Mathematical Foundations of Machine Learning

Mathematical Foundations of Machine Learning, available at $119.99, has an average rating of 4.59, with 117 lectures, 1 quizzes, based on 6427 reviews, and has 125612 subscribers.

You will learn about Understand the fundamentals of linear algebra and calculus, critical mathematical subjects underlying all of machine learning and data science Manipulate tensors using all three of the most important Python tensor libraries: NumPy, TensorFlow, and PyTorch How to apply all of the essential vector and matrix operations for machine learning and data science Reduce the dimensionality of complex data to the most informative elements with eigenvectors, SVD, and PCA Solve for unknowns with both simple techniques (e.g., elimination) and advanced techniques (e.g., pseudoinversion) Appreciate how calculus works, from first principles, via interactive code demos in Python Intimately understand advanced differentiation rules like the chain rule Compute the partial derivatives of machine-learning cost functions by hand as well as with TensorFlow and PyTorch Grasp exactly what gradients are and appreciate why they are essential for enabling ML via gradient descent Use integral calculus to determine the area under any given curve Be able to more intimately grasp the details of cutting-edge machine learning papers Develop an understanding of what’s going on beneath the hood of machine learning algorithms, including those used for deep learning This course is ideal for individuals who are You use high-level software libraries (e.g., scikit-learn, Keras, TensorFlow) to train or deploy machine learning algorithms, and would now like to understand the fundamentals underlying the abstractions, enabling you to expand your capabilities or You’re a software developer who would like to develop a firm foundation for the deployment of machine learning algorithms into production systems or You’re a data scientist who would like to reinforce your understanding of the subjects at the core of your professional discipline or You’re a data analyst or A.I. enthusiast who would like to become a data scientist or data/ML engineer, and so you’re keen to deeply understand the field you’re entering from the ground up (very wise of you!) It is particularly useful for You use high-level software libraries (e.g., scikit-learn, Keras, TensorFlow) to train or deploy machine learning algorithms, and would now like to understand the fundamentals underlying the abstractions, enabling you to expand your capabilities or You’re a software developer who would like to develop a firm foundation for the deployment of machine learning algorithms into production systems or You’re a data scientist who would like to reinforce your understanding of the subjects at the core of your professional discipline or You’re a data analyst or A.I. enthusiast who would like to become a data scientist or data/ML engineer, and so you’re keen to deeply understand the field you’re entering from the ground up (very wise of you!).

Enroll now: Mathematical Foundations of Machine Learning

Summary

Title: Mathematical Foundations of Machine Learning

Price: $119.99

Average Rating: 4.59

Number of Lectures: 117

Number of Quizzes: 1

Number of Published Lectures: 115

Number of Curriculum Items: 118

Number of Published Curriculum Objects: 115

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the fundamentals of linear algebra and calculus, critical mathematical subjects underlying all of machine learning and data science

Manipulate tensors using all three of the most important Python tensor libraries: NumPy, TensorFlow, and PyTorch

How to apply all of the essential vector and matrix operations for machine learning and data science

Reduce the dimensionality of complex data to the most informative elements with eigenvectors, SVD, and PCA

Solve for unknowns with both simple techniques (e.g., elimination) and advanced techniques (e.g., pseudoinversion)

Appreciate how calculus works, from first principles, via interactive code demos in Python

Intimately understand advanced differentiation rules like the chain rule

Compute the partial derivatives of machine-learning cost functions by hand as well as with TensorFlow and PyTorch

Grasp exactly what gradients are and appreciate why they are essential for enabling ML via gradient descent

Use integral calculus to determine the area under any given curve

Be able to more intimately grasp the details of cutting-edge machine learning papers

Develop an understanding of what’s going on beneath the hood of machine learning algorithms, including those used for deep learning

Who Should Attend

You use high-level software libraries (e.g., scikit-learn, Keras, TensorFlow) to train or deploy machine learning algorithms, and would now like to understand the fundamentals underlying the abstractions, enabling you to expand your capabilities

You’re a software developer who would like to develop a firm foundation for the deployment of machine learning algorithms into production systems

You’re a data scientist who would like to reinforce your understanding of the subjects at the core of your professional discipline

You’re a data analyst or A.I. enthusiast who would like to become a data scientist or data/ML engineer, and so you’re keen to deeply understand the field you’re entering from the ground up (very wise of you!)

Target Audiences

You use high-level software libraries (e.g., scikit-learn, Keras, TensorFlow) to train or deploy machine learning algorithms, and would now like to understand the fundamentals underlying the abstractions, enabling you to expand your capabilities

You’re a software developer who would like to develop a firm foundation for the deployment of machine learning algorithms into production systems

You’re a data scientist who would like to reinforce your understanding of the subjects at the core of your professional discipline

You’re a data analyst or A.I. enthusiast who would like to become a data scientist or data/ML engineer, and so you’re keen to deeply understand the field you’re entering from the ground up (very wise of you!)

Mathematics forms the core of data science and machine learning. Thus, to be the best data scientist you can be, you must have a working understanding of the most relevant math.

Getting started in data science is easy thanks to high-level libraries like Scikit-learn and Keras. But understanding the math behind the algorithms in these libraries opens an infinite number of possibilities up to you. From identifying modeling issues to inventing new and more powerful solutions, understanding the math behind it all can dramatically increase the impact you can make over the course of your career.

Led by deep learning guru Dr. Jon Krohn, this course provides a firm grasp of the mathematics — namely linear algebra and calculus — that underlies machine learning algorithms and data science models.

Course Sections

1. Linear Algebra Data Structures

2. Tensor Operations

3. Matrix Properties

4. Eigenvectors and Eigenvalues

5. Matrix Operations for Machine Learning

6. Limits

7. Derivatives and Differentiation

8. Automatic Differentiation

9. Partial-Derivative Calculus

10. Integral Calculus

Throughout each of the sections, you’ll find plenty of hands-on assignments, Python code demos, and practical exercises to get your math game in top form!

This Mathematical Foundations of Machine Learning course is complete, but in the future, we intend on adding extra content from related subjects beyond math, namely: probability, statistics, data structures, algorithms, and optimization. Enrollment now includes free, unlimited access to all of this future course content — over 25 hours in total.

Are you ready to become an outstanding data scientist? See you in the classroom.

Course Curriculum

Chapter 1: Data Structures for Linear Algebra

Lecture 1: Introduction

Lecture 2: What Linear Algebra Is

Lecture 3: Plotting a System of Linear Equations

Lecture 4: Linear Algebra Exercise

Lecture 5: Tensors

Lecture 6: Scalars

Lecture 7: Vectors and Vector Transposition

Lecture 8: Norms and Unit Vectors

Lecture 9: Basis, Orthogonal, and Orthonormal Vectors

Lecture 10: Matrix Tensors

Lecture 11: Generic Tensor Notation

Lecture 12: Exercises on Algebra Data Structures

Lecture 13: Learning Paths

Chapter 2: Tensor Operations

Lecture 1: Segment Intro

Lecture 2: Tensor Transposition

Lecture 3: Basic Tensor Arithmetic, incl. the Hadamard Product

Lecture 4: Tensor Reduction

Lecture 5: The Dot Product

Lecture 6: Exercises on Tensor Operations

Lecture 7: Solving Linear Systems with Substitution

Lecture 8: Solving Linear Systems with Elimination

Lecture 9: Visualizing Linear Systems

Chapter 3: Matrix Properties

Lecture 1: Segment Intro

Lecture 2: The Frobenius Norm

Lecture 3: Matrix Multiplication

Lecture 4: Symmetric and Identity Matrices

Lecture 5: Matrix Multiplication Exercises

Lecture 6: Matrix Inversion

Lecture 7: Diagonal Matrices

Lecture 8: Orthogonal Matrices

Lecture 9: Orthogonal Matrix Exercises

Chapter 4: Eigenvectors and Eigenvalues

Lecture 1: Segment Intro

Lecture 2: Applying Matrices

Lecture 3: Affine Transformations

Lecture 4: Eigenvectors and Eigenvalues

Lecture 5: Matrix Determinants

Lecture 6: Determinants of Larger Matrices

Lecture 7: Determinant Exercises

Lecture 8: Determinants and Eigenvalues

Lecture 9: Eigendecomposition

Lecture 10: Eigenvector and Eigenvalue Applications

Chapter 5: Matrix Operations for Machine Learning

Lecture 1: Segment Intro

Lecture 2: Singular Value Decomposition

Lecture 3: Data Compression with SVD

Lecture 4: The Moore-Penrose Pseudoinverse

Lecture 5: Regression with the Pseudoinverse

Lecture 6: The Trace Operator

Lecture 7: Principal Component Analysis (PCA)

Lecture 8: Resources for Further Study of Linear Algebra

Chapter 6: Limits

Lecture 1: Segment Intro

Lecture 2: Intro to Differential Calculus

Lecture 3: Intro to Integral Calculus

Lecture 4: The Method of Exhaustion

Lecture 5: Calculus of the Infinitesimals

Lecture 6: Calculus Applications

Lecture 7: Calculating Limits

Lecture 8: Exercises on Limits

Chapter 7: Derivatives and Differentiation

Lecture 1: Segment Intro

Lecture 2: The Delta Method

Lecture 3: How Derivatives Arise from Limits

Lecture 4: Derivative Notation

Lecture 5: The Derivative of a Constant

Lecture 6: The Power Rule

Lecture 7: The Constant Multiple Rule

Lecture 8: The Sum Rule

Lecture 9: Exercises on Derivative Rules

Lecture 10: The Product Rule

Lecture 11: The Quotient Rule

Lecture 12: The Chain Rule

Lecture 13: Advanced Exercises on Derivative Rules

Lecture 14: The Power Rule on a Function Chain

Chapter 8: Automatic Differentiation

Lecture 1: Segment Intro

Lecture 2: What Automatic Differentiation Is

Lecture 3: Autodiff with PyTorch

Lecture 4: Autodiff with TensorFlow

Lecture 5: The Line Equation as a Tensor Graph

Lecture 6: Machine Learning with Autodiff

Chapter 9: Partial Derivative Calculus

Lecture 1: Segment Intro

Lecture 2: What Partial Derivatives Are

Lecture 3: Partial Derivative Exercises

Lecture 4: Calculating Partial Derivatives with Autodiff

Lecture 5: Advanced Partial Derivatives

Lecture 6: Advanced Partial-Derivative Exercises

Lecture 7: Partial Derivative Notation

Lecture 8: The Chain Rule for Partial Derivatives

Lecture 9: Exercises on the Multivariate Chain Rule

Lecture 10: Point-by-Point Regression

Lecture 11: The Gradient of Quadratic Cost

Lecture 12: Descending the Gradient of Cost

Lecture 13: The Gradient of Mean Squared Error

Lecture 14: Backpropagation

Instructors

Dr Jon Krohn
Chief Data Scientist and #1 Bestselling Author

SuperDataScience Team
Helping Data Scientists Succeed

Ligency Team
Helping Data Scientists Succeed

Rating Distribution

1 stars: 63 votes

2 stars: 92 votes

3 stars: 514 votes

4 stars: 1983 votes

5 stars: 3777 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

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