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Programming


With voice-controlled touchpoints becoming more and more the norm in human-computer interactions, our Speech-to-Text (STT) API is a great option for developers looking to build voice into their applications. The API processes over 1 billion spoken minutes of speech each month, enough to transcribe all Presidential inauguration speeches in U.S. history over 1 million times. Our …

In the United States, Tax Season descends upon the country every April, requiring millions of Americans to spend hours deciphering cryptic documents and performing complex math just to figure out what they owe. Wouldn’t it be grand if there was a way for a computer to take all the relevant documents and extract out exactly …

Bringing AI models to a production environment is one of the biggest challenges of AI practitioners. Much of the discussions in the AI/ML space revolve around model development. As shown in this diagram from the canonical Google paper “Hidden Technical Debt in Machine Learning Systems”, the bulk of activities, time and expense in building and …

At enterprises across industries, documents are at the center of core business processes. Documents store a treasure trove of valuable information whether it’s a company’s invoices, HR documents, tax forms and much more. However, the unstructured nature of documents make them difficult to work with as a data source. We call this “dark data” or unstructured data …

Explainable AI (XAI) helps you understand and interpret how your machine learning models make decisions. We’re excited to announce that BigQuery Explainable AI is now generally available (GA). BigQuery is the data warehouse that supports explainable AI in a most comprehensive way w.r.t both XAI methodology and model types. It does this at BigQuery scale, enabling millions of …

In this tutorial, you’ll learn about imbalanced data and how to handle them in machine learning classification in Python. Imbalanced data occurs when the classes of the dataset are distributed unequally. It is common for machine learning classification prediction problems. An extreme example could be when 99.9% of your data set is class A (majority class). At the same …

In this tutorial, we’ll explain the random forest algorithm in machine learning. Random forests are powerful, popular, and easy to use algorithms for predictive modeling. As the name suggests, the model is an ensemble of many decision trees, with better performance than an individual tree alone. The algorithm can be used for both supervised classification and regression problems. Following …

Machine learning is famous for its ability to analyze large data sets and identify patterns. It is basically a subset of artificial intelligence. Machine learning uses algorithms that leverages previous data-sets and statistical analysis to make assumptions and pass on judgments about behavior. The best part, software or computers powered by machine learning algorithms can …

Opening up the “black box” helps remove uncertainties about AI outcomes, providing insight into the modeling process and identifying biases and errors. Artificial intelligence (AI) is being used more frequently in our daily lives, with systems such as Siri and Alexa becoming commonplace in many households. Many households themselves are “smart,” powered by devices that …

What does it mean for a machine to learn? In a way, machines learn just like humans. They infer patterns from data through a combination of experience and instruction. In this article, we will give you a sense of the applications for machine learning and explain why Python is a perfect choice for getting started. …