What kind of Math is needed for Machine Learning?

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Introduction

The question "how much math do I need to know for machine learning" is one of the most frequently asked by people aspiring to a career in data science. Math is a significant barrier to entry for those who want to pursue careers in machine learning.

Most businesses utilize data to solve use cases that are relatively similar. They need data scientists to create machine learning models to segment the market, forecast revenue, and anticipate client turnover.

These issues can be solved using a similar strategy, but the process becomes monotonous. They make use of pre-established ML techniques thus there is no need to invent the wheel.

Even if there is a situation where you need to build your own machine-learning model, an intuitive understanding of specific topics is enough. You don't have to go deep, and you certainly don't have to be a math whiz to become a machine learning engineer.

For instance, we are aware that in linear regression, the line of best fit is determined via gradient descent. You don't have to start studying differential equations just now. To obtain an understanding of how calculus works, you only need to be familiar with its basic concepts.

Similarly, if you were to create a neural network using Tensorflow — you have to do a lot of matrix manipulation, but you'll be doing it with the help of a computer program. This means you don't have to go back and practice solving algebraic equations. You just have to understand how they work.

In this article, we'll direct you to resources to help you get started with teaching math for data science.

What Connection Exists Between Mathematics and Machine Learning?

Math is at the heart of machine learning, which enables the development of an algorithm that can learn from data and generate precise predictions. Identifying dogs or cats from a series of photos or deciding what kind of things to suggest to a consumer based on past purchases are examples of basic predictions. Therefore, it is essential to properly understand the mathematical concepts behind any central machine learning algorithm. This will help you choose all the suitable algorithms for your data science and machine learning project.

Machine learning is primarily built on mathematical assumptions, so it will be more attractive if you understand why mathematics is used. This will help you understand why we choose one machine learning algorithm over another and how it affects the performance of a machine learning model.

Linear algebra, calculus, and statistics serve as the machine learning field's mathematical building blocks. Because matrices and vectors are used to represent data in machine learning, linear algebra is the most important subject. Statistics are necessary to interpret the results obtained by learning algorithms and understand the data distribution. The number helps you know how the learning process works under the hood.

Our Learners Also Read: What is Tableau used for?

What Mathematics Do I Require for Machine Learning?

Your need for math will largely depend on the position you're vying for. Machine learning scientists, researchers, and engineers are divided into two groups in recent years. Machine learning engineers are not. Let's say your goal is to work in research. In such an instance, your math requirements will be greater than those of a machine learning engineer who wants to apply well-established algorithms to the commercial issues facing their company.

In addition, some fields, such as deep learning for computer vision or natural language processing, require stronger mathematical foundations than traditional machine learning.

Mathematical concepts important to machine learning and data science:

1. Linear algebra
2. Statistics and Probability
3. Calculus

Machine Learning Engineer

As a machine learning engineer, your job is to automate making sense of your company's data. Reread it! The emphasis here is on process automation. Algorithms and models to help you understand data and make inferences already exist. You don't have to reinvent the wheel, code a neural network, or support a vector machine from scratch. Instead, you use a package or framework like SciKitLearn or Tensorflow. Maybe you have a researcher in your company who has already trained and modified models on your data.

In terms of math, here's the bare minimum:

Introduction to Linear Algebra: You must understand what vectors and matrices are as well as how to add, subtract, and multiply using dot products.

Probability and Statistics: A basic statistics course should be sufficient. You ought to be familiar with the ideas of statistical independence, conditional probability, and random variables. You also need to know how to compute and decipher a data set's mean, standard deviation, and standard deviation. You must be familiar with both the normal or Gaussian distribution and the binomial distribution when it comes to probability distributions. Recognize confidence intervals and p-values.

Calculus: When creating models using pre-existing frameworks, calculus is not necessary. The calculation is used by Machine Learning algorithms like gradient descent to find ideal values. Frameworks like TensorFlow take care of these details for you. I know that's a bold statement that many people will disagree with. Knowing partial derivatives is helpful for better understanding how many machine learning models work. However, it is not a requirement to use frameworks. So if you just want the bare minimum to get started with machine learning, calculus isn't a requirement.

Why should you be interested in Mathematics? Why do you need Math in Machine Learning Projects?

There are many reasons why mathematics is essential for machine learning, and I will share some important points below:

• Choosing the best algorithm requires consideration of accuracy, training time, model complexity, number of parameters, and features.
• Selection of parameter values and verification methods.
• Understanding the Bias-Variance trade-off will allow you to identify underfitting and overfitting problems that commonly occur in program execution.
• Determining the correct confidence and uncertainty interval.

What is the Right way to Learn Math for Machine Learning?

There are plenty of worthwhile resources available online that explain concepts such as vector calculus of matrix decompositions, matrix analytic geometry, linear algebra, the mathematics behind the principal component analysis, and support vector machines. Not all resources are a comprehensive solution to your understanding. Therefore, we have compiled a list of books and youtube channels that can help you improve your theoretical idea in the field of artificial intelligence.

Mathematics for Machine Learning by Marc Peter Deisenroth is a book that can help you start your mathematical journey. The practical applications of the algorithms and the mathematics behind them were clearly explained. 

Multivariate Calculus by Imperial College London: Imperial College London has essentially developed a YouTube series that covers the essential concepts of multivariate calculus and its application in various ml algorithms. Although the entire course is in partnership with Coursera, Imperial College London has made it available for free to all inquisitive students.

Khan Academy courses on Linear Algebra, Probability and Statistics, Calculus of Variables, and Optimization – a comprehensive and accessible resource available to all students to deepen their knowledge in complex concepts such as the matrix of analytic geometry of linear algebra.

All Statistics: A Short Course in Statistical Inference by Larry Wasserman is said to be another comprehensive resource that includes detailed explanations of essential concepts such as

Udacity's Introduction to Statistics – is another free resource to gain an initial understanding of the statistics needed for data science.

Conclusion

It will take about 3-4 months to learn and use the math concepts in practice. Please look at the above resources and learn this in parallel with machine learning algorithms to understand which algorithm is suitable for your model.

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