If you are reading this article, you are most likely somebody who wants to learn machine learning but just doesn’t know where to start. You might be thinking about what courses to complete, what books to buy, what Facebook/Meet-Up groups to join, and much more. You may be so overwhelmed with the amount of resources present that you may decide to give-up on learning machine learning. Rest assured, you are not the only one in this position. In fact, I have been in your same position. Don’t give up! Machine learning is a very vast field, but taking the time to learn skills in certain sub-fields of machine learning can pay huge dividends.
Machine learning has been around for quite some time now. It’s concepts were derived in the late 1900s; however, scientists “gave-up” on the field because of the lack of computational power. Only in the last decade or 2, did it pick up some steam. This is primarily due to 2 things: the exponential increases in data exhaust and computational power. The rise of the internet has caused us to “exhaust” more data than before. Everywhere you go, you leave some data. For example, if I go to the coffee shop, I will be exhausting location data via my phone. As you may or will understand, machine learning systems are only as good as the data they are given.
My apologies for going on such a tangent; however, I believe if you are to understand machine learning, you must understand its roots. However, a point I do want to bring up again is the fact that machine learning has been around for a while now. With this in mind, there are an endless number of resources to use to learn machine learning. For the sake of convenience, I have broken down the resources into books/references and courses. However, before I get into the lists, I need to mention 1 must-have resource.
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