Brochure
Enquire- Introduction
- What Linear Algebra Is
- Plotting a System of Linear Equations
- Linear Algebra Exercise
- Tensors
- Scalars
- Vectors and Vector Transposition
- Norms and Unit Vectors
- Basis, Orthogonal, and Orthonormal Vectors
- Matrix Tensors
- Generic Tensor Notation
- Exercises on Algebra Data Structures
Mathematical Foundations of Machine Learning
BY
Udemy
Learn the mathematical concepts essential for machine learning and data science with the Mathematical Foundations of ...Read more
Online
₹ 399 3699
Quick Facts
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course overview
Mathematical Foundations of Machine Learning Course will expose the learners to the mathematics and mathematical theorems used in machine learning. The curriculum of the online course developed jointly by Dr. Jon Krohn, Ligency I Team, and Ligency Team will equip the students with a solid understanding of mathematics needed for machine learning and data science such as linear algebra and calculus, critical mathematical subjects, and many more. Mathematical Foundations of Machine Learning Online Course recommends the learner to have acquaintance with secondary school-level mathematics to take full advantage of the programme.
Mathematical Foundations of Machine Learning Certification, offered by Udemy, will explore Data structures, Tensor operations, Eigenvectors, Eigenvalues, and the like. The students will be awarded a certificate of completion and lifetime and unlimited access to learning materials with a one-time fee only.
The highlights
- Online course
- Downloadable resources
- Full lifetime access
- Access on mobile and TV
- Certificate of completion
- English videos
- 30-Day Money-Back Guarantee
Program offerings
- 15.5 hours on-demand video
- 1 article
- Full lifetime access
- Access on mobile and tv
- Certificate of completion
- English videos
Course and certificate fees
Fees information
₹ 399 ₹3,699
certificate availability
Yes
certificate providing authority
Udemy
Who it is for
What you will learn
Through Mathematical Foundations of Machine Learning Online Certification, students will learn all the vector and matrix operations needed for machine learning and data science, NumPy, PyTorch, Automatic Differentiation, Partial Derivative calculus, and whatnot.
The syllabus
Data Structures for Linear Algebra
Tensor Operations
- Segment Intro
- Tensor Transposition
- Basic Tensor Arithmetic, incl. the Hadamard Product
- Tensor Reduction
- The Dot Product
- Exercises on Tensor Operations
- Solving Linear Systems with Substitution
- Solving Linear Systems with Elimination
- Visualizing Linear Systems
Matrix Properties
- Segment Intro
- The Frobenius Norm
- Matrix Multiplication
- Symmetric and Identity Matrices
- Matrix Multiplication Exercises
- Matrix Inversion
- Diagonal Matrices
- Orthogonal Matrices
- Orthogonal Matrix Exercises
Eigenvectors and Eigenvalues
- Segment Intro
- Applying Matrices
- Affine Transformations
- Eigenvectors and Eigenvalues
- Matrix Determinants
- Determinants of Larger Matrices
- Determinant Exercises
- Determinants and Eigenvalues
- Eigendecomposition
- Eigenvector and Eigenvalue Applications
Matrix Operations for Machine Learning
- Segment Intro
- Singular Value Decomposition
- Data Compression with SVD
- The Moore-Penrose Pseudoinverse
- Regression with the Pseudoinverse
- The Trace Operator
- Principal Component Analysis (PCA)
- Resources for Further Study of Linear Algebra
Limits
- Segment Intro
- Intro to Differential Calculus
- Intro to Integral Calculus
- The Method of Exhaustion
- Calculus of the Infinitesimals
- Calculus Applications
- Calculating Limits
- Exercises on Limits
Derivatives and Differentiation
- Segment Intro
- The Delta Method
- How Derivatives Arise from Limits
- Derivative Notation
- The Derivative of a Constant
- The Power Rule
- The Constant Multiple Rule
- The Sum Rule
- Exercises on Derivative Rules
- The Product Rule
- The Quotient Rule
- The Chain Rule
- Advanced Exercises on Derivative Rules
- The Power Rule on a Function Chain
Automatic Differentiation
- Segment Intro
- What Automatic Differentiation Is
- Autodiff with PyTorch
- Autodiff with TensorFlow
- The Line Equation as a Tensor Graph
- Machine Learning with Autodiff
Partial Derivative Calculus
- Segment Intro
- What Partial Derivatives Are
- Partial Derivative Exercises
- Calculating Partial Derivatives with Autodiff
- Advanced Partial Derivatives
- Advanced Partial-Derivative Exercises
- Partial Derivative Notation
- The Chain Rule for Partial Derivatives
- Exercises on the Multivariate Chain Rule
- Point-by-Point Regression
- The Gradient of Quadratic Cost
- Descending the Gradient of Cost
- The Gradient of Mean Squared Error
- Backpropagation
- Higher-Order Partial Derivatives
- Exercise on Higher-Order Partial Derivatives
Integral Calculus
- Segment Intro
- Binary Classification
- The Confusion Matrix
- The Receiver-Operating Characteristic (ROC) Curve
- What Integral Calculus Is
- The Integral Calculus Rules
- Indefinite Integral Exercises
- Definite Integrals
- Numeric Integration with Python
- Definite Integral Exercise
- Finding the Area Under the ROC Curve
- Resources for the Further Study of Calculus
- Congratulations!
Instructors
Dr Jon Krohn
Data Scientist
Freelancer
Ph.D
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