Basic Concepts in Machine Learning

What are the basic concepts in machine learning?

I found that the best way to discover and get a handle on the basic concepts in machine learning is to review the introduction chapters to machine learning textbooks and to watch the videos from the first model in online courses.

Pedro Domingos is a lecturer and professor on machine learning at the University of Washing and author of a new book titled “The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World“.

Domingos has a free course on machine learning online at courser titled appropriately “Machine Learning“. The videos for each module can be previewed on Coursera any time.

In this post you will discover the basic concepts of machine learning summarized from Week One of Domingos’ Machine Learning course.

Machine Learning

The first half of the lecture is on the general topic of machine learning.

What is Machine Learning?

Why do we need to care about machine learning?

A breakthrough in machine learning would be worth ten Microsofts.
— Bill Gates, Former Chairman, Microsoft

Machine Learning is getting computers to program themselves. If programming is automation, then machine learning is automating the process of automation.

Writing software is the bottleneck, we don’t have enough good developers. Let the data do the work instead of people. Machine learning is the way to make programming scalable.

  • Traditional Programming: Data and program is run on the computer to produce the output.
  • Machine Learning: Data and output is run on the computer to create a program. This program can be used in traditional programming.

Machine learning is like farming or gardening. Seeds is the algorithms, nutrients is the data, the gardner is you and plants is the programs.

Applications of Machine Learning

Sample applications of machine learning:

  • Web search: ranking page based on what you are most likely to click on.
  • Computational biology: rational design drugs in the computer based on past experiments.
  • Finance: decide who to send what credit card offers to. Evaluation of risk on credit offers. How to decide where to invest money.
  • E-commerce:  Predicting customer churn. Whether or not a transaction is fraudulent.
  • Space exploration: space probes and radio astronomy.
  • Robotics: how to handle uncertainty in new environments. Autonomous. Self-driving car.
  • Information extraction: Ask questions over databases across the web.
  • Social networks: Data on relationships and preferences. Machine learning to extract value from data.
  • Debugging: Use in computer science problems like debugging. Labor intensive process. Could suggest where the bug could be.

What is your domain of interest and how could you use machine learning in that domain?


Jason Brownlee PhD – I help developers get results with machine learning.


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