*These algorithms are called machine learning algorithms and there are literally hundreds of them.*Covering how much math is needed for every type of algorithm in depth is not within the scope of this post, I will discuss how much math you need to know for each of the following commonly-used algorithms: What they are: Naïve Bayes’ classifiers are a family of algorithms based on the common principle that the value of a specific feature is independent of the value of any other feature.

*These algorithms are called machine learning algorithms and there are literally hundreds of them.Covering how much math is needed for every type of algorithm in depth is not within the scope of this post, I will discuss how much math you need to know for each of the following commonly-used algorithms: What they are: Naïve Bayes’ classifiers are a family of algorithms based on the common principle that the value of a specific feature is independent of the value of any other feature.Math is like an octopus: it has tentacles that can reach out and touch just about every subject.*

These suggestions are derived from my own experience in the data science field, and following up with the latest resources suggested by the community.

However, if you are a beginner in machine learning and looking to get a job in industry, I don’t recommend studying all the math before starting to do actual practical work, this bottom up approach is counter-productive and you’ll get discouraged, as you started with the theory ().

Learning the theoretical background for data science or machine learning can be a daunting experience, as it involves multiple fields of mathematics, and a long list of online resources.

In this piece, my goal is to suggest resources to build the mathematical background necessary to get up and running in data science practical/research work.

Hope that helps you expand your machine learning knowledge, and fight your fear of discovering what’s happening behind the scenes of your sklearn/keras/pandas import statements.

Your contributions are very welcomed, through reviewing one of the listed resources, or adding new awesome ones.- [Instructor] A total of 72 people participated in survey about their music preferences.The results, separated by gender, are displayed above.So there's 35 males that were asked what is there musical preference.And we wanna know what is the probability that a male likes rap music according to the survey. So out of the 35 males, how many of them liked rap music?My advice is to do it the other way around (top down approach), learn how to code, learn how to use the Py Data stack (Pandas, sklearn, Keras, etc..), get your hands dirty building real world projects, use libraries documentations and You Tube/Medium tutorials.THEN, you’ll start to see the bigger picture, noticing your lack of theoretical background, to actually understand how those algorithms work, at that moment, studying math will make much more sense to you!Here at Dataquest, we define data science as the discipline of using data and advanced statistics to make predictions.It’s a professional discipline that’s focused on creating understanding from sometimes-messy and disparate data (although precisely what a data scientist is tackling will vary by employer).Here’s an article by the awesome I will divide the resources to 3 sections (Linear Algebra, Calculus, Statistics and probability), the list of resources will be in no particular order, resources are diversified between video tutorials, books, blogs, and online courses.Used in machine learning (& deep learning) to understand how algorithms work under the hood.

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