# All the maths you need for machine learning for FREE!

TLDRDiscover a free resource for mastering the math behind machine learning. The book 'Mathematics for Machine Learning' offers a comprehensive guide covering linear algebra, calculus, and statistics, essential for understanding and building machine learning algorithms. Though not for complete beginners, it's perfect for those with a high school math foundation. The book is available for free online, complete with exercises and tutorials, making it an invaluable resource for anyone looking to enhance their machine learning knowledge without breaking the bank.

### Takeaways

- 📚 The video introduces a free resource for learning the math needed for machine learning.
- 🧠 Mathematics is essential in machine learning due to the prevalence of vectors and matrices.
- 📈 Linear algebra, calculus, and probability are key areas of math covered for understanding machine learning algorithms.
- 📘 The book 'Mathematics for Machine Learning' is recommended but can be expensive.
- 🆓 The same topics can be found for free by the same authors, suitable for those with high school level math.
- 🔢 The book assumes a basic understanding of vectors, calculus, and matrices, directing beginners to free resources like Khan Academy.
- 🛠️ It is designed for those without a math degree but who want to understand the math behind deploying and building machine learning algorithms.
- 📚 The book is divided into two parts: mathematical foundations and applying those foundations to build machine learning algorithms.
- 🈚️ The book contains no code, focusing solely on the mathematical concepts.
- 📖 The explanations are clear, and the book does a good job of explaining mathematical symbols for non-mathematicians.
- 🔗 The book is available as a free PDF download from the authors' website, which also includes teaching exercises and tutorials.

### Q & A

### What is the importance of mathematics in machine learning?

-Mathematics is crucial in machine learning because most concepts are based on vectors and matrices, requiring a good understanding of linear algebra, calculus, and probability and statistics.

### What is the title of the book mentioned in the transcript that covers mathematics for machine learning?

-The title of the book is 'Mathematics for Machine Learning'.

### Is the book 'Mathematics for Machine Learning' available for free?

-Yes, the book 'Mathematics for Machine Learning' is available for free from the authors.

### Who is the target audience for the book 'Mathematics for Machine Learning'?

-The book is aimed at people who do not have a mathematics degree but want to understand enough math to deploy and build machine learning algorithms.

### What are the prerequisites for understanding the content of 'Mathematics for Machine Learning'?

-The prerequisites include having the equivalent of high school mathematics, with basic knowledge of vectors, calculus, and matrices.

### How is the book 'Mathematics for Machine Learning' structured?

-The book is divided into two parts: the first part covers mathematical foundations like linear algebra, matrix decomposition, vector calculus, probability, and distributions. The second part shows how to use these foundations to build machine learning algorithms.

### Does the book 'Mathematics for Machine Learning' include any coding examples?

-No, the book does not include code; it focuses solely on teaching the mathematical concepts.

### What additional resources are available on the book's website?

-The book's website offers downloadable PDFs of the book, a table of contents, and teaching exercises for further learning.

### How can one access the book 'Mathematics for Machine Learning' and its resources?

-You can access the book and its resources by visiting the book's website, where you can download the PDF and find additional tutorials and exercises.

### What is the publisher of the book 'Mathematics for Machine Learning'?

-The book is published by Cambridge University Press.

### How does the book 'Mathematics for Machine Learning' explain mathematical symbols?

-The book is noted for explaining mathematical symbols in a clear and well-explained manner, making it accessible for non-mathematicians.

### Outlines

### 📚 Free Resource for Machine Learning Mathematics

The speaker introduces the concept of a free, comprehensive resource for learning the mathematics necessary for machine learning. They emphasize the importance of understanding linear algebra, calculus, and statistics in this field. The speaker then mentions a book, 'Mathematics for Machine Learning,' which they found on Amazon but discovered is also available for free from the authors. It's noted that the book is not for complete beginners but requires a high school level of mathematics. The book is divided into two parts: the first covering mathematical foundations and the second applying these to build machine learning algorithms. The speaker appreciates the clarity and explanation of mathematical symbols in the book, which can be daunting for non-mathematicians. The video features a humorous moment with turkeys walking by, adding a light-hearted touch to the educational content. The book is published by Cambridge University Press, and the speaker highly recommends the resource, mentioning that it can be downloaded as a PDF from the book's website, which also includes teaching exercises.

### Mindmap

### Keywords

### 💡Machine Learning

### 💡Linear Algebra

### 💡Gradient Descent

### 💡Calculus

### 💡Probability

### 💡Statistics

### 💡Mathematics for Machine Learning

### 💡Vector

### 💡Matrix

### 💡Khan Academy

### 💡Cambridge University Press

### 💡Teaching Exercises

### Highlights

Learning all the maths needed for machine learning from one free source is possible.

Mathematics is essential in machine learning due to the prevalence of vectors and matrices.

Linear algebra, gradient descent, and probability are fundamental areas of maths for machine learning.

The book 'Mathematics for Machine Learning' is frequently recommended on Amazon.

A free alternative to the book is available directly from the authors.

The book is not suitable for complete beginners; a basic understanding of maths is required.

For those lacking the basics, resources like Khan Academy can help prepare for the book.

The book targets individuals without a maths degree who want to understand machine learning algorithms.

The book is divided into two parts: mathematical foundations and application to machine learning algorithms.

Part one covers linear algebra, matrix decomposition, vector calculus, probability, and distributions.

Part two demonstrates the use of mathematical foundations in building machine learning algorithms.

The book contains no code, focusing solely on the mathematical concepts.

The explanations are clear and well-structured, making the maths accessible to non-mathematicians.

The book explains mathematical symbols thoroughly, which can be challenging for those unfamiliar with them.

The book is published by Cambridge University Press, ensuring high-quality content.

The entire book can be downloaded as a PDF from the book's official website.

Teaching exercises are available on the website to complement the book's content.

The book's website also includes a table of contents and tutorials for further learning.

A link to the book's website is provided in the video description for easy access.