Tag

machine

Deploying Machine Learning Models, Part 4: creating a Docker image

From Machine Learning Bookcamp by Alexey Grigorev

In this series, we cover model deployment: the process of putting models to use. In particular, we’ll see how to package a model inside a web service, allowing other services to use it. We also show how to deploy the web service to a production-ready environment.

Deploying Machine Learning Models, Part 3: managing dependencies

From Machine Learning Bookcamp by Alexey Grigorev In this series, we cover model deployment: the process of putting models to use. In particular, we’ll see how to package a model inside a web service, allowing other services to use it…. Continue Reading →

Deploying Machine Learning Models, Part 2: model serving

From Machine Learning Bookcamp by Alexey Grigorev

In this series, we cover model deployment: the process of putting models to use. In particular, we’ll see how to package a model inside a web service, allowing other services to use it. We also show how to deploy the web service to a production-ready environment.

Deploying Machine Learning Models, Part 1: saving models

From Machine Learning Bookcamp by Alexey Grigorev

In this series, we cover model deployment: the process of putting models to use. In particular, we’ll see how to package a model inside a web service, allowing other services to use it. We also show how to deploy the web service to a production-ready environment.

Sentiment Classification Using a Large Movie Review Dataset, Part 2

From Machine Learning with TensorFlow, Second Edition by Chris Mattmann This article covers: Building sentiment classifier using logistic regression and with softmax Measuring classification accuracy Computing ROC curve and measure classifier effectiveness Submitting your results to the Kaggle challenge for… Continue Reading →

Applying VACUUM to Data

From Cloud Native Machine Learning by Carl Osipov

The goal of this article is to teach you the data quality criteria you should use across any machine learning project, regardless of the dataset. This means that this part deals primarily with concepts rather than code.

Sentiment Classification Using a Large Movie Review Dataset, Part 1

From Machine Learning with TensorFlow, Second Edition by Chris Mattmann

This article covers using text and word frequency (Bag of Words) to represent sentiment.

Case Study: Breast Cancer Diagnosis

From Ensemble Methods for Machine Learning by Gautam Kunapuli

Our first case study explores a medical decision-making task: breast cancer diagnosis. We will see how to use scikit-learn’s homogeneous parallel ensemble modules in practice. Specifically, we will train and evaluate the performance of three homogeneous parallel algorithms, each characterized by increasing randomness: bagging with decision trees, random forests and ExtraTrees.

Object-Oriented Coding in Python

From The Well-Grounded Python Developer by Doug Farrell

This article delves into using the OOP coding paradigm in the Python language.

The path to becoming a Pythonista

From The Well-Grounded Python Developer by Doug Farrell

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