Scikit-Learn

Supervised Learning with scikit-learn

Grow your machine learning skills with scikit-learn in Python. Use real-world datasets in this interactive course and learn how to make powerful predictions!

Grow your machine learning skills with scikit-learn in Python. Use real-world datasets in this interactive course and learn how to make powerful predictions!

Machine Learning with Scikit-Learn

Learn how to use scikit-learn, the popular open-source Python library, to build efficient machine learning models.

Learn how to use scikit-learn, the popular open-source Python library, to build efficient machine learning models.

Packt Machine Learning 101 with Scikit-learn and StatsModels

Packt Practical Machine Learning with TensorFlow 2.0 and Scikit-Learn

Packt scikit-learn Recipes

Scaling scikit-learn Solutions

This course covers the important considerations for scikit-learn models in improving prediction latency and throughput; specific feature representation and partial learning techniques, as well as implementations of incremental learning, out-of-core learning, and multicore parallelism.

This course covers the important considerations for scikit-learn models in improving prediction latency and throughput; specific feature representation and partial learning techniques, as well as implementations of incremental learning, out-of-core learning, and multicore parallelism.

Building Neural Networks with scikit-learn

This course covers all the important aspects of support currently available in scikit-learn for the construction and training of neural networks, including the perceptron, MLPClassifier, and MLPRegressor, as well as Restricted Boltzmann Machines.

This course covers all the important aspects of support currently available in scikit-learn for the construction and training of neural networks, including the perceptron, MLPClassifier, and MLPRegressor, as well as Restricted Boltzmann Machines.

Building Classification Models with scikit-learn

This course covers several important techniques used to implement classification in scikit-learn, starting with logistic regression, moving on to Discriminant Analysis, Naive Bayes and the use of Decision Trees, and then even more advanced techniques such as Support Vector Classification and Stochastic Gradient Descent Classification.

This course covers several important techniques used to implement classification in scikit-learn, starting with logistic regression, moving on to Discriminant Analysis, Naive Bayes and the use of Decision Trees, and then even more advanced techniques such as Support Vector Classification and Stochastic Gradient Descent Classification.

Employing Ensemble Methods with scikit-learn

This course covers the theoretical and practical aspects of building ensemble learning solutions in scikit-learn; from random forests built using bagging and pasting to adaptive and gradient boosting and model stacking and hyperparameter tuning.

This course covers the theoretical and practical aspects of building ensemble learning solutions in scikit-learn; from random forests built using bagging and pasting to adaptive and gradient boosting and model stacking and hyperparameter tuning.

Building Your First scikit-learn Solution

This course covers both the why and how of using scikit-learn. You'll delve into scikit-learn’s niche in the ever-growing taxonomy of machine learning libraries, and important aspects of working with scikit-learn estimators and pipelines.

This course covers both the why and how of using scikit-learn. You'll delve into scikit-learn’s niche in the ever-growing taxonomy of machine learning libraries, and important aspects of working with scikit-learn estimators and pipelines.

Building Clustering Models with scikit-learn

This course covers several important techniques used to implement clustering in scikit-learn, including the K-means, mean-shift and DBScan clustering algorithms, as well as the role of hyperparameter tuning, and performing clustering on image data.

This course covers several important techniques used to implement clustering in scikit-learn, including the K-means, mean-shift and DBScan clustering algorithms, as well as the role of hyperparameter tuning, and performing clustering on image data.

Model Evaluation and Selection Using scikit-learn

Review the techniques and metrics used to evaluate how well your machine learning model performs. You will also learn methods to select the best machine learning model from a set of models that you've built.

Review the techniques and metrics used to evaluate how well your machine learning model performs. You will also learn methods to select the best machine learning model from a set of models that you've built.