Linear Algebra & Probability: Advanced Linear Algebra
Machine Learning
| Intermediate
- 14 Videos | 1h 42m 55s
- Includes Assessment
- Earns a Badge
Learners will discover how to apply advanced linear algebra and its principles to derive machine learning implementations in this 14-video course. Explore PCA, tensors, decomposition, and singular-value decomposition, as well as how to reconstruct a rectangular matrix from singular-value decomposition. Key concepts covered here include how to use Python libraries to implement principal component analysis with matrix multiplication; sparse matrix and its operations; tensors in linear algebra and arithmetic operations that can be applied; and how to implement Hadamard product on tensors by using Python. Next, learn how to calculate singular-value decomposition and reconstruct a rectangular matrix; learn the characteristics of probability applicable in machine learning; and study probability in linear algebra and its role in machine learning. You will learn types of random variables and functions used to manage random numbers in probability; examine the concept and characteristics of central limit theorem and means and learn common usage scenarios; and examine the concept of parameter estimation and Gaussian distribution. Finally, learn the characteristics of binomial distribution with real-time examples.
WHAT YOU WILL LEARN
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discover the key concepts covered in this courseuse Python libraries to implement principal component analysis with matrix multiplicationdescribe sparse matrix and the operations that can be performed on sparse matrixdefine the concept of tensors in linear algebra and list the arithmetic operations that can be applied on tensorsimplement Hadamard product on tensors using Pythondescribe singular-value decomposition and how to calculate itreconstruct a rectangular matrix from single-value decomposition
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recognize the characteristics of probability that are applicable in machine learningdescribe probability in linear algebra and its role in machine learningrecall the types of random variables and the functions that can be used to manage random numbers in probabilitydescribe the concept and characteristics of central limit theorem and means and recognize common usage scenariosdescribe parameter estimation and distribution using Gaussiandescribe binomial distribution and its characteristicsrecall the arithmetic operations that can be applied on tensors, list the features of multivariate statistics that are applicable in machine learning, and implement Hadamard product on tensors using Python
IN THIS COURSE
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1.Course Overview1m 52sUP NEXT
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2.Matrix and PCA6m 22s
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3.Sparse Matrix9m 33s
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4.Tensor Arithmetic5m 26s
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5.Hadamard Product and Tensors3m 55s
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6.Singular-Value Decomposition5m 51s
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7.Reconstruct Rectangular Matrix Using SVD6m 48s
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8.Probability15m 2s
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9.Probability Basics and Propositions11m 47s
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10.Random Variable10m 3s
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11.Central Limit Theorem7m 36s
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12.Parameter Estimation and Gaussian Distribution6m 39s
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13.Binomial Distribution7m 17s
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14.Exercise: Tensor Arithmetic and Hadamard Product4m 44s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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