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In today’s world of Siri and Google Assistant, data science and machine learning have become the most favourite career options. College-going students are doing their master’s degree in data science and professionals are doing certificate courses so that they can enter into data science, machine learning and artificial intelligence domain. But many people have the perception that they will need to have expertise in math to have a career in AI. Let us break that perception today.
If you are looking for an answer to this question in a simple YES or NO, then the answer is YES! YES, it is POSSIBLE to have a career in data science, machine learning and AI even if you are not an expert in math. After you have got your simple answer, let us understand how it is possible.
How is it possible to have a career in AI if you are not good at Math?
If you are willing to build a career in data science domain in machine learning or AI, you will be able to build a career in AI even if you are not a math expert. However, you shouldn’t think that there is no use of math in AI or machine learning.
To learn practical machine learning, you will need to learn some topics of math such as working with numbers, calculating the percentage. We will discuss the same in the next section of this article but in summary, you’ll need college-level math knowledge such as basic statistics which includes mean, standard deviation, variance, and few other things.
Keep in mind that while math is needed in AI and Machine Learning but you don’t have to be an expert or master in math or statistics. If people try to convince you that, you have to be master in calculus, linear algebra, differential equations, and optimization theory then you shouldn’t be convinced with what they are saying. While basic knowledge of these aspects will be useful when you start learning advanced skills. If you don’t know these things, there is no need to worry. If you take a course on ProLearn, it will cover all these aspects.
Why math is needed at all?
Machine Learning is all about creating an algorithm which can learn from data to make a prediction. That prediction can be identifying an object or predicting the future price of a stock. Machine learning is built on mathematical prerequisites.
In Artificial Intelligence and Machine Learning, math provides a way to implement how goals can be accomplished. Math gives us powerful and valid answers. It demonstrates how to solve difficult and deep abstract problems.
Sometimes it may feel like learning math and learning machine learning will be difficult and overwhelming. But the truth is, it is not difficult. It's not as long as you understand why the match is being used. Once you crack it, it will surely make artificial intelligence and machine learning a lot more fun.
What math you will learn to build a career in AI, ML and data science?
Let us discuss what topic in math you will need to learn after you get started in Artificial Intelligence and Machine Learning. You really don’t need to have knowledge of these aspects in advance or from day one. I will share a secret tip with you at before the end of this section but first, let us have a look at some of the topics you can become familiar with after you get started.
Statistics: As you will be working with data, having a familiarity with statistics, and the ability to understand and visualize that data will help you a lot. Most of us learned statistics in our higher secondary school days so it’s difficult for us.
What are the chances of getting tails when we toss a coin? It’s 50% or 0.5, right? As you will already be familiar with the basics of probability, it will not be a steep learning curve for you. You’ll need to learn the application of probability (probabilistic models) when you are working on advanced applications such as self-driving cars and artificial intelligence.
Linear Algebra: Linear Algebra is a topic with which you will need to get familiar with. Why? It is because when you are doing Machine Learning or AI, your data will be in the form of multi-dimensional matrices. Once you get familiar with linear algebra and multi-dimensional matrices, you can do magic with your code. We recommend you to learn this after you get started.
Learning differential calculus is not at all mandatory. Even without knowing it, you will be able to create a project in deep learning. Let’s say if you are willing to go deeper and want to do our own research from scratch then you will it is suggested to learn differential calculus.
These are the topic you will learn in math after you get started with AI or machine learning. As we discussed earlier, you don’t need to become a master in all the topics. Having basic fundamentals clear of those concepts will be enough for you unless you want to do detailed research. There are multiple courses of Manipal ProLearn in which you will learn these aspects with ease.
Too long; didn’t read? Let us summarize!
In today’s world of Google Assistant and Alexa; data science, machine learning and artificial intelligence are becoming a hot favourite career option. Many of us have a solid interest in choosing this domain at career path but may be worried and concerned because they might not be great at math.
If you have the same concern, you don’t need to worry at all. Once you enroll for Artificial Intelligence and Machine Learning courses at Manipal ProLearn, you will learn math fundamentals in a very lucid & engaging manner. Instructors will help you learn in such a way that even if you are non-enthusiast math, will fall in love with math and you will master it.
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We hope that this article helped you to understand how you can build a career in data science domain when if you are not an expert in math. Do you still have any questions in your mind? Feel free to ask us any many as you want by writing to us at email@example.com