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

TLDRThe speaker emphasizes the importance of understanding the mathematical foundations of machine learning, despite the availability of courses that skip over the math. They argue that while one can apply machine learning algorithms without deep math knowledge, true mastery requires understanding concepts like linear algebra, calculus, and statistics. The speaker encourages learners to delve into the math behind algorithms, as it's crucial for avoiding overfitting and improving model performance. They suggest that educators often avoid teaching the math due to its complexity, making it the learner's responsibility to seek out this knowledge.

### Takeaways

- ๐งฎ Math is fundamental to machine learning, yet many courses and workshops skip over it.
- ๐ซ Skipping math is dangerous because it's crucial for understanding how machine learning algorithms work.
- ๐จโ๐ซ The lack of math in machine learning education is often due to time constraints or the difficulty of teaching it.
- ๐ Understanding the math behind algorithms is essential for avoiding overfitting and improving model performance.
- ๐ Machine learning involves linear algebra, calculus, and statistics, which are key areas of math to master.
- ๐ค Many people apply machine learning without a deep understanding of the underlying math, which can lead to issues.
- ๐ The speaker suggests that learning the math behind machine learning is crucial for success in the field.
- ๐ The speaker encourages picking up textbooks to learn the math if it's not taught in courses or workshops.
- ๐ก It's the responsibility of the learner to go beyond the basics and understand the math behind machine learning.
- ๐ The speaker promises to create content on the fundamental mathematics of machine learning if there's enough interest.

### Q & A

### How important is math in learning machine learning according to the speaker?

-The speaker emphasizes that math is extremely important in learning machine learning, as it is the fundamental thing that drives machine learning algorithms.

### What are some common excuses given for skipping math in machine learning courses?

-The speaker mentions that instructors often claim that math is beyond the scope of the course or that they are constrained by time, which results in skipping over the mathematical details.

### What are the fundamental math concepts required for machine learning?

-The speaker highlights that linear algebra, calculus, and statistics are fundamental concepts in math that one needs to know before becoming a machine learning engineer or data scientist.

### Why does the speaker find it dangerous to skip math in machine learning courses?

-The speaker finds it dangerous because without understanding the math, learners may not grasp the intricacies of algorithms, leading to potential overfitting or underperformance of machine learning models.

### What is the speaker's opinion on the current state of machine learning education?

-The speaker believes that many machine learning courses and workshops skip over the math, which is not good, and that it is the responsibility of learners to go beyond these courses and learn the math on their own.

### What is the role of math in understanding and improving machine learning models?

-Math is crucial for understanding the underlying mechanisms of machine learning algorithms and for diagnosing and improving model performance, such as avoiding overfitting and updating weights through backpropagation.

### Why does the speaker encourage learners to learn the math behind machine learning?

-The speaker encourages learning the math because it can be a make-or-break factor for job success in machine learning or for successfully completing projects.

### What is the speaker's suggestion for those interested in the mathematics of machine learning?

-The speaker suggests that interested learners should pick up textbooks and start learning the math immediately, as it is essential for a deep understanding of machine learning.

### How does the speaker feel about the ease of programming in machine learning?

-The speaker believes that the programming aspect of machine learning is not very hard, thanks to packages like scikit-learn, PyTorch, and TensorFlow, but mastering the math behind it is more challenging and time-consuming.

### What is the speaker's call to action for machine learning learners?

-The speaker calls on machine learning learners to go beyond just learning how to apply machine learning and to delve into the fundamental source code or textbooks to learn the applied math.

### Outlines

### ๐งฎ The Importance of Mathematics in Machine Learning

The speaker emphasizes the critical role of mathematics in machine learning, arguing that while it's possible to engage with machine learning without deep mathematical knowledge, understanding the underlying math is essential. They critique the common practice of skipping over mathematical details in courses and workshops, suggesting this approach is 'dangerously dangerous.' The speaker points out that fundamental concepts like linear algebra, calculus, and statistics are crucial for anyone aiming to be a machine learning engineer or data scientist. They also highlight the issue of overfitting and the need for a solid mathematical foundation to diagnose and improve model performance. The speaker encourages viewers to learn the math behind machine learning algorithms and expresses a desire to create content on this topic if there's enough interest.

### ๐ Beyond Workshops: Delving into the Mathematics of Machine Learning

In this paragraph, the speaker urges viewers to take the initiative to learn the mathematical foundations of machine learning, even if their courses or workshops do not cover it. They stress the importance of not solely relying on online courses and workshops, which may not delve into the complex mathematics due to time constraints or the difficulty of teaching it. The speaker encourages viewers to explore the source code and textbooks to gain a deeper understanding of the mathematics applied in machine learning. They conclude by inviting viewers to share their experiences with learning machine learning and mathematics, and to provide feedback on the importance of math in their journey. The speaker also asks viewers to subscribe and engage with the content by liking and commenting.

### Mindmap

### Keywords

### ๐กMachine Learning

### ๐กMathematics

### ๐กLinear Algebra

### ๐กCalculus

### ๐กStatistics

### ๐กOverfitting

### ๐กActivation Function

### ๐กBackpropagation

### ๐กGradient Descent

### ๐กDeterminant

### ๐กNeural Networks

### Highlights

You don't need to be an expert in every area of math to learn machine learning.

It's possible to learn machine learning without deep math knowledge, but it's risky.

Many machine learning courses and workshops skip over the math, which is a problem.

Math is fundamental to understanding how machine learning algorithms work.

Knowing the math behind algorithms is crucial for avoiding overfitting and improving model performance.

Machine learning involves linear algebra, calculus, and statistics.

Statistics and linear algebra are must-know subjects for machine learning engineers and data scientists.

People often apply machine learning without fully understanding the underlying math, leading to issues.

Courses sometimes skip math details, leaving students to learn on their own.

It's the responsibility of the learner to understand the math behind machine learning.

Teaching the math behind machine learning is challenging and time-consuming for instructors.

The speaker is considering making videos about the fundamental mathematics of machine learning.

Machine learning programming is easier than understanding the underlying mathematics.

Understanding concepts like matrix multiplication, determinants, and gradient descent is essential.

The purpose of activation functions and backpropagation is often overlooked in courses.

Learners should go beyond basic workshops and learn the math behind machine learning.

The speaker encourages comments on the importance of math in machine learning and learning experiences.

The speaker asks viewers to subscribe and like the video for support.

## Casual Browsing

Should you learn Ai and Machine Learning?

2024-09-22 14:12:00

How I'd Learn AI (If I Had to Start Over)

2024-09-22 12:48:01

How to use the Free Microsoft Math Solver tool #Maths #education #learning

2024-09-10 20:35:00

How I'd Learn AI in 2024 (if I could start over)

2024-09-22 15:53:00

Use the Microsoft Edge Math Solver to Help You With Your Math Problems

2024-09-10 18:37:00

Huxli AI - Solving Math With AI - Easy Tutorial

2024-09-10 13:50:00