SOURCE : https://machinelearningmastery.com/think-machine-learning
By: Jason Brownlee
Machine learning is a large and interdisciplinary field of study.
You can achieve impressive results with machine learning and find solutions to very challenging problems. But this is only a small corner of the broader field of machine learning often called predictive modeling or predictive analytics.
In this post, you will discover how to change the way you think about machine learning in order to best serve you as a machine learning practitioner.
After reading this post, you will know:
What machine learning is and how it relates to artificial intelligence and statistics.
The corner of machine learning that you should focus on.
How to think about your problem and the machine learning solution to your problem.
Let’s get started.
1Photo by Rajarshi MITRA, some rights reserved.
Overview
This post is divided into 3 parts; they are:
1. You’re Confused
2. What is Machine Learning?
3. Your Machine Learning
You’re Confused
You have a machine learning problem to solve, but you’re confused about what exactly machine learning is.
There’s good reason to be confused. It is confusing to beginners.
Machine learning is a large field of study, and not all much of it is going to be relevant to you if you’re focused on solving a problem.
In this post, I hope to clear things up for you.
We will start off by describing machine learning in the broadest terms and how it relates to other fields of study like statistics and artificial intelligence.
After that, we will zoom in on the aspects of machine learning that you really need to know about for practical engineering and problem solving.
What is Machine Learning?
Machine learning is a field of computer science concerned with programs that learn.
“The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience.”
— Machine Learning (http://amzn.to/2svq4dV), 1997.
That is super broad.
There are many types of learning, many types of feedback to learn from, and many things that can be learned.
This could encompass diverse types of learning, such as:
• Developing code to investigate how populations of organisms “learn” to adapt to their environment over evolutionary time.
• Developing code to investigate how one neuron in the brain “learns” in response to stimulus from other neurons.
• Developing code to investigate how ants “learn” the optimal path from their home to their food source.
I give these esoteric examples on purpose to help you really nail down that machine learning is a broad and far reaching program of research.
Another case that you may be more familiar with is:
• Developing code to investigate how to “learn” patterns in historical data.
This is less glamorous, but is the basis of the small corner of machine learning in which we as practitioners are deeply interested.
This corner is not distinct from the other examples; there can be a lot of overlap in methods for learning, fundamental tasks, ways of evaluating learning, and so on.
What About Artificial Intelligence?
Machine learning is a subfield of artificial intelligence.
It overlaps with machine learning.
Artificial intelligence is also an area of computer science, but it is concerned with developing programs that are intelligent, or can-do intelligent things.
Intelligence involves learning, e.g. machine learning, but may involve other concerns such as reasoning, planning, memory, and much more.
This could encompass diverse types of learning such as:
• Developing code to investigate how to optimally plan logistics.
• Developing code to investigate how to reason about a paragraph of text.
• Developing code to investigate how to perceive the contents of a photograph.
Artificial intelligence is often framed in the context of an agent in an environment with the intent to address some problem, but this does not have to be the case.
Machine learning could just as easily be named artificial learning to remain consistent with artificial intelligence and help out beginners.
The lines are blurry. Machine learning problems are also artificial intelligence problems.
What About Statistics?
Statistics, or applied statistics with computers, is a sub-field of mathematics that is concerned with describing and understanding the relationships in data.
This could encompass diverse types of learning such as:
• Developing models to summarize the distribution of a variable.
• Developing models to best characterize the relationship between two variables.
• Developing models to test the similarity between two populations of observations.
It also overlaps with the corner of machine learning interested in learning patterns in data.
Many methods used for understanding data in statistics can be used in machine learning to learn patterns in data. These tasks could be called machine learning or applied statistics.
