If you’re not a technical person and you are interested in machine learning then you might be wondering whether or not it will be possible for you to do it.
This post will show you whether or not a non-technical person can learn machine learning and how I would recommend that you go about doing it.
So, can a non-technical person learn machine learning? Machine learning makes use of a lot of programming and mathematics. However, even if you are not a technical person, it will be possible for you to learn machine learning if you follow the right path.
Machine learning has become very popular in recent years and this has resulted in many educational resources on machine learning being made. There are even courses available which will teach you how machine learning works even if you have no prior knowledge of programming and college level mathematics. This means that, even as a non-technical person, it will be possible for you to learn machine learning.
Below, I will show you the path that I would recommend that you take to learn machine learning:
- Take the machine learning course taught by Andrew Ng that has no prerequisites
- Learn Python programming with this course
- Learn Data analysis in Python with this Youtube series and these lecture notes
- Take the deep learning course taught by Andrew Ng
- Read this book or watch this YouTube series on how to implement machine learning in Python
- Start applying your knowledge of machine learning on Kaggle competitions
- Learn calculus, linear algebra and probability on edX
- Take this more mathematical course on machine learning offered by MIT
The reason that I recommend that you start with Andrew Ng’s course before you even start learning to program is that it will give you the chance to see if machine learning is something that you would actually be interested in before investing a lot of time into it.
Once you have completed step 6 then you will be able to implement the machine learning models yourself just fine.
However, you will not have a good understanding of the mathematics that goes into the machine learning models to do that it will be necessary for you to learn the mathematics that goes into them. This is why I recommend that you do steps 7 and 8 to get a better understanding of how the machine learning models work mathematically.
How technical machine learning is
Overall, machine learning is quite a technical field since it makes use of programming, statistics and mathematics to apply statistical algorithms to data.
In order to be able to apply the machine learning models, it will be necessary for you to learn how to program in a programming language such as Python.
The machine learning models make use of a lot of mathematics to work. The mathematics that the machine learning models use include linear algebra, calculus and probability theory. However, there are courses that will teach you how the machine learning models work assuming that you do not already know any of the mathematics. I would recommend that you start there and then learn the mathematics of the machine learning more thoroughly after.
If you intend to work with very large datasets or you intend to get a job in machine learning then it will also be necessary for you to learn SQL.
If you intend to get a job in machine learning it would also be necessary for you to learn algorithmic complexity which is focused on analyzing the time that computer programs take to run.
Where to start learning machine learning
If you are interested in learning machine learning then I would recommend that you start by taking this course taught by Andrew Ng from Stanford University. The course does not assume that you have any prior knowledge but it does teach you the linear algebra that is necessary to understand the machine learning algorithms. This is likely the most popular machine learning course since Andrew Ng does a very good job of explaining machine learning and the models in a very intuitive way.
I would recommend that you start there since you will be able to see whether or not machine learning is something that you are actually interested in before spending a lot of time learning the programming and mathematics.
How to learn programming for machine learning
In order to be able to apply the machine learning models, it will be necessary for you to learn how to program.
The programming language that I would recommend the most would be Python. This is because it is the most popular machine learning language, it is relatively easy to learn and it has a number of built-in libraries that allow you to implement the machine learning algorithms easily. To learn Python I would recommend that you take this course taught by MIT on edX.
Once you have learned how to program in Python I would recommend that you learn how to do some basic data analysis in Python. This is because data analysis gets used a lot in machine learning especially when you are preparing the data so that it works well with the machine learning models. To learn data analysis in Python I would recommend that you learn Numpy with these lecture notes and Pandas using this YouTube playlist.
There are many more programming languages that get used in machine learning and one of them is R. This is a programming language that is specifically designed to be used in statistics which is why it tends to be very popular in statistical fields. It has a number of built-in libraries and a reasonably large support community. However, I would recommend that you learn Python over R since most machine learning tutorials are in Python.
You can watch the video below to see more about programming languages in machine learning.
What to do after you have learned to program
After you have learned to program in Python I would recommend that you take this course on deep learning taught by Andrew Ng.
Deep learning is a subset of machine learning which features the algorithms that do very well when they are given a lot of data. They have become very popular in recent years due to the sharp increase in data that businesses have had become available to them.
Next, it would be worthwhile to take a more practical machine learning course that will show you how to implement the different machine learning algorithms in Python. I would recommend this book. However, a free alternative would be to watch this YouTube series.
Now, you’ll be ready to implement the machine learning algorithms yourself on your own datasets. You can do this by coming up with your own datasets to use machine learning on or you can compete in machine learning competitions on Kaggle.
Will it be necessary to learn the mathematics that goes into machine learning?
If you want to learn how the machine learning models work mathematically, how to fully optimize the machine learning models or you want to get a job or research position in machine learning then it will be necessary for you to learn the mathematics.
To learn the mathematics subjects I would recommend the following:
After learning the mathematics for machine learning you can either take a more mathematical machine learning course or you could read a machine learning book. There are many different books that you can get but a popular one is this one which also has a slightly less mathematical version which is this one. The course that I would recommend would be this one which is taught by MIT.
Consider taking a paid machine learning course
There are also paid machine learning courses, that you can take, that will take you from having no knowledge of machine learning to being able to apply machine learning models yourself.
The paid course that I would recommend would be the one offered by Dataquest which will teach you the mathematics that goes into machine learning, how to learn programming for machine learning and how to implement the machine learning models.
You can watch a review of Dataquest in the video below.
If you would like to learn more about how to implement machine learning algorithms, consider taking a look at DataCamp which teaches you data science and how to implement machine learning algorithms.
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