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
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Linear Algebra Data Structures
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Tensor Operations
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Matrix Properties
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Eigenvectors and Eigenvalues
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Matrix Operations for Machine Learning
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Limits
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Derivatives and Differentiation
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Automatic Differentiation
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Partial-Derivative Calculus
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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
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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
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