If you are looking to learn machine learning then you might be wondering whether or not it is difficult to learn.
This post will show you what goes into learning machine learning and how you can go about learning it.
So, is machine learning difficult to learn? Machine learning requires the knowledge of calculus, linear algebra, probability, statistics and programming. It is not something that can be easily learned overnight but there are many MOOCs available that will guide you through the process.
There are actually a number of different disciplines that machine learning makes use of but the truth is that anyone can learn machine learning if they are willing to put in the time and effort in doing so.
Why people find it difficult to learn machine learning
People will often start trying to learn machine learning and quickly get discouraged when they realize that there is actually a lot of prerequisite knowledge that is required.
Machine learning makes use of a number of different subjects including mathematics, statistics, probability, and programming. If you don’t have a good understanding of these subjects when you start learning machine learning then it will be difficult to follow what is going on. This is because the material will assume that you have an understanding of these topics.
Despite that, if you take the time beforehand to get a good understanding of the prerequisites then it will make learning machine learning much easier and not too difficult.
Additionally, there are some machine learning courses that don’t assume that you have any of the prerequisite knowledge.
One of the most popular courses on machine learning which is taught by Andrew Ng doesn’t assume any prerequisite knowledge from you. It teaches you the theory that goes into the different machine learning algorithms. If you are looking to learn machine learning then I would recommend that you start there.
How long will it take to learn machine learning?
The machine learning course by Andrew Ng is estimated to take 55 hours to complete according to their website.
However, a big part of making use of the different machine learning models will involve preprocessing and analyzing data. This is so that the machine learning models will work with the data and so that you know which model is appropriate.
The Andrew Ng course doesn’t go into much detail there so it will also be necessary for you to learn a programming language and how to do data analysis in that language.
The programming language that I would recommend for machine learning would be Python and the free course that I would recommend to learn Python with would be this one. It takes 9 weeks to complete and you should expect to spend 15 hours a week on the course. After that, I would recommend this youtube series to learn data analysis in Python.
Having said that, the amount of time that it will take you to learn machine learning will depend largely on what you want to do with it and your background.
If you already have a good understanding of programming, probability, statistics and mathematics then it will just be necessary for you to learn the different machine learning algorithms and how to use them. In this case, it would just be necessary for you to take a machine learning course or to read a machine learning book and then to start working on machine learning projects yourself.
However, if you don’t know those subjects then it will likely take longer for you since you’ll be needing to learn them as well.
How to learn machine learning
There are a number of different ways that you can go about learning machine learning. My advice would be to start by learning from a machine learning course that doesn’t have prerequisites such as Andrew Ng’s course and to learn to program in Python using this course while you are taking it.
After that, I would suggest that you learn how to do data analysis in Python using this playlist. Then I would suggest that you learn from a practical machine learning course or book such as Hands on machine learning.
Now, I would say that the best thing for you to do would be to apply your knowledge on your own projects and to do some of the competitions on Kaggle.
Once you have a good understanding of probability, linear algebra and calculus then I would recommend that you take a look at Machine learning with Python by MIT. The course is a 15-week course and you should expect to spend 10-14 hours each week on it. The course will give you a much more detailed understanding of the mathematics behind the different machine learning and deep learning algorithms.
Consider the way that you learn best
There are a wide variety of different machine learning resources that you can use to learn from and it would be helpful for you to consider the way that you learn best.
If you learn best from books then there are a number of different books that you can get on machine learning, programming, data analysis and mathematics. The advantages of using books are that you’ll be able to go at your own pace, they are yours to keep and they will normally go into a lot of detail. However, some of the code in books related to programming will not be up to date unless the book was released recently.
There are also many online courses such as the ones that are referenced above that you can use to learn machine learning. The online courses tend to be very well structured, interactive and will allow you to learn from some of the worlds top universities without actually going there.
Apply the models to real world problems
Once you have learned the theory behind machine learning and how to implement the different machine learning algorithms it will be very useful for you to start doing projects of your own.
One of the best ways to learn is by doing active learning where you are having to figure out and remember things yourself and many people say that they learned machine learning best when they started to tackle their own machine learning projects.
A good way to find datasets that you can use to practice machine learning with is to use the website Kaggle.com. On there you will be able to find hundreds of different datasets relating to many different fields that you can use yourself. It also features machine learning competitions, with prize money, where people compete against each other to get the best score by applying machine learning algorithms to certain datasets.
When learning machine learning it is important to take a longer-term approach and not to expect to learn it overnight without any challenges. Unless you have a strong background in all of the prerequisites, which most people do not, then you’ll likely bump into some hurdles on the way. However, if you stick with it then you’ll come out with a very rewarding and useful skill that is currently highly sought after that is in a field that is constantly making new discoveries.