Math for Machine Learning

Watch Math for Machine Learning

  • 2018
  • 1 Season

Richard Han's Math for Machine Learning is an educational show hosted by Richard Han, an experienced machine learning engineer and tutor. The show aims to provide a comprehensive understanding of the mathematical foundations necessary to understand and excel in machine learning.

The show starts with an exploration of linear algebra, an essential branch of mathematics that is the foundation for many machine learning concepts. Richard delves into vector spaces, matrices, determinants, and eigenvalues to provide viewers with a deep understanding of these fundamental concepts.

Next, Richard moves onto calculus, where he discusses the principles of differentiation and integration. He takes the time to explain the different types of differentiation and how they can be used to optimize functions. He also introduces viewers to the concepts of optimization and gradient descent, which are important tools in machine learning.

Probability theory is another fundamental branch of mathematics discussed in the series. Richard provides an in-depth exploration of probability distribution, conditional probability, Bayes theorem, and random variables. He uses practical examples to demonstrate how these concepts are used in machine learning and data analysis.

Once viewers have a solid understanding of the mathematical foundations, Richard begins to discuss more advanced topics. These include statistical inference, which is the process of deducing properties of an underlying distribution from observations. Richard explains estimation techniques such as maximum likelihood and Bayesian estimation and their application in machine learning.

He also covers linear regression and logistic regression, two common supervised learning algorithms that play a crucial role in the field of data science. Richard explains how these algorithms work, what are their strengths and weaknesses, and how they can be used to solve real-life problems.

Richard then moves onto more advanced machine learning algorithms like decision trees and neural networks. He takes the time to explain the basic architecture of a neuron, the backpropagation algorithm, and how neural networks are trained. He also provides insights into different types of neural networks like CNNs, RNNs, and GANs, and their applications in image processing, speech recognition, and natural language processing.

Throughout the series, Richard takes a practical approach to teaching mathematical concepts. He uses real-life examples to illustrate concepts, so viewers can see how they are applied in machine learning. He also provides plenty of exercises and sample problems to give viewers the opportunity to practice and reinforce their understanding of the material.

Richard Han's Math for Machine Learning is an excellent resource for anyone interested in pursuing a career in machine learning or data analysis. The show provides a comprehensive and practical foundation in the mathematical concepts necessary to understand and excel in machine learning. Whether you are new to the field or an experienced practitioner, Richard Han's Math for Machine Learning is a must-see series that will take your skills to the next level.

Math for Machine Learning is a series that ran for 1 seasons (144 episodes) between May 2, 2018 and on Richard Han

Filter by Source
Do you have Apple TV?
What are you waiting for?
Nice! Browse Apple TV with Yidio.
Ad Info
Seasons
Summary: Support Vector Machine Classifier
72. Summary: Support Vector Machine Classifier
May 2, 2018
We review the support vector machine classifier.
Summary: Support Vector Machine Classifier
72. Summary: Support Vector Machine Classifier
May 2, 2018
We review the support vector machine classifier.
The Kernel Trick
71. The Kernel Trick
May 2, 2018
Students will learn how the support vector machine classifier works by using the kernel trick.
The Kernel Trick
71. The Kernel Trick
May 2, 2018
Students will learn how the support vector machine classifier works by using the kernel trick.
Enlarging the Feature Space
70. Enlarging the Feature Space
May 2, 2018
Students will see how the support vector machine basically works.
Enlarging the Feature Space
70. Enlarging the Feature Space
May 2, 2018
Students will see how the support vector machine basically works.
Section 7: Support Vector Machine Classifier (First Lecture: Support Vector Machine Classifier)
69. Section 7: Support Vector Machine Classifier (First Lecture: Support Vector Machine Classifier)
May 2, 2018
An introduction to the support vector machine classifier is provided. Practice problems available at onlinemathtraining.
Section 7: Support Vector Machine Classifier (First Lecture: Support Vector Machine Classifier)
69. Section 7: Support Vector Machine Classifier (First Lecture: Support Vector Machine Classifier)
May 2, 2018
An introduction to the support vector machine classifier is provided. Practice problems available at onlinemathtraining.
Summary: Support Vector Classifier
68. Summary: Support Vector Classifier
May 2, 2018
We review the support vector classifier.
Summary: Support Vector Classifier
68. Summary: Support Vector Classifier
May 2, 2018
We review the support vector classifier.
Support Vector Classifier Example 2
67. Support Vector Classifier Example 2
May 2, 2018
Students will see how the support vector classifier works in a second simple but specific example.
Support Vector Classifier Example 2
67. Support Vector Classifier Example 2
May 2, 2018
Students will see how the support vector classifier works in a second simple but specific example.
Support Vector Classifier Example 1
66. Support Vector Classifier Example 1
May 2, 2018
Students will see how the support vector classifier works in a simple but specific example.
Support Vector Classifier Example 1
66. Support Vector Classifier Example 1
May 2, 2018
Students will see how the support vector classifier works in a simple but specific example.
Classifying Test Points (Soft Margin)
65. Classifying Test Points (Soft Margin)
May 2, 2018
Students will learn how to classify test points using the support vector classifier.
Classifying Test Points (Soft Margin)
65. Classifying Test Points (Soft Margin)
May 2, 2018
Students will learn how to classify test points using the support vector classifier.
The Support Vectors (Soft Margin)
64. The Support Vectors (Soft Margin)
May 2, 2018
We define the support vectors for the support vector classifier.
The Support Vectors (Soft Margin)
64. The Support Vectors (Soft Margin)
May 2, 2018
We define the support vectors for the support vector classifier.
The Coefficients for the Soft Margin Hyperplane
63. The Coefficients for the Soft Margin Hyperplane
May 2, 2018
Students will learn how to find the coefficients for the soft margin hyperplane.
The Coefficients for the Soft Margin Hyperplane
63. The Coefficients for the Soft Margin Hyperplane
May 2, 2018
Students will learn how to find the coefficients for the soft margin hyperplane.
Solving the Convex Optimization Problem (Soft Margin)
62. Solving the Convex Optimization Problem (Soft Margin)
May 2, 2018
Students will learn how to solve the convex optimization problem using Lagrange multipliers.
Solving the Convex Optimization Problem (Soft Margin)
62. Solving the Convex Optimization Problem (Soft Margin)
May 2, 2018
Students will learn how to solve the convex optimization problem using Lagrange multipliers.
A Convex Optimization Problem
61. A Convex Optimization Problem
May 2, 2018
We identify the optimization problem as a convex optimization problem.
A Convex Optimization Problem
61. A Convex Optimization Problem
May 2, 2018
We identify the optimization problem as a convex optimization problem.
Definition of Support Vector Classifier
60. Definition of Support Vector Classifier
May 2, 2018
We define the support vector classifier.
Definition of Support Vector Classifier
60. Definition of Support Vector Classifier
May 2, 2018
We define the support vector classifier.
Formulating the Optimization Problem
59. Formulating the Optimization Problem
May 2, 2018
We formulate the optimization problem for the support vector classifier.
Formulating the Optimization Problem
59. Formulating the Optimization Problem
May 2, 2018
We formulate the optimization problem for the support vector classifier.
Slack Variables: Points on Wrong Side of Hyperplane
58. Slack Variables: Points on Wrong Side of Hyperplane
May 2, 2018
We characterize points on the wrong side of the hyperplane using slack variables.
Slack Variables: Points on Wrong Side of Hyperplane
58. Slack Variables: Points on Wrong Side of Hyperplane
May 2, 2018
We characterize points on the wrong side of the hyperplane using slack variables.
Slack Variables: Points on Correct Side of Hyperplane
57. Slack Variables: Points on Correct Side of Hyperplane
May 2, 2018
We characterize points on the correct side of the hyperplane using slack variables.
Slack Variables: Points on Correct Side of Hyperplane
57. Slack Variables: Points on Correct Side of Hyperplane
May 2, 2018
We characterize points on the correct side of the hyperplane using slack variables.
Section 6: Support Vector Classifier (First Lecture: Support Vector Classifier)
56. Section 6: Support Vector Classifier (First Lecture: Support Vector Classifier)
May 2, 2018
An introduction to the support vector classifier is provided. Practice problems available at onlinemathtraining.
Section 6: Support Vector Classifier (First Lecture: Support Vector Classifier)
56. Section 6: Support Vector Classifier (First Lecture: Support Vector Classifier)
May 2, 2018
An introduction to the support vector classifier is provided. Practice problems available at onlinemathtraining.
Summary: Maximal Margin Classifier
55. Summary: Maximal Margin Classifier
May 2, 2018
A summary of the maximal margin classifier is provided.
Summary: Maximal Margin Classifier
55. Summary: Maximal Margin Classifier
May 2, 2018
A summary of the maximal margin classifier is provided.
Maximal Margin Classifier Example 2
54. Maximal Margin Classifier Example 2
May 2, 2018
A second example of applying the maximal margin classifier is provided.
Maximal Margin Classifier Example 2
54. Maximal Margin Classifier Example 2
May 2, 2018
A second example of applying the maximal margin classifier is provided.
Maximal Margin Classifier Example 1
53. Maximal Margin Classifier Example 1
May 2, 2018
An example of applying the maximal margin classifier to solve a classification problem is provided.
Maximal Margin Classifier Example 1
53. Maximal Margin Classifier Example 1
May 2, 2018
An example of applying the maximal margin classifier to solve a classification problem is provided.
Classifying Test Points
52. Classifying Test Points
May 2, 2018
Students will learn how to classify test points.
Classifying Test Points
52. Classifying Test Points
May 2, 2018
Students will learn how to classify test points.
The Support Vectors
51. The Support Vectors
May 2, 2018
We define what support vectors are.
The Support Vectors
51. The Support Vectors
May 2, 2018
We define what support vectors are.
The Coefficients for the Maximal Margin Hyperplane
50. The Coefficients for the Maximal Margin Hyperplane
May 2, 2018
Students will learn how to solve for the coefficients for the maximal margin hyperplane.
The Coefficients for the Maximal Margin Hyperplane
50. The Coefficients for the Maximal Margin Hyperplane
May 2, 2018
Students will learn how to solve for the coefficients for the maximal margin hyperplane.
Solving the Dual Problem
49. Solving the Dual Problem
May 2, 2018
In this lecture, we solve the dual problem.
Solving the Dual Problem
49. Solving the Dual Problem
May 2, 2018
In this lecture, we solve the dual problem.
Primal and Dual Problems
48. Primal and Dual Problems
May 2, 2018
Students will learn what the primal and dual problems are.
Primal and Dual Problems
48. Primal and Dual Problems
May 2, 2018
Students will learn what the primal and dual problems are.
KKT Conditions
47. KKT Conditions
May 2, 2018
Students will learn what the KKT conditions are.
KKT Conditions
47. KKT Conditions
May 2, 2018
Students will learn what the KKT conditions are.
Solving the Convex Optimization Problem
46. Solving the Convex Optimization Problem
May 2, 2018
We introduce a strategy for solving the optimization problem.
Solving the Convex Optimization Problem
46. Solving the Convex Optimization Problem
May 2, 2018
We introduce a strategy for solving the optimization problem.
Proof 5 (Optional)
45. Proof 5 (Optional)
May 2, 2018
This is a supplementary resource for the lecture titled Reformulating the Optimization Problem.
Proof 5 (Optional)
45. Proof 5 (Optional)
May 2, 2018
This is a supplementary resource for the lecture titled Reformulating the Optimization Problem.
Proof 4 (Optional)
44. Proof 4 (Optional)
May 2, 2018
This is a supplementary resource for the lecture titled Reformulating the Optimization Problem.
Proof 4 (Optional)
44. Proof 4 (Optional)
May 2, 2018
This is a supplementary resource for the lecture titled Reformulating the Optimization Problem.
Proof 3 (Optional)
43. Proof 3 (Optional)
May 2, 2018
This is a supplementary resource for the lecture titled Reformulating the Optimization Problem.
Proof 3 (Optional)
43. Proof 3 (Optional)
May 2, 2018
This is a supplementary resource for the lecture titled Reformulating the Optimization Problem.
Proof 2 (Optional)
42. Proof 2 (Optional)
May 2, 2018
This is a supplementary resource for the lecture titled Reformulating the Optimization Problem.
Proof 2 (Optional)
42. Proof 2 (Optional)
May 2, 2018
This is a supplementary resource for the lecture titled Reformulating the Optimization Problem.
Reformulating the Optimization Problem
41. Reformulating the Optimization Problem
May 2, 2018
In this lecture, we reformulate the maximization problem as a convex optimization problem.
Reformulating the Optimization Problem
41. Reformulating the Optimization Problem
May 2, 2018
In this lecture, we reformulate the maximization problem as a convex optimization problem.
Definition of Maximal Margin Classifier
40. Definition of Maximal Margin Classifier
May 2, 2018
The maximal margin classifier is defined.
Definition of Maximal Margin Classifier
40. Definition of Maximal Margin Classifier
May 2, 2018
The maximal margin classifier is defined.
Maximizing the Margin
39. Maximizing the Margin
May 2, 2018
in this lecture, we formulate a maximization problem.
Maximizing the Margin
39. Maximizing the Margin
May 2, 2018
in this lecture, we formulate a maximization problem.
Proof 1 (Optional)
38. Proof 1 (Optional)
May 2, 2018
This is a supplementary resource for the lecture titled Definitions of Separating Hyperplane and Margin.
Proof 1 (Optional)
38. Proof 1 (Optional)
May 2, 2018
This is a supplementary resource for the lecture titled Definitions of Separating Hyperplane and Margin.
Definitions of Separating Hyperplane and Margin
37. Definitions of Separating Hyperplane and Margin
May 2, 2018
In this lecture, we provide definitions of separating hyperplane and margin.
Definitions of Separating Hyperplane and Margin
37. Definitions of Separating Hyperplane and Margin
May 2, 2018
In this lecture, we provide definitions of separating hyperplane and margin.
Section 5: Maximal Margin Classifier (First Lecture: Maximal Margin Classifier)
36. Section 5: Maximal Margin Classifier (First Lecture: Maximal Margin Classifier)
May 2, 2018
An introduction to maximal margin classifier, support vector classifier, and support vector machine is provided. Practice problems available at onlinemathtraining.
Section 5: Maximal Margin Classifier (First Lecture: Maximal Margin Classifier)
36. Section 5: Maximal Margin Classifier (First Lecture: Maximal Margin Classifier)
May 2, 2018
An introduction to maximal margin classifier, support vector classifier, and support vector machine is provided. Practice problems available at onlinemathtraining.
Summary: Artificial Neural Networks
35. Summary: Artificial Neural Networks
May 2, 2018
A summary of artificial neural networks is provided.
Summary: Artificial Neural Networks
35. Summary: Artificial Neural Networks
May 2, 2018
A summary of artificial neural networks is provided.
Summary of Backpropagation
34. Summary of Backpropagation
May 2, 2018
A summary of backpropagation is provided.
Summary of Backpropagation
34. Summary of Backpropagation
May 2, 2018
A summary of backpropagation is provided.
Backpropagation Equations
33. Backpropagation Equations
May 2, 2018
Students will learn how the backpropagation equations are used to help find the gradient of the error function.
Backpropagation Equations
33. Backpropagation Equations
May 2, 2018
Students will learn how the backpropagation equations are used to help find the gradient of the error function.
Minimizing the Error Function Using Gradient Descent
32. Minimizing the Error Function Using Gradient Descent
May 2, 2018
Students will learn how gradient descent is used to minimize the error function.
Minimizing the Error Function Using Gradient Descent
32. Minimizing the Error Function Using Gradient Descent
May 2, 2018
Students will learn how gradient descent is used to minimize the error function.
Error Function for Multiclass Classification
31. Error Function for Multiclass Classification
May 2, 2018
Students will learn which error function to use for multi-class classification problems.
Error Function for Multiclass Classification
31. Error Function for Multiclass Classification
May 2, 2018
Students will learn which error function to use for multi-class classification problems.
Error Function for Binary Classification
30. Error Function for Binary Classification
May 2, 2018
Students will learn which error function to use for binary classification problems.
Error Function for Binary Classification
30. Error Function for Binary Classification
May 2, 2018
Students will learn which error function to use for binary classification problems.
Error Function for Regression
29. Error Function for Regression
May 2, 2018
Students will learn which error function to use for regression problems.
Error Function for Regression
29. Error Function for Regression
May 2, 2018
Students will learn which error function to use for regression problems.
Estimating the Output Functions
28. Estimating the Output Functions
May 2, 2018
We introduce a strategy for estimating the output functions.
Estimating the Output Functions
28. Estimating the Output Functions
May 2, 2018
We introduce a strategy for estimating the output functions.
Choosing Activation Functions
27. Choosing Activation Functions
May 2, 2018
Students will learn which activation functions to choose for each type of problem.
Choosing Activation Functions
27. Choosing Activation Functions
May 2, 2018
Students will learn which activation functions to choose for each type of problem.
Forward Propagation
26. Forward Propagation
May 2, 2018
The notion of forward propagation is discussed.
Forward Propagation
26. Forward Propagation
May 2, 2018
The notion of forward propagation is discussed.
Neural Network Model of the Output Functions
25. Neural Network Model of the Output Functions
May 2, 2018
In this lecture, we build a neural network model for the output functions using a neural network diagram.
Neural Network Model of the Output Functions
25. Neural Network Model of the Output Functions
May 2, 2018
In this lecture, we build a neural network model for the output functions using a neural network diagram.
Section 4: Artificial Neural Networks (First Lecture: Artificial Neural Networks)
24. Section 4: Artificial Neural Networks (First Lecture: Artificial Neural Networks)
May 2, 2018
An introduction to artificial neural networks is provided. Practice problems available at onlinemathtraining.
Section 4: Artificial Neural Networks (First Lecture: Artificial Neural Networks)
24. Section 4: Artificial Neural Networks (First Lecture: Artificial Neural Networks)
May 2, 2018
An introduction to artificial neural networks is provided. Practice problems available at onlinemathtraining.
Summary: Logistic Regression
23. Summary: Logistic Regression
May 2, 2018
A summary of logistic regression is provided.
Summary: Logistic Regression
23. Summary: Logistic Regression
May 2, 2018
A summary of logistic regression is provided.
Example: Logistic Regression
22. Example: Logistic Regression
May 2, 2018
Students will learn how to apply logistic regression to solve a classification problem.
Example: Logistic Regression
22. Example: Logistic Regression
May 2, 2018
Students will learn how to apply logistic regression to solve a classification problem.
Maximizing the Log-Likelihood Function
21. Maximizing the Log-Likelihood Function
May 2, 2018
In this lecture, we apply the multivariate Newton-Raphson method to the log-likelihood function and learn about iterative reweighted least squares.
Maximizing the Log-Likelihood Function
21. Maximizing the Log-Likelihood Function
May 2, 2018
In this lecture, we apply the multivariate Newton-Raphson method to the log-likelihood function and learn about iterative reweighted least squares.
The Multivariate Newton-Raphson Method
20. The Multivariate Newton-Raphson Method
May 2, 2018
Students will learn how the Multivariate Newton-Raphson method is used to maximize a function.
The Multivariate Newton-Raphson Method
20. The Multivariate Newton-Raphson Method
May 2, 2018
Students will learn how the Multivariate Newton-Raphson method is used to maximize a function.
Estimating the Posterior Probability Function
19. Estimating the Posterior Probability Function
May 2, 2018
In this lecture, we introduce a strategy for estimating the posterior probability function.
Estimating the Posterior Probability Function
19. Estimating the Posterior Probability Function
May 2, 2018
In this lecture, we introduce a strategy for estimating the posterior probability function.
Logistic Regression Model of the Posterior Probability Function
18. Logistic Regression Model of the Posterior Probability Function
May 2, 2018
In this lecture, we model the posterior probability function.
Logistic Regression Model of the Posterior Probability Function
18. Logistic Regression Model of the Posterior Probability Function
May 2, 2018
In this lecture, we model the posterior probability function.
Section 3: Logistic Regression (First Lecture: Logistic Regression)
17. Section 3: Logistic Regression (First Lecture: Logistic Regression)
May 2, 2018
The method of logistic regression is introduced. Practice problems available at onlinemathtraining.
Section 3: Logistic Regression (First Lecture: Logistic Regression)
17. Section 3: Logistic Regression (First Lecture: Logistic Regression)
May 2, 2018
The method of logistic regression is introduced. Practice problems available at onlinemathtraining.
Summary: Linear Discriminant Analysis
16. Summary: Linear Discriminant Analysis
May 2, 2018
A summary of linear discriminant analysis is provided.
Summary: Linear Discriminant Analysis
16. Summary: Linear Discriminant Analysis
May 2, 2018
A summary of linear discriminant analysis is provided.
LDA Example 2
15. LDA Example 2
May 2, 2018
Another example of applying linear discriminant analysis is provided.
LDA Example 2
15. LDA Example 2
May 2, 2018
Another example of applying linear discriminant analysis is provided.
LDA Example 1
14. LDA Example 1
May 2, 2018
Students will see an example of applying linear discriminant analysis.
LDA Example 1
14. LDA Example 1
May 2, 2018
Students will see an example of applying linear discriminant analysis.
Classifying Data Points Using Linear Discriminant Functions
13. Classifying Data Points Using Linear Discriminant Functions
May 2, 2018
Students will learn how to classify data points using linear discriminant functions.
Classifying Data Points Using Linear Discriminant Functions
13. Classifying Data Points Using Linear Discriminant Functions
May 2, 2018
Students will learn how to classify data points using linear discriminant functions.
Estimating the Linear Discriminant Functions
12. Estimating the Linear Discriminant Functions
May 2, 2018
In this lecture, we estimate the linear discriminant functions.
Estimating the Linear Discriminant Functions
12. Estimating the Linear Discriminant Functions
May 2, 2018
In this lecture, we estimate the linear discriminant functions.
Linear Discriminant Functions
11. Linear Discriminant Functions
May 2, 2018
Students will learn what linear discriminant functions are.
Linear Discriminant Functions
11. Linear Discriminant Functions
May 2, 2018
Students will learn what linear discriminant functions are.
Modelling the Posterior Probability Functions
10. Modelling the Posterior Probability Functions
May 2, 2018
In this lecture, we model the posterior probability functions.
Modelling the Posterior Probability Functions
10. Modelling the Posterior Probability Functions
May 2, 2018
In this lecture, we model the posterior probability functions.
The Posterior Probability Functions
9. The Posterior Probability Functions
May 2, 2018
In this lecture, we build a formula for the posterior probability.
The Posterior Probability Functions
9. The Posterior Probability Functions
May 2, 2018
In this lecture, we build a formula for the posterior probability.
Linear Discriminant Analysis
8. Linear Discriminant Analysis
May 2, 2018
The method of linear discriminant analysis is introduced.
Linear Discriminant Analysis
8. Linear Discriminant Analysis
May 2, 2018
The method of linear discriminant analysis is introduced.
Section 2: Linear Discriminant Analysis (First Lecture: Classification)
7. Section 2: Linear Discriminant Analysis (First Lecture: Classification)
May 2, 2018
Students will be introduced to classification problems. Practice problems available at onlinemathtraining.
Section 2: Linear Discriminant Analysis (First Lecture: Classification)
7. Section 2: Linear Discriminant Analysis (First Lecture: Classification)
May 2, 2018
Students will be introduced to classification problems. Practice problems available at onlinemathtraining.
Summary: Linear Regression
6. Summary: Linear Regression
May 2, 2018
A summary of linear regression is provided.
Summary: Linear Regression
6. Summary: Linear Regression
May 2, 2018
A summary of linear regression is provided.
Example: Linear Regression
5. Example: Linear Regression
May 2, 2018
An example of applying the least squares method is provided.
Example: Linear Regression
5. Example: Linear Regression
May 2, 2018
An example of applying the least squares method is provided.
Linear Algebra Solution to Least Squares Problem
4. Linear Algebra Solution to Least Squares Problem
May 2, 2018
Students will learn about a linear algebra approach to solving the least squares problem.
Linear Algebra Solution to Least Squares Problem
4. Linear Algebra Solution to Least Squares Problem
May 2, 2018
Students will learn about a linear algebra approach to solving the least squares problem.
The Least Squares Method
3. The Least Squares Method
May 2, 2018
Students will learn how to apply the least squares method to solve the least squares problem.
The Least Squares Method
3. The Least Squares Method
May 2, 2018
Students will learn how to apply the least squares method to solve the least squares problem.
Section 1: Linear Regression (First Lecture: Linear Regression)
2. Section 1: Linear Regression (First Lecture: Linear Regression)
May 2, 2018
Students will learn about the notion of residual sum of squares. Practice problems available at onlinemathtraining.
Section 1: Linear Regression (First Lecture: Linear Regression)
2. Section 1: Linear Regression (First Lecture: Linear Regression)
May 2, 2018
Students will learn about the notion of residual sum of squares. Practice problems available at onlinemathtraining.
Introduction Lecture (1 of 72 Lectures)
1. Introduction Lecture (1 of 72 Lectures)
May 2, 2018
An introduction to the course is provided. Practice problems available at onlinemathtraining.
Introduction Lecture (1 of 72 Lectures)
1. Introduction Lecture (1 of 72 Lectures)
May 2, 2018
An introduction to the course is provided. Practice problems available at onlinemathtraining.
Description
Where to Watch Math for Machine Learning
Math for Machine Learning is available for streaming on the Richard Han website, both individual episodes and full seasons. You can also watch Math for Machine Learning on demand at Amazon.
  • Premiere Date
    May 2, 2018
  • All Your TV All your TV. All in one App.
  • Easily Find What You want Easily find what you want to watch.
  • Already On Your Devices Already on your favorite devices.
Ad Info
If You Like Math for Machine Learning, Then Try...
Calculus 1 for Beginners: Open Doors to Great Careers
Calculus 1 For Beginners: Open Doors To Great Careers
Linear Algebra for Beginners: Open Doors to Great Careers
Linear Algebra For Beginners: Open Doors To Great Careers
Top TV Shows
Time Bandits
Time Bandits
Sunny
Sunny
Love & Hip Hop: Atlanta
Love & Hip Hop: Atlanta
Power Book II: Ghost
Power Book II: Ghost
The Bachelorette
The Bachelorette
Euphoria
Euphoria
Learning Network Penetration Testing with Kali Linux
Learning Network Penetration Testing With Kali Linux
The Late Show with Stephen Colbert
The Late Show With Stephen Colbert
The Vampire Diaries
The Vampire Diaries
Bel-Air
Bel-Air
Real Time with Bill Maher
Real Time With Bill Maher
Watch shows on Apple TV+
Cowboy Cartel
Cowboy Cartel
Bad Monkey
Bad Monkey
Presumed Innocent
Presumed Innocent
Slow Horses
Slow Horses
Lady in the Lake
Lady In The Lake
Ted Lasso
Ted Lasso
SEE
SEE
Swagger
Swagger
Hijack
Hijack
Masters of the Air
Masters Of The Air
Pachinko
Pachinko