AlgoExpert – Machine Learning Coding Questions 2022-3 – Download

Description

In the Machine Learning Coding Questions course, you’ll practice what you need to succeed in machine learning coding interviews. There are currently 10 ML coding questions. But don’t let that small number fool you. These are not your typical coding questions. You will spend a lot of time working on these tantalizing treasures. Some of the questions are: K nearest neighbors, classifying trucks, predicting cancellations, sparse matrix multiplication and… Question categories include mathematical and model concepts, model applications and data processing.

Within each coding question you will find an integrated 3 and 4 part workspace. Simply put, an integrated workspace means you have all the tools you need to address and resolve issues. The MLExpert workspace includes windows such as: prompt, notebook, solutions, video explanations. You will also find tips, an area to write your solutions and another area where you publish your code to see the result in real time. You’ll then learn about features like font size selection, light/dark mode, and syntax highlighting. You can even choose which text editor you want to use (Sublime, Emacs or Vim).

The Machine Learning Coding Questions course is suitable for people who

  • Those preparing for machine learning interviews
  • Experienced ML professionals who want to deepen their skills
  • A professional without ML training who wants to master ML

Course Details

Editor: AlgoExpert

English language

Training level: introduction to advanced

Number of lessons: 10

Training duration: 2 hours and 16 minutes

Course titles

AlgoExpert – Machine Learning Coding Questions

Preconditions

Some questions include:

  • K Nearest neighbors
  • Classified trucks
  • Predict cancellations
  • Sparse matrix multiplication

Pictures

Machine Learning Coding Questions

Example video

installation guide

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Quality: 1080p

Download link

Download the course – 563 MB

password file(s): free download software

size

563 MB

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