AI/ML

We will have a detailed look at the development process of an application for getting music by name. To do it we will use the Streamlit framework, which allows you to create beautiful sites using only Python. It is cool, as far you don’t need to be familiar with any other technology or language.

To implement an machine learning algorithms in recommender systems successfully, one needs to understand the structure, mathematical model, and also the strengths and weaknesses. We will provide you the introduction to machine learning with python.

API practices that are presented in the article serve as an effective instrument to avoid a security breach. Though they’re not that exhaustive, they’re widely introduced and used to ensure API safety.

There are a great number of solutions that may facilitate the process of application creation and meet the needs of a developer. Now let’s review some of the most popular Machine Learning APIs

In the 21st century, aka the era of customer-centricity, it’s hard to think of a high-quality software product that does not use personal recommendations. This functionality is actively used for cross-selling and upselling and brings immense benefits to both the company and the customer.

We all know that Machine Learning is awesome in processing big data sets. Based on this data, ML can build accurate forecasts for the future and predict certain events or behavior. But what about assessing one’s skills in real-time?

It’s easy to get confused by all the relatively new terms so we will have a close look at each and will try to answer a question: is there actually a difference between Data Science, Data Analytics, and Big Data.

There are about 4.7 billion mobile users in the world — can you imagine how many mobile apps each one of them uses and how many apps are there in general?