Large language models (LLM) are sophisticated statistical models of natural language applied across very specific implementations, such as machine translation, speech recognition, and text generation.

In many ML interview rounds, candidates are asked to demonstrate their coding skills w.r.t. These kind of rounds helps to identify both coding skills as well as ML skills required to be a top notch ML engineer in some of the top companies. Let's look at some of the common ML coding problems asked in such interviews: Given the API rand7() that generates a uniform random integer in the range [1, 7], write a function rand10() that generates a uniform random integer in the range [1, 10]. You can only call the API rand7(), and you shouldn't call any other API. Please do not use a language's built-in random API.

Reinforcement learning is a Machine Learning paradigm oriented on agents learning to take the best decisions in order to maximize a reward. It is a very popular type of Machine Learning algorithms because some view it as a way to build algorithms that act as close as possible to human beings: choosing the action at every step so that you get the highest reward possible. While in the other article we've explored the technical aspects of Reinforcement Learning, this time we will focus on the more practical aspects of the task. So let's jump right into the code. We will need to install only 2 dependencies for this one.

Feature engineering is an informal topic, but it is considered essential in applied machine learning.