Stochastic Gradient Descent in Python: Interactive Guide with a Learning-Rate Race
Learn stochastic gradient descent by playing: loss functions, batch vs mini-batch vs stochastic, a live learning-rate race, and SGDClassifier code.
Learn stochastic gradient descent by playing: loss functions, batch vs mini-batch vs stochastic, a live learning-rate race, and SGDClassifier code.
Learn blending by playing: hold-out validation sets, base models and the blender, how blending differs from stacking, and leak-free Python code.
Learn the confusion matrix, accuracy, precision, recall, specificity, F1, MCC, log loss and ROC AUC by playing with a live threshold and ROC curve on real data.
Learn support vector machines by playing: hyperplanes, margins and support vectors, the C parameter on real data, the kernel trick, and SVM code in Python.
Silhouette score Bouldin index Calinski-Harabasz Index Dunn Index
In this blog we will going to learn about the Knowledge distillation, its types and the implementation in python. Introduction Knowledge distillation is a technique in machine learning where knowledge…
Blending is an ensemble machine learning technique that uses a machine learning model to learn how to best combine the predictions from multiple contributing ensemble member models. It is applicable…
In this blog you will be going to learn all the important commands in this Github crash course that 90% of the Data Scientist used in their day to day…
In the era of Machine learning, no single algorithm performs best across all problems. This is where ensemble learning comes into play. A powerful technique that combines the strengths of…
In this blog, we’ll walk through how to build an ETL pipeline that connects Salesforce with Azure Data Factory. You’ll learn how to extract, transform, and load Salesforce data into…