Machine learning is one of the most popular topics today. It is also not beginner-friendly, rather the opposite. It is one of those subjects that are hard to start with. This article will give you a roadmap that will help you start with machine learning the easy way. Use the following steps and start learning machine learning today. This may sound as a weird advice since machine learning is based on mathematics and statistics. However, it is the mathematical and statistical side of machine learning that is often the hardest for beginners to swallow. It is not so hard to imagine a guy or girl interested in machine learning. With the current traction of this subject this group is gaining new members every day.

These are two excellent books on machine learning (AKA, statistical learning; AKA, model building). If we're talking about entry level data scientists to intermediate level data scientists, I'd estimate that they spend less than 5% of their time actually doing mathematics. Even if you use "off the shelf" tools like R's caret and Python's scikit-learn – tools that do much of the hard math for you – you won't be able to make these tools work without a solid understanding of exploratory data analysis and data visualization. While this figure is about data science in general, it also applies to machine learning specifically: when you're building machine learning models, 80% of your time will be spent getting data, exploring it, cleaning it, and analyzing results (using data visualization).

When beginners get started with machine learning, the inevitable question is "what are the prerequisites? What do I need to know to get started?" You need to master math. A list like this is enough to intimidate anyone but a person with an advanced math degree. It's unfortunate, because I think a lot of beginners lose heart and are scared away by this advice.

When beginners get started with machine learning, the inevitable question is "what are the prerequisites? What do I need to know to get started?" A list like this is enough to intimidate anyone but a person with an advanced math degree. It's unfortunate, because I think a lot of beginners lose heart and are scared away by this advice. If you're intimidated by the math, I have some good news for you: in order to get started building machine learning models (as opposed to doing machine learning theory), you need less math background than you think (and almost certainly less math than you've been told that you need).