Importance of Mathematics in Machine Learning Education

Mathematics is crucial for mastering machine learning, enabling deeper understanding and effective application of algorithms.

### The Importance of Mathematics in Machine Learning
* 📚 To succeed in machine learning, it's crucial to **learn the underlying mathematics**, even if it's not covered in your workshops or courses.
* 🔍 Understanding concepts like **linear algebra, calculus, and statistics** is essential for effectively applying **machine learning techniques and algorithms**.
* 💡 Many learners can implement machine learning without a strong math background, but knowing the math can **significantly enhance your ability to troubleshoot and optimize models**.
* ⚠ There is a common issue where workshops and online courses **skip over the mathematical details**, which can lead to **misunderstandings and poor model performance**.
* 🧠 A solid grasp of mathematical principles is necessary to avoid pitfalls like **overfitting** and to ensure **accurate model evaluation and improvement**.
* ✏ The speaker expresses a desire for **more resources focused on the mathematics of machine learning**, indicating a **gap in current educational offerings**.
* 💬 Viewers are encouraged to **share their experiences with learning the math behind machine learning** and to consider the **importance of this knowledge** in their own journeys.
* 🔗 The speaker emphasizes that while programming tools can simplify implementation, **understanding the math is vital** for a deeper comprehension of machine learning algorithms.

How good do you really need to be in math to learn machine learning? Very good, but not in everything. Can you get away without learning math in machine learning? Absolutely yes, and that's why it's dangerously dangerous. I don't know about you, but how many people do you really see out there teaching math for machine learning? It could be some YouTube videos, or it could be an online course, or a workshop. I know a lot of workshops skip over math, and that's not good. Some of them say that it's, you know, I'm constrained, it's beyond the scope of this course, or whatever. Like, it's almost like there's always an excuse to skip over math, but math is the fundamental thing that drives machine learning, whatever algorithm you pick. So, let me know in the comments below. When you took a machine learning course, did they teach you the math behind the algorithms? CU, I'm very interested to know how many people know the math involved in a machine learning algorithm. Let it be linear regression, or you know, simple neuron networks, whatever it is. Like, how

many people really know the intricate math details for that algorithm, and this is why math is very important. Let me tell you, you know, if you're learning machine learning or you just started learning machine learning, just be aware that you can do machine learning without knowing the math, but it's very important that you know what the math behind all of this machine learning is. Look, machine learning involves a lot of linear algebra, a little bit of calculus, and quite a lot of Statistics. So, these are fundamental concepts in math that you need to know before you actually become a machine learning engineer or even a data scientist. Like, statistics is a must, right? Linear algebra is a must if you're using something like neural networks or linear regression, logistic regression, whatever it may be. And here's why math is really important, because a lot of times what I see is people applying machine learning for anything and everything, and a lot of times they have really high accuracies or very low accuracies, and that's a problem because you don't want models that overfit on

your training data. At the same time, if you want to improve the performance of your model, you need to know exactly what the problem is. And this is why I think there are a lot of people out there doing machine learning without really knowing the math behind these algorithms. And it's not entirely their fault because, you know, you take a workshop, for example, or an online course, and they skip over the details of the math. They say, Oh, okay, we get these equations. These are called, you know, activation functions or, you know, some kind of a loss function that needs to be determined. Basically, do this by, you know, following some kind of a math, linear algebra, blah, blah, blah. And then they say, Oh, we got to update the weights, so we do something called backpropagation, which involves calculus. So this is not a calculus class. Let's just skip over it, you know, spend some time on your own, learn on your own, and they'll be done with it. That is okay. It's not on them to entirely teach you the math behind machine learning. It's your

responsibility, and I know that there are a lot of people who teach ML, know linear algebra, they know how the math behind these algorithms work, but when it comes to teaching, they know that it's a lot more effort that they need to put in to refresh their memory or refresh how things work on paper at the fundamental level. So, I don't blame them entirely. Let me know in the comments below if you're interested to learn the mathematics involved in machine learning. If that's the case, if I get a good enough response, I'll probably make videos about the fundamental mathematics involved in building a machine learning model. I really wish teaching machine learning started with teaching the mathematics behind it because you can get really quick and up to speed when it comes to the programming aspect because it's not really that hard considering the fact that you've got packages like PyIT learn, PyTorch, TensorFlow, blah blah blah, but the mathematics is a problem because it takes time to sit down and, you know, understand the concepts. How do you do matrix multiplication? How do you use

linear algebra to I don't know find the determinant or something like how does gradient descent work and how do you update the weights back propagation blah blah blah what's the real purpose of the activation function and so on and so forth so my message for this video is just two things number one start learning the mathematics behind machine learning even if you're not taught your Workshop does not teach you or you've never learned the math you just know some of the basics and very broad overview of it pick up a textbook and start learning right away because that could be the make or break decision if you're looking for a job in machine learning or if you're trying to complete a project successfully and number two I want to say is don't just rely on online courses and workshops you have to go beyond that you have to go beyond just these workshops because they teach you a lot of things but there's also a lot of things that they cannot teach you simply because either they're constrained by time or nobody wants to

do the hard work of actually teaching the most difficult part of machine learning, which is the mathematics behind it. So, whatever the reason may be, I want to encourage you to go beyond just learning the basics of how to apply machine learning. Go dig up the code, the very fundamental source code, or pick up a textbook and learn the math that is being applied, because that is very important. Let me know in the comments below what you think. How important is math in machine learning? How many of you were actually taught the mathematics behind machine learning? Were you taught machine learning with math? Like, how did you guys start? Like, did you start with coding, or did you start with the mathematics behind machine learning? Let me know in the comments below and subscribe to the channel as always, and hit the like button because it goes a long way to support this channel. So, thank you all for your support so far. I will see you in the next one.

Machine learning and AI is extremely easy if you learn the math: My rant.

ChemCoder

The Importance of Math in Learning Machine Learning

Math is essential for understanding machine learning, but it's often overlooked in courses and workshops.

Many programs skip teaching the math behind algorithms, which can be detrimental.

The speaker is interested in knowing how many people have learned the math involved in machine learning algorithms.

The Importance of Math in Machine Learning

Math is crucial for understanding machine learning, with key concepts including linear algebra, calculus, and statistics.

A strong foundation in these areas is essential for roles like machine learning engineer or data scientist.

The author emphasizes the need for a deeper understanding of the math behind algorithms to avoid issues like overfitting and to improve model accuracy.

The Importance of Understanding Math in Machine Learning

Many people engage in machine learning without a strong grasp of the underlying math, often due to workshops and online courses skipping over important details.

These courses provide a basic overview of concepts like activation functions and backpropagation but may not offer in-depth explanations.

While it's not solely the instructor's responsibility to teach the math, learners are encouraged to take the initiative to study it on their own.

The Importance of Mathematics in Machine Learning

Learning the mathematics behind machine learning is crucial for success in the field, regardless of prior knowledge or formal education.

Online courses and workshops are helpful, but they often lack the depth needed to teach the complex mathematical concepts.

It's important to go beyond the basics and actively seek out textbooks or fundamental source code to understand the math being applied in machine learning.