Mathematical Foundations of Machine Learning

Name: Mathematical Foundations of Machine Learning
Price: 9 USD
Availability: InStock

16h 25m 26s

English

Paid

September 4, 2024

Course description

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.

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#1: What Linear Algebra Is

All Course Lessons (112)

#	Lesson Title	Duration
1	What Linear Algebra Is Demo	23:30
2	Plotting a System of Linear Equations	09:19
3	Linear Algebra Exercise	05:07
4	Tensors	02:34
5	Scalars	13:05
6	Vectors and Vector Transposition	12:20
7	Norms and Unit Vectors	14:38
8	Basis, Orthogonal, and Orthonormal Vectors	04:31
9	Matrix Tensors	08:24
10	Generic Tensor Notation	06:44
11	Exercises on Algebra Data Structures	02:08
12	Segment Intro	01:20
13	Tensor Transposition	03:53
14	Basic Tensor Arithmetic, incl. the Hadamard Product	06:13
15	Tensor Reduction	03:32
16	The Dot Product	05:14
17	Exercises on Tensor Operations	02:39
18	Solving Linear Systems with Substitution	09:48
19	Solving Linear Systems with Elimination	11:48
20	Visualizing Linear Systems	11:00
21	Segment Intro	02:06
22	The Frobenius Norm	05:02
23	Matrix Multiplication	24:29
24	Symmetric and Identity Matrices	04:42
25	Matrix Multiplication Exercises	07:22
26	Matrix Inversion	17:07
27	Diagonal Matrices	03:26
28	Orthogonal Matrices	05:17
29	Orthogonal Matrix Exercises	15:00
30	Segment Intro	17:53
31	Applying Matrices	07:32
32	Affine Transformations	18:21
33	Eigenvectors and Eigenvalues	26:14
34	Matrix Determinants	08:05
35	Determinants of Larger Matrices	08:42
36	Determinant Exercises	04:42
37	Determinants and Eigenvalues	15:44
38	Eigendecomposition	12:16
39	Eigenvector and Eigenvalue Applications	12:30
40	Segment Intro	03:22
41	Singular Value Decomposition	10:50
42	Data Compression with SVD	11:00
43	The Moore-Penrose Pseudoinverse	12:24
44	Regression with the Pseudoinverse	18:25
45	The Trace Operator	04:37
46	Principal Component Analysis (PCA)	08:28
47	Resources for Further Study of Linear Algebra	05:38
48	Segment Intro	03:40
49	Intro to Differential Calculus	13:26
50	Intro to Integral Calculus	02:25
51	The Method of Exhaustion	06:46
52	Calculus of the Infinitesimals	09:34
53	Calculus Applications	08:36
54	Calculating Limits	17:50
55	Exercises on Limits	06:07
56	Segment Intro	01:17
57	The Delta Method	15:47
58	How Derivatives Arise from Limits	13:53
59	Derivative Notation	04:20
60	The Derivative of a Constant	01:30
61	The Power Rule	01:17
62	The Constant Multiple Rule	03:11
63	The Sum Rule	02:27
64	Exercises on Derivative Rules	11:09
65	The Product Rule	03:51
66	The Quotient Rule	04:05
67	The Chain Rule	06:46
68	Advanced Exercises on Derivative Rules	11:49
69	The Power Rule on a Function Chain	04:38
70	Segment Intro	01:50
71	What Automatic Differentiation Is	04:43
72	Autodiff with PyTorch	06:18
73	Autodiff with TensorFlow	03:53
74	The Line Equation as a Tensor Graph	19:42
75	Machine Learning with Autodiff	40:12
76	Segment Intro	22:39
77	What Partial Derivatives Are	29:23
78	Partial Derivative Exercises	06:16
79	Calculating Partial Derivatives with Autodiff	05:19
80	Advanced Partial Derivatives	14:40
81	Advanced Partial-Derivative Exercises	06:12
82	Partial Derivative Notation	02:28
83	The Chain Rule for Partial Derivatives	09:18
84	Exercises on the Multivariate Chain Rule	05:19
85	Point-by-Point Regression	15:25
86	The Gradient of Quadratic Cost	15:17
87	Descending the Gradient of Cost	12:53
88	The Gradient of Mean Squared Error	24:22
89	Backpropagation	06:00
90	Higher-Order Partial Derivatives	11:54
91	Exercise on Higher-Order Partial Derivatives	02:56
92	Segment Intro	02:45
93	Binary Classification	09:14
94	The Confusion Matrix	02:30
95	The Receiver-Operating Characteristic (ROC) Curve	09:43
96	What Integral Calculus Is	06:15
97	The Integral Calculus Rules	05:38
98	Indefinite Integral Exercises	02:59
99	Definite Integrals	06:48
100	Numeric Integration with Python	04:52
101	Definite Integral Exercise	04:25
102	Finding the Area Under the ROC Curve	03:36
103	Resources for the Further Study of Calculus	04:02
104	Congratulations!	01:56
105	Probability & Information Theory	07:40
106	A Brief History of Probability Theory	03:37
107	What Probability Theory Is	05:16
108	Events and Sample Spaces	08:36
109	Multiple Independent Observations	08:03
110	Combinatorics	06:48
111	Exercises on Event Probabilities	09:57
112	More Lectures are on their Way!	00:22

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