All the maths you need for machine learning for FREE!

Python Programmer
7 Sept 202003:22

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

00:00

📚 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

Machine learning is a subset of artificial intelligence that enables computers to learn from and make predictions or decisions based on data. It is central to the video's theme as the entire discussion revolves around understanding the mathematical concepts necessary for effectively implementing machine learning algorithms. The script emphasizes the importance of math in machine learning, stating that 'practically everything is either a vector or a matrix'.

💡Linear Algebra

Linear algebra is a branch of mathematics that deals with linear equations, linear transformations, and their representations in vector spaces and through matrices. In the context of the video, linear algebra is a foundational math area needed for machine learning because it provides the tools to understand and manipulate vectors and matrices, which are ubiquitous in machine learning models.

💡Gradient Descent

Gradient descent is an optimization algorithm used in machine learning to minimize a function by iteratively moving in the direction of the steepest descent as defined by the negative of the gradient. The script mentions that gradient descent 'relies on calculus,' highlighting its importance in the optimization process of machine learning algorithms.

💡Calculus

Calculus is a branch of mathematics that deals with limits, derivatives, integrals, and infinite series. It is essential in machine learning for understanding how algorithms learn from data, particularly in the context of optimization problems like gradient descent. The video script underscores the reliance of gradient descent on calculus.

💡Probability

Probability is the measure of the likelihood that a given event will occur. In machine learning, probability is crucial for understanding the uncertainty inherent in predictions and for developing statistical models. The script mentions probability as one of the key areas of math needed for machine learning.

💡Statistics

Statistics is the discipline that concerns the collection, analysis, interpretation, presentation, and organization of data. It plays a vital role in machine learning for model evaluation, hypothesis testing, and data analysis. The video script includes statistics as a key mathematical concept necessary for those looking to understand and build machine learning algorithms.

💡Mathematics for Machine Learning

This refers to the book 'Mathematics for Machine Learning' mentioned in the script, which is a resource that covers the mathematical concepts needed for machine learning. The book is not for complete beginners and assumes a high school level of understanding of math. It is highlighted as being available for free from the authors, which is a significant point in the video's message about accessible learning resources.

💡Vector

A vector is a mathematical object that has both magnitude and direction, or in the context of machine learning, it can be a one-dimensional array of numbers. The script notes that 'practically everything is either a vector or a matrix,' indicating the fundamental role vectors play in representing data in machine learning.

💡Matrix

A matrix is a two-dimensional array of numbers arranged in rows and columns. In the script, matrices are mentioned alongside vectors as fundamental structures in machine learning, where they are used for various operations including transformations and calculations.

💡Khan Academy

Khan Academy is a non-profit educational organization that provides free online courses, lessons, and practice exercises. In the video script, it is recommended for those who do not have a high school level of math to get up to speed before diving into more advanced machine learning mathematics.

💡Cambridge University Press

Cambridge University Press is a renowned publisher of academic and educational content. The script mentions that the book 'Mathematics for Machine Learning' is published by Cambridge University Press, indicating the quality and credibility associated with the book.

💡PDF

PDF stands for Portable Document Format, a file format used to present documents in a manner independent of application software, hardware, and operating systems. The script informs viewers that they can download the 'Mathematics for Machine Learning' book as a PDF from the book's website, emphasizing the ease of access to this free resource.

💡Teaching Exercises

Teaching exercises are practical activities designed to help learners understand and apply concepts. The script mentions that the book's website includes teaching exercises, which are additional resources for those learning the mathematical foundations for machine learning.

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.

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