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This post is a part of a series about feature engineering techniques for machine learning with Python.

You can check out the rest of the articles:

Welcome to the last of this series on feature engineering! In today’s article, we’ll explore some advanced feature engineering techniques across different tasks. Specifically, we’ll look at advanced categorical encoding, advanced outlier detection, automated feature engineering and more.

Continue reading Hands-on with Feature Engineering Techniques: Advanced Methods

At I/O 2018, Google announced the release of Firebase’s ML Kit, a developer-friendly software package that allows mobile engineers to quickly integrate Machine Learning features in their applications with just a few lines of code. With ML Kit, we’re able to do amazing things like face detection, text recognition, and landmark recognition, all without needing to have deep knowledge about neural networks or model optimization.

Continue reading Creating an Android app with Snapchat-style filters in 7 steps using Firebase’s ML Kit

Quick answer:

In a nutshell and much more simple words that may not be as comprehensive, AI is the study of automating intelligent decisions.

In order to train and test the methodologies scientists come up with. They need some sort of a playground to experiment in. A playground is an environment that is inspired by reality and agents are the decision-makers.

Continue reading What is AI: Everything You Need to Know

In this post we’re going to embark on a journey to explore and dive deep into the world of optimizers for machine learning models. We will also try and understand the foundational mathematics behind these functions and discuss their use cases, merits, and demerits.

So, what are we waiting for? Let’s get started!

Continue reading Exploring Optimizers in Machine Learning

In this tutorial we’ll cover XGBoost, a machine learning algorithm that has dominated the applied machine learning space recently.

XGBoost is an open source library that provides gradient boosting for Python, Java and C++, R and Julia. In this tutorial, our focus will be on Python. Gradient Boosting is a machine learning technique for classification and regression problems that produces a prediction from an ensemble of weak decision trees.

Continue reading Boosting your Machine Learning Models Using XGBoost

Push notifications—being one of the most integral parts of a mobile application—should be one of the first things you configure while building your mobile app. This blog will help you get you familiar with the fundamentals of setting up push notifications in your Xamarin.Android project using Firebase.

Firebase Cloud Messaging (FCM) is a cross-platform service that handles the sending, routing and queueing of messages between server applications and mobile apps.

Continue reading Firebase Cloud Messaging for Remote Push Notifications on Android with Xamarin

Custom Vision is a platform that allows you to build your own customized image classifiers. If you want to develop a mobile app where you need to classify images, Custom Vision (by Microsoft) offers you one of the fastest ways to go about it.

With Custom Vision, you simply upload your labeled images, train the model on the platform, and then export a Core ML model (for iOS) or TensorFlow model for Android (and even ONNX for Windows ML and DockerFile for AzureML). Yes, you heard that correctly. And it is free for 2 projects, and up to 5000 training images per project.

Continue reading Build Your Own Customized Image Classification Mobile App in 10 Minutes

Creating accurate machine learning models capable of identifying multiple objects in a single image remains a core challenge in computer vision, but now with the advancement of deep learning and computer vision models for mobile, you can easily detect target objects from an image on Android.

In this article, we’ll do just that with the help of Firebase ML Kit’s Object Detection API.

Continue reading Object Detection in Android Using Firebase ML Kit

In the first part of this series, I introduced deep learning in JavaScript—we explored why you should consider using Javascript for deep learning, and then went on to create a neural network to predict areas acutely affected by forest fires.

As you might have noticed, if you read part one or are otherwise familiar with TF.js, both training and inference happened directly in the browser. While training in the browser can be fast and effective for small datasets, it quickly becomes intractable as the data scales. This is mostly because the amount of storage assigned to a browser is minimal.

Continue reading Deep Learning in JavaScript (Part 2)

Supporting deep learning inference on mobile and edge devices has gained popularity more than ever and we have a greater number of options to choose from when carrying out AI-related development tasks on our little companions than we could have guessed.

Not only is implementing machine learning models—the standard for tasks such as computer vision—faster and easier on mobile devices these days, but the renewed competition between the developers of frameworks supporting them also seems to have ensured that the process itself reaches new heights in terms of performance, flexibility and adaptability.

Continue reading Alibaba’s Mobile Neural Network: A deep learning framework for mobile and embedded devices