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Machine Learning DIY

Multi-Core Machine Learning in Python

Many computationally expensive tasks for machine learning can be made parallel by splitting the work across multiple CPU cores, referred to as multi-core processing. Common machine...

How to Hill Climb the Test Set for Machine Learning

Hill climbing the test set is an approach to achieving good or perfect predictions on a machine learning competition without touching the training set or...

Developing a Gradient Boosting Machine Ensemble in Python

The Gradient Boosting Machine is a powerful ensemble machine learning algorithm that uses decision trees. Boosting is a general ensemble technique that involves sequentially adding...

Linear Discriminant Analysis classification in Python

Linear Discriminant Analysis is a linear classification machine learning algorithm. The algorithm involves developing a probabilistic model per class based on the specific distribution of observations...

Developing an AdaBoost Ensemble in Python

Boosting is a class of ensemble machine learning algorithms that involve combining the predictions from many weak learners. A weak learner is a model that...

Developing an Extra Trees Ensemble with Python

Extra Trees is an ensemble machine learning algorithm that combines the predictions from many decision trees. It is related to the widely used random forest...

Developing a Random Forest Ensemble in Python

Random forest is an ensemble machine learning algorithm. It is perhaps the most popular and widely used machine learning algorithm given its good or excellent...

Developing Voting Ensembles With Python

Voting is an ensemble machine learning algorithm. For regression, a voting ensemble involves making a prediction that is the average of multiple other regression models. In...

Scikit-Optimization for Hyperparameter Tuning

  Hyperparameter optimization refers to performing a search in order to discover the set of specific model configuration arguments that result in the best performance...

Basics of TPOT for Automated Machine Learning

Automated Machine Learning (AutoML) refers to techniques for automatically discovering well-performing models for predictive modeling tasks with very little user involvement. TPOT is an open-source...

Combined Algorithm Selection and Hyperparameter Optimization (CASH Optimization)

Machine learning model selection and configuration may be the biggest challenge in applied machine learning. Controlled experiments must be performed in order to discover what...

How to Extracting Wikipedia Data Using Python

Here's the Visual Edition of this Tutorial: https://youtu.be/DVQlxBxp2zc I need to mention that we are not going to web scrape wikipedia pages manually, wikipedia module already did the...
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SwiftUI TabView Introduction and Tab Bar Customization

  The tab bar interface appears in some of the most popular mobile apps such as Facebook, Instagram, and Twitter. A tab bar appears at...

Preparing Data in Machine Learning

Data preparation may be one of the most difficult steps in any machine learning project. The reason is that each dataset is different and highly...

Using blockchain to crack down abusive imagery

Blockchain could be an effective and efficient solution for helping to rid the internet of abusive imagery. Tackling abusive imagery can help victims...

Transforming chatbots throgh AI and ML

The increasing technology has always been a saviour for us. Technology still provides us with solutions for existing problems. One of the answers offered...
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