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 …
PixelLib – Segmenting Objects and Videos in Python 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 …
How to Develop LARS Regression Models in Python? Read More »
Disclaimer: The bot built here should be used only as a learning tool. If you choose to do real trading on Binance, then you have to build your own criteria and logic for trading. The author is not responsible for any losses incurred if you choose to use the code developed here on Binance. Note: …
Binance Trading API – Creating Your First Simple Crypto-Trading Bot 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 …
How to Classify Star Wars Lego Images using CNN and Transfer Learning 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 …
[Cheat Sheet] 6 Pillar Machine Learning Algorithms 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 …
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 …
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 …
SVM sklearn: Python Support Vector Machines Made Simple 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 …
Python Scikit-Learn Decision Tree [Video + Blog] 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 …
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 …
K-Nearest Neighbors (KNN) with sklearn in Python Read More »
If there is one clustering algorithm you need to know – whether you are a computer scientist, data scientist, or machine learning expert – it’s the K-Means algorithm. In this tutorial drawn from my book Python One-Liners, you’ll learn the general idea and when and how to use it in a single line of Python …
[Tutorial] K-Means Clustering with SKLearn in One Line Read More »