Feature Engineering at Scale: PySpark, Python & Snowflake
Imagine you’re staring at a database containing thousands of merchants across multiple countries, each with its own website.
Machine Learning
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The Power of Ensemble Learning: A Comprehensive Python Guide
Unlock AI’s true potential with Ensemble Learning! Dive into bagging, boosting, stacking, and voting techniques in Python with scikit-learn.
10 Must-Know Machine Learning Algorithms
Master these, and you won’t just be dipping your toes in the machine learning pool — you’ll be doing cannonballs into real-world problem-solving.
PyTorch: A Quick & Dirty Intro
This article provides a hands-on introduction to PyTorch, covering installation, building a simple linear regression model, data preparation, training, evaluation, and further resources.
Handling Categorical Variables in scikit-learn: Strategies and Encoding Techniques
Categorical variables must be encoded before use in scikit-learn models. This article covers 3 of the core strategies and best practices for handling categorical features in machine learning with code examples.
An Overview of Feature Selection Techniques in scikit-learn
This article provides an overview of the main feature selection techniques available in scikit-learn.
Evaluating Classification Model Performance in scikit-learn
This article provides an overview of how to evaluate classification model performance in scikit-learn using metrics like accuracy, precision, recall, F1 score, and ROC AUC. It includes code examples and explanations of each metric.
Scikit-learn Crash Course for Data Scientists
This crash course is designed to provide you with a solid foundation in Scikit-learn to start building machine learning models in Python. It introduces key concepts like model evaluation and selection, discuss the major algorithms like regression and classification, and walk through the typical Scikit-learn workflow for developing predictive models.
Handling Imbalanced Datasets in scikit-learn: Techniques and Best Practices
This article provides an overview of techniques like oversampling, undersampling, and adjusting class weights that can be used in scikit-learn to handle imbalanced data and improve model metrics. It also covers best practices like stratification and SMOTE oversampling.
Unsupervised Learning with scikit-learn: An overview
This tutorial demonstrates key unsupervised learning techniques in scikit-learn through code examples, covering dimensionality reduction, clustering algorithms, association rule learning, and anomaly detection. A practical guide to leveraging unsupervised learning to derive insights from unlabeled datasets.