A complete, step by step guide to building a production-grade machine learning app with Django, PostgreSQL, React, Redux and Docker
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Machine Learning
Build a fully production ready machine learning app with Python Django, React, and Docker towardsdatascience.com
Machine Learning from Scratch github.io
This book covers the building blocks of the most common methods in machine learning. This set of methods is like a toolbox for machine learning engineers. Those entering the field of machine learning should feel comfortable with this toolbox so they have the right tool for a variety of tasks. Each chapter in this book corresponds to a single machine learning method or group of methods. In other words, each chapter focuses on a single tool within the ML toolbox.
LendingClub is the world’s largest peer-to-peer lending platform. Until recently (through the end of 2018), LendingClub published a public dataset of all loans issued since the company’s launch in 2007.
This post reviews NumPy main components and functionality, with attention to the needs of Data Science and Machine Learning practitioners, and people who aspire to become a data professional.
Deploy Machine Learning Models with Django deploymachinelearning.com
What it says on the tin.
Turbo-Charging Data Science with AutoML brighttalk.com
Although there are an increasing number of commercial AutoML products, the open-source ecosystem has been innovating here as well. In the early days of the AutoML movement, the focus was on those looking to leverage the power of ML models without a background in data science – citizen data scientists. Today, however, AutoML tools have a lot to offer experts too.
Loominus co-founder Hung Luu shows you how to train, compare and deploy a logistic regression model in eight minutes using Learner and Modops.
One of the most common mistakes data scientists make when training machine learning models is incorrectly splitting data for training and testing. The train/test split involves splitting data during the model training and evaluation process.
Learner makes this simple with a single parameter selection during the model building process. It’s also simple to set the percentage split between training and testing data for each model trained.
Image Classification with Transfer Learning and PyTorch stackabuse.com
Transfer learning is a powerful technique for training deep neural networks that allows one to take knowledge learned about one deep learning problem and apply it to a different, yet similar learning problem.
Using transfer learning can dramatically speed up the rate of deployment for an app you are designing, making both the training and implementation of your deep neural network simpler and easier.
Keras Learning Rate Finder pyimagesearch.com
In this tutorial, you will learn how to automatically find learning rates using Keras. This guide provides a Keras implementation of fast.ai’s popular “lr_find” method.
Searching for Pulsars with Machine Learning medium.com
Searching for pulsars is a labor-intensive process that requires experienced astronomers and trained volunteers for their classification. In this article, we implement machine learning techniques to facilitate the process.
Data pipelines are where most of the time is spent for those working with data because the bulk of a machine learning project involves data collection and cleaning. Loominus gives everyone the power to build the data pipelines critical to any machine learning project.
Teraport is a powerful tool within the Loominus product suite that ingests and stages data. In another post, we’ll discuss the data ingestion APIs. For now we’ll focus on building a powerful data pipeline for feature engineering.
Python for NLP: Introduction to the Pattern Library stackabuse.com
The Pattern library is a multipurpose library capable of handling the following tasks:
- Natural Language Processing: Performing tasks such as tokenization, stemming, POS tagging, sentiment analysis, etc.
- Data Mining: It contains APIs to mine data from sites like Twitter, Facebook, Wikipedia, etc.
- Machine Learning: Contains machine learning models such as SVM, KNN, and perceptron, which can be used for classification, regression, and clustering tasks.
In this article, we will see the first two applications of the Pattern library from the above list. We will explore the use of the Pattern Library for NLP by performing tasks such as tokenization, stemming and sentiment analysis. We will also see how the Pattern library can be used for web mining.
End-To-End Topic Modeling in Python towardsdatascience.com
Topic Model: In a nutshell, it is a type of statistical model used for tagging abstract “topics” that occur in a collection of documents that best represents the information in them.
Many techniques are used to obtain topic models. This post aims to demonstrate the implementation of LDA: a widely used topic modeling technique.
