinIO as Artifact Store using docker containers

14 mins read In this post, I will show how to configure MLflow in a way that allows multiple data scientists using different […]

Audio source separation (vocal remover) system based on Deep Learning

12 mins read Table of Contents: Introduction Source Separation Problem Source Separation Use Cases Deep Model Architecture Architecture Training Output Signal Reconstruction Sample […]

A simple tutorial on Sampling Importance and Monte Carlo with Python codes

16 mins read Introduction In this post, I’m going to explain the importance sampling. Importance sampling is an approximation method instead of a […]

What is Reservoir Sampling in Stream Processing?

4 mins read Reservoir sampling is a fascinating algorithm that is especially useful when you have to deal with streaming data, which is […]

A comprehensive tutorial on MLflow for MLOps: From experimentation to production

39 mins read After reading this post you will be able to: Understand how you and your Data Science teams can improve your […]

Understanding DenseNet architecture with PyTorch code

20 mins read DenseNet Architecture Introduction In a standard Convolutional Neural Network, we have an input image, that is then passed through the network […]

Understanding Deep U-Nets for Semantic Segmentation: A salt identification case study with Keras

19 mins read Introduction Deep Learning has enabled the field of Computer Vision to advance rapidly in the last few years. In this […]

Understanding Transposed Convolution with Python example

25 mins read Transposed Convolutions is a revolutionary concept for applications like image segmentation, super-resolution, etc but sometimes it becomes a little trickier […]

Understanding the basics of audio data with Python code

36 mins read Overview A huge amount of audio data is being generated every day in almost every organization. Audio data yields substantial […]

The default Random Forest feature importance is not reliable: Understanding Permutation Feature Importance

47 mins read The scikit-learn Random Forest feature importance and R’s default Random Forest feature importance strategies are biased. To get reliable results […]

Stratified K-fold Cross Validation for imbalanced classification tasks

10 mins read Model evaluation involves using the available dataset to fit a model and estimate its performance when making predictions on unseen […]

How to select classification threshold for imbalanced datasets

21 mins read Classification predictive modeling typically involves predicting a class label. Nevertheless, many machine learning algorithms are capable of predicting a probability […]

Predicting Customer Churn with Machine Learning: From EDA to Classification

27 mins read Table of Contents Introduction Objective Libraries Parameters and Variables Functions A Quick Look at our Data Creating a Test Set […]

Which performance metrics to use for evaluating a classification model on imbalanced datasets?

8 mins read There are various metrics to evaluate a classification model: Accuracy, Precision, Recall F1-score, and AUC-ROC score. However, it is always […]

Understanding the ROC curve and AUC-ROC with Python example

17 mins read AUC (Area Under the Curve)-ROC(Receiver Characteristic Operator) curve helps us visualize how well our machine learning classifier is performing. Although […]

Hyperparameter optimization with Scikit-Learn GridSearchCV using different models

4 mins read Basically it is a bit difficult to manually perform grid search across different models in scikit-learn. We usually need to […]

Visual comparison of decision boundaries for different classifiers

33 mins read There are many debates on how to decide on the best classifier. Measuring the Performance Metrics score, and getting the […]

Handling imbalanced datasets for machine learning tasks

12 mins read You can find the implementation of codes in this post in the GitHub Gist. Introduction When observation in one class […]

Evaluation metrics for Multi-Label Classification with Python codes

10 mins read In a traditional classification problem formulation, classes are mutually exclusive. In other words, under the condition of mutual exclusivity, each […]

Understanding Micro, Macro, and Weighted Averages for Scikit-Learn metrics in multi-class classification with example

11 mins read The F1 score (aka F-measure) is a popular metric for evaluating the performance of a classification model. In the case […]