For data scientists, checking correlations is an important part of the exploratory data analysis process. This analysis is one of the methods used to decide which features affect the target variable the most, and in turn, get used in predicting this target variable. In other words, it’s a commonly-used method for feature selection in machine learning.
Continue reading Seaborn Heatmaps: 13 Ways to Customize Correlation Matrix Visualizations
In these unprecedented times involving a worldwide pandemic, many changes have taken place in our daily lives. To ensure the safety and well being of all, many protocols and guidelines have been implemented across society, such as maintaining a minimum distance or limiting physical contact with exposed and shared surfaces.
Businesses are getting creative by adapting their shopping experiences to ensure the safety of their customers and employees. Contactless services have become more of a necessity than a fad, and technology has evolved to keep pace with these newfound demands. Many stores have standardized Scan & Go technology, which allows customers to scan item barcodes and pay using just their smartphones, thus limiting human contact and providing a faster, simpler alternative.
Continue reading Safer and Smarter: Contactless shopping with on-device object detection
At WWDC 2020, Apple introduced a bunch of great new updates to SwiftUI to make it even easier for developers to write apps for Apple platforms. In this tutorial, you’ll learn how to use those new features to make your app work on both iOS and macOS. By the end of this tutorial you will have created a fully functional HackerNews reader.
If you’re tired of building and running your iOS apps via Xcode’s ▶️ button, let’s explore an exciting way to build and run your apps without touching the ▶️ button (ever again).
This is a four-step process.
Xcode comes with a number of command line tools. These tools are capable of performing pretty much every thing you can do via Xcode’s UI. While you need a human to point and click certain buttons in Xcode to make it work, these command line tools can help automate the whole process of building and running your project. Powerful, right?
The ability of a machine or program to identify spoken words and transcribe them to readable text is called speech recognition (speech-to-text). In this tutorial, I will be walking you through analyzing speech data and converting them to a useful text for sentiment analysis using Pydub and SpeechRecognition library in Python.
Sentiment analysis is the use of natural language to classify the opinion of people. It helps to classify words (written or spoken) into positive, negative, or neutral depending on the use case. The sentiment analyzed can help identify the pattern of a product; it helps to know what the users are saying and take the necessary steps to mitigate any problems.
Continue reading Sentiment Analysis of Speech Using PyDub and SpeechRecognition in Python
Machine learning (ML) is arguably the most sought after skill in the fields of data and computer science. ML related projects and problems can be done using any programming language.
Developers have been led to believe that, to build and train an ML model, they are restricted to using a select few programming languages— Python, R and Java often top the list.
If our data isn’t good enough, there’s no machine learning tool, platform, or framework that exists that will work well—no matter how good the algorithm is.
So while debugging machine learning models, we also need to make sure our input data is prepared properly. For example the input data may not be a valid data type for a particular feature. Like in case of gender the allowed values are M or F, while the input data may contain other letter values for this feature.
For years now, Snapchat has been at the forefront of mobile machine learning — their popular Lenses, which often combine on-device ML models with augmented reality, have become shining examples of the power and flexibility of on-device machine learning.
Given our respect and admiration for Snap’s work in this area, our team was thrilled to hear about the recent release of SnapML, Snap’s new ML framework inside their development platform Lens Studio (released with 3.0).
Last year, at I/O 2018, Google announced a brand new SDK available for developers: ML Kit. It’s no surprise that Google’s advances in machine learning are miles ahead of what any other company is aiming for. Through this SDK, Google was hoping to help mobile developers bring machine learning to their apps with simple, concise code. As part of the Firebase ecosystem, ML Kit allows developers to implement ML functionality with just a few lines of code; everything from vision to natural language to custom models.
Continue reading Exploring the new ML Kit features on iOS using Swift
One thing I’ve observed in many data science tutorials when it comes to modeling, is that once a certain performance threshold is achieved on test data, rarely is the model deployed/pushed to production—and it’s a common case in the industry more broadly.
This tutorial aims to take modeling a step further by building a REST API and deploying the model into production. In addition to the REST API, we’re building a simple web application that predicts whether a piece of text belongs to any of these classes: atheism, computer graphics, medical science, Christianity, or politics.
Continue reading Deploying and Hosting a Machine Learning Model Using Flask, Heroku and Gunicorn
