Machine Learning Fundamentals

• How to Measure the Performance of a Machine Learning Classifier with Python and Scikit-Learn?Using confusion matrix and error metrics for measuring classification performance in machine learning with Python.
• Measuring Regression Errors with PythonThis tutorial presents six regression error metrics to measure model performance and shows how to implement them with Python and Scikit-learn
• Tuning Model Hyperparameters with Grid Search at the Example of Training a Random Forest Classifier in PythonLearn how to tune the model hyperparameters of a Random Forest that predicts the survival of Titanic passengers using grid search in Python.
• Using Random Search to Tune the Hyperparameters of a Random Decision Forest with PythonLearn how to use Random Search to tune the model hyperparameters of a Random Forest with Python that predicts house sale prices.
• Feature Engineering and Selection for Regression Models with Python and Scikit-learnThis guide provides tips on feature exploration, engineering, and selection for machine learning using Python and Scikit-Learn
• Mastering Multivariate Stock Market Prediction with Python: A Guide to Effective Feature Engineering TechniquesFeature engineering for multivariate time series models using the example of stock market forecasting with Python and Keras Neural Networks.
• Cluster Analysis with k-Means in PythonThis tutorial presents k-mean clustering and how to perform a cluster analysis on synthetic data with Python and Scikit-Learn.
• How to Use Hierarchical Clustering For Customer Segmentation in PythonIn this tutorial, we will use Python and the scikit-learn library to apply hierarchical clustering to a dataset of customer data.
• Multivariate Anomaly Detection on Time-Series Data in Python: Using Isolation Forests to Detect Credit Card FraudThis article describes multivariate anomaly detection in the example of credit card fraud using Random Isolation Forests in Python
• Training a Sentiment Classifier with Naive Bayes and Logistic Regression in PythonThis article deals with sentiment analysis and shows how to build a sentiment classifier using logistic regression and naive Bayes in Python.
• What is Naive Bayes?The basic idea behind naive Bayes is that we can use the probabilities of each feature belonging to a particular class to predict the likelihood that a…
• Image Classification with Convolutional Neural Networks - Classifying Cats and Dogs in PythonLearn about image classification with deep learning and develop a convolutional neural network that distinguishes between cats and dogs!
• Using Fairlearn to Build Fair Machine Machine Learning Models with Python: Step-by-Step Towards More Responsible AIMove towards responsible AI by using FairLearn, an open-source Python package for assessing and mitigating unfairness in machine learning.
• Rolling Time Series Forecasting: Creating a Multi-Step Prediction for a Rising Sine Curve using Neural Networks in PythonThis article shows how to create a rolling multi-step forecast for a rising sine curve using Keras neural networks with lstm layers in Python
• Univariate Stock Market Forecasting using Facebook Prophet in PythonThis tutorial gives an overview of Facebook Prophet and shows how to use the framework in Python to create a univariate time series forecast.
• Forecasting Beer Sales with ARIMA in PythonThis Python tutorial shows how to use Auto-ARIMA for time series forecasting using the example of forecasting beer sales.
• Stock Market Prediction using Univariate Recurrent Neural Networks (RNN) with PythonThis article shows how to train a univariate neural network model for stock market forecasting with Python and Scikit-learn.

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