This tutorial is about applying object segmentation in five lines of code. We show a beginner-friendly code implementation using the PixelLib library on Google Colab. Does machine learning have to be hard? Well, back then when everything must be coded from scratch, yes. But today, we are stepping on the shoulders of giants. Lots of … Read more
What is LARS regression? Regression is the analysis of how a variable (the outcome variable) depends on the evolution of other variables (explanatory variables). In regression, we are looking for the answer to the question of what is the function that can be used to predict the value of another variable Y by knowing the … Read more
TensorFlow is one of the most popular open-source libraries for machine learning and deep neural networks in Python—initiated by the machine learning engineers at Google! Problem Formulation: Given a PyCharm project. How to install the TensorFlow library in your project within a virtual environment or globally? Here’s a solution that always works: Open File > … Read more
Would you like to be able to scrape information from any article without having to write a completely different set of code every time? In this post, I will show you a Python library which allows you to scrape any article using only a few lines of code. It’s called Newspaper3k. ? Video: As you go … Read more
This tutorial is about training deep learning (DL) models to classify Star Wars Lego images. We use the TensorFlow library to create and compare the image classifiers. Are you looking for interesting deep learning projects that are suitable for beginners? Do not worry, this is not another MNIST image classification tutorial. Instead, we are going … Read more
This machine learning cheat sheet gives you a visual overview of 6 must-know machine learning algorithms (and where to learn more). Linear Regression: train your linear model to predict output values. K-Means Clustering: apply it on unlabeled data to find clusters and patterns in your data. K-Nearest Neighbors: use a similarity metric to find the … Read more
Logistic regression is a popular algorithm for classification problems (despite its name indicating that it is a “regression” algorithm). It belongs to one of the most important algorithms in the machine learning space. Linear Regression Background Let’s review linear regression. Given the training data, we compute a line that fits this training data so that … Read more
Does your model’s prediction accuracy suck but you need to meet the deadline at all costs? Try the quick and dirty “meta-learning” approach called ensemble learning. In this article, you’ll learn about a specific ensemble learning technique called random forests that combines the predictions (or classifications) of multiple machine learning algorithms. In many cases, it … Read more
Support Vector Machines (SVM) have gained huge popularity in recent years. The reason is their robust classification performance – even in high-dimensional spaces: SVMs even work if there are more dimensions (features) than data items. This is unusual for classification algorithms because of the curse of dimensionality – with increasing dimensionality, data becomes extremely sparse … Read more
Decision Trees are powerful and intuitive tools in your machine learning toolbelt. Decision trees are human-readable – in contrast to most other machine learning techniques. You can easily train a decision tree and show it to your supervisors who do not need to know anything about machine learning in order to understand how your model … Read more
Neural Networks have gained massive popularity in the last years. This is not only a result of the improved algorithms and learning techniques in the field but also of the accelerated hardware performance and the rise of General Processing GPU (GPGPU) technology. In this article, you’ll learn about the Multi-Layer Perceptron (MLP) which is one … Read more
The popular K-Nearest Neighbors (KNN) algorithm is used for regression and classification in many applications such as recommender systems, image classification, and financial data forecasting. It is the basis of many advanced machine learning techniques (e.g., in information retrieval). There is no doubt that understanding KNN is an important building block of your proficient computer … Read more