Machine Learning

Use Cases

Machine learning (ML) is a subset of artificial intelligence (AI). It is great for making predictions based on historical data. [1]


  • Image recognition

    • Identifying objects in an image.

  • Natural language processing (NLP)

    • Understanding written or vocal speech.

  • Recommendation engines

    • Predicting what similar products a user might like.


Machine learning does not always require having a deep knowledge of math. When it is needed, these are the most relevant mathematical subjects a machine learning expert should be familiar with [2][3]:

  • Linear algebra

  • Statistics

  • Differential calculus

  • Integral calculus

Programming Languages

More than half of all machine learning programs are built using Python. [4] Here are the top 3 programming languages used [4][5]:

  1. Python

  2. R

  3. Java

Graphics Card Vendors

NVIDIA provides the best support for machine learning with its proprietary CUDA library. It is possible to use AMD and Intel graphics cards by using the open source OpenCL library [7] but NVIDIA provides the best performance and compatibility. [6]


  1. “Classification, regression, and prediction - what’s the difference?” Towards Data Science. December 11, 2020. Accessed November 7, 2022.

  2. “A beginner’s guide to the math that powers machine learning.” TNW The heart of tech. October 2, 2022. Accessed November 7, 2022.

  3. “Math for Machine Learning: 14 Must-Read Books.” Machine Learning Techniques. June 13, 2022. Accessed November 7, 2022.

  4. “What is the best programming language for Machine Learning?” Towards Data Science. May 5, 2017. Accessed November 7, 2022.

  5. “7 Top Machine Learning Programming Languages.” Codeacademy. October 20, 2021. Accessed November 7, 2022.

  6. “How to Pick the Best Graphics Card for Machine Learning.” Towards Data Science. September 19, 2022. Accessed November 7, 2022.

  7. “Does TensorFlow Support OpenCL?” IndianTechWarrior. Accessed November 7, 2022.