Also, no one can deny that Linear Algebra is undoubtedly the important and primary thing to process the applications of Machine Learning. The term Linear Algebra was initially introduced in the early 18 th century to find out the unknowns in Linear equations and solve the equation easily hence it is an important branch of mathematics that helps study data. Linear Algebra is an essential field of mathematics, which defines the study of vectors, matrices, planes, mapping, and lines required for linear transformation. Each machine learning algorithm is based on the concepts of mathematics & also with the help of mathematics, one can choose the correct algorithm by considering training time, complexity, number of features, etc.
Machine learning has a strong connection with mathematics. Hope you find this quick read useful.Next → ← prev Linear Algebra for Machine learning These are my recommendations for someone who wants to get into this fantastic and futuristic world known as, machine learning. Scikit-Learn, for machine learning pre-defined algorithms and functionsįor learning the ins and outs of all the libraries (packages) listed above, I will recommend you to subscribe, at least for a month, to DataCamp (In my opinion one of the best learning sources out there for those who want to become Data Scientists.Pandas, for data manipulation and data organization.
Once you get the grasp of programming in Python, I will move quickly into learning the following libraries (packages): Also, you can try the Codecademy Python interactive module, for daily practice. This is a great resource for learning Python. I fully recommend a Udemy course called "Complete Python Bootcamp" (). If you already know another programming language, don't worry about it, really, like I said above, machine learning is all about math.
Here is why: it is free, there are hundreds of supporting communities (and still growing) out there, and it is very versatile due to a large number of free libraries (packages) available online, that let you do almost anything. If you ask me, I would choose Python by default, not because it is the programming language I already know, but because if you are new to machine learning and new to programming, Python will be one of the best choices for this purpose. (Once you refresh all these concepts, and theorems, it should be time to decide what programming language to learn, if you don't know one already. The best (by far) free resource online out there, without a doubt, is Khan Academy. For this reason, the most obvious first step on this journey should be to re-visit and practice basic concepts on linear algebra, probability, statistics, and calculus. Machine learning is all about math, more than it is to know how to program, which is, mistakenly, what most people think about when they hear these two words.
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