Math 0-1: Calculus for Data Science & Machine Learning

  • Welcome
  • Introduction
Introduction
04:01
Outline
07:25
How to Succeed in this Course
08:45
Where to get the code
02:06
Functions Review
25:34
Functions Review in Python
11:27
What Are Limits?
14:30
Precise Definition of Limit (Optional)
07:13
Limit Laws
04:35
Infinities and Asymptotes
06:49
Indeterminate Forms
12:32
Continuity
10:20
Limits in Python
08:01
Limits with Plotting in Python
03:35
Limits Section Summary
03:26
Suggestion Box
03:10
Slopes, Tangent Lines, and Derivatives
20:56
More On Tangent Lines, Derivative Checking
14:10
Exercise: Quadratic
03:35
Exercise: Cubic
03:51
Exercise: Reciprocal
03:25
Exercise: Root
05:58
Alternate Notations & Higher Order Derivatives
08:42
Derivative Checking in Python
03:03
Derivatives Section Summary
04:19
Power Rule
11:50
Constant Multiple, Addition, Subtraction Rules
09:52
Exponent Rule
08:39
Exponent Rule (continued)
07:08
Exponent Rule (even more)
05:30
Chain Rule
21:46
Exercises: Chain Rule
10:45
Product and Quotient Rules
19:45
Exercises: Product and Quotient Rules
12:41
Implicit Differentiation
10:08
Logarithm Rule
07:33
Implicit Differentiation Applications
07:13
Logarithmic Differentiation
07:55
Exercise: Derivatives of Hyperbolic Functions
08:53
Exercise: Sum of Polynomials
08:10
Exercise: Gaussian Variance
07:30
Exercise: Entropy
06:47
Trigonometric Functions (Optional)
11:50
Inverse Trigonometric Functions (Optional)
09:30
Derivative Rules Section Summary
04:26
Finding the Minimum / Maximum
12:21
Minimum / Maximum Clarifications and Examples
09:52
Second Derivative Test
03:59
Exercise: Minimums and Maximums
05:33
Exercise: Entropy
06:31
Exercise: Gaussian 1
08:40
Exercise: Gaussian 2
06:38
Why Does Taking the Log Work? (VIP only)
07:51
l'Hopital's Rule
06:40
Newton's Method
08:57
Newton's Method in Python
08:41
Derivative Applications Section Summary
02:49
Integrals: Section Introduction
06:39
Area Under Curve
10:56
Fundamental Theorem of Calculus (pt 1)
22:03
Fundamental Theorem of Calculus (pt 2)
08:01
Definite and Indefinite Integrals
07:22
Exercises: Definite Integrals
14:38
Exercises: Indefinite Integrals
14:16
Exercises: Improper Integrals
14:00
Numerical Integration in Python
06:58
Integration Section Summary
02:55
Functions of Multiple Variables
12:45
Partial Differentiation
20:02
The Gradient
20:01
Exercise: Gradient of Log
03:09
The Jacobian and Hessian
16:01
Differentials and Chain Rule in Multiple Dimensions
14:50
Exercise: Chain Rule
11:29
Exercise: Softmax
15:17
Why is the Gradient the Direction of Steepest Ascent?
12:41
Steepest Ascent in Python
09:28
Optimization and Lagrange Multipliers (pt 1)
24:36
Optimization and Lagrange Multipliers (pt 2)
16:49
Exercise: Linear Regression
17:27
Vector Calculus Section Summary
07:58
Infinite Series and Taylor Expansion
22:22
Polynomial Regression
02:42
Taylor Expansion Examples
17:08
Taylor Expansion in Python
05:32
Taylor Expansion in Multiple Dimensions
11:53
Application: Derivative Approximations Revisited
10:03
Taylor Expansion Section Summary
03:25
What is the Appendix?
03:47
Pre-Installation Check
04:13
Anaconda Environment Setup
20:21
How to install Numpy, Scipy, Matplotlib, Pandas, PyTorch, and TensorFlow
17:33
How to use Github & Extra Coding Tips (Optional)
11:12
Math Order for Machine Learning & Data Science
16:20
Can YouTube Teach Me Calculus? (Optional)
15:08
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
22:05
What order should I take your courses in? (part 1)
11:19
What order should I take your courses in? (part 2)
16:07
Where to get discount coupons and FREE deep learning material
05:49
Introduction
04:01
Outline
07:25
How to Succeed in this Course
08:45
Where to get the code
02:06
Functions Review
25:34
Functions Review in Python
11:27
What Are Limits?
14:30
Precise Definition of Limit (Optional)
07:13
Limit Laws
04:35
Infinities and Asymptotes
06:49
Indeterminate Forms
12:32
Continuity
10:20
Limits in Python
08:01
Limits with Plotting in Python
03:35
Limits Section Summary
03:26
Suggestion Box
03:10
Slopes, Tangent Lines, and Derivatives
20:56
More On Tangent Lines, Derivative Checking
14:10
Exercise: Quadratic
03:35
Exercise: Cubic
03:51
Exercise: Reciprocal
03:25
Exercise: Root
05:58
Alternate Notations & Higher Order Derivatives
08:42
Derivative Checking in Python
03:03
Derivatives Section Summary
04:19
Power Rule
11:50
Constant Multiple, Addition, Subtraction Rules
09:52
Exponent Rule
08:39
Exponent Rule (continued)
07:08
Exponent Rule (even more)
05:30
Chain Rule
21:46
Exercises: Chain Rule
10:45
Product and Quotient Rules
19:45
Exercises: Product and Quotient Rules
12:41
Implicit Differentiation
10:08
Logarithm Rule
07:33
Implicit Differentiation Applications
07:13
Logarithmic Differentiation
07:55
Exercise: Derivatives of Hyperbolic Functions
08:53
Exercise: Sum of Polynomials
08:10
Exercise: Gaussian Variance
07:30
Exercise: Entropy
06:47
Trigonometric Functions (Optional)
11:50
Inverse Trigonometric Functions (Optional)
09:30
Derivative Rules Section Summary
04:26
Finding the Minimum / Maximum
12:21
Minimum / Maximum Clarifications and Examples
09:52
Second Derivative Test
03:59
Exercise: Minimums and Maximums
05:33
Exercise: Entropy
06:31
Exercise: Gaussian 1
08:40
Exercise: Gaussian 2
06:38
Why Does Taking the Log Work? (VIP only)
07:51
l'Hopital's Rule
06:40
Newton's Method
08:57
Newton's Method in Python
08:41
Derivative Applications Section Summary
02:49
Integrals: Section Introduction
06:39
Area Under Curve
10:56
Fundamental Theorem of Calculus (pt 1)
22:03
Fundamental Theorem of Calculus (pt 2)
08:01
Definite and Indefinite Integrals
07:22
Exercises: Definite Integrals
14:38
Exercises: Indefinite Integrals
14:16
Exercises: Improper Integrals
14:00
Numerical Integration in Python
06:58
Integration Section Summary
02:55
Functions of Multiple Variables
12:45
Partial Differentiation
20:02
The Gradient
20:01
Exercise: Gradient of Log
03:09
The Jacobian and Hessian
16:01
Differentials and Chain Rule in Multiple Dimensions
14:50
Exercise: Chain Rule
11:29
Exercise: Softmax
15:17
Why is the Gradient the Direction of Steepest Ascent?
12:41
Steepest Ascent in Python
09:28
Optimization and Lagrange Multipliers (pt 1)
24:36
Optimization and Lagrange Multipliers (pt 2)
16:49
Exercise: Linear Regression
17:27
Vector Calculus Section Summary
07:58
Infinite Series and Taylor Expansion
22:22
Polynomial Regression
02:42
Taylor Expansion Examples
17:08
Taylor Expansion in Python
05:32
Taylor Expansion in Multiple Dimensions
11:53
Application: Derivative Approximations Revisited
10:03
Taylor Expansion Section Summary
03:25
What is the Appendix?
03:47
Pre-Installation Check
04:13
Anaconda Environment Setup
20:21
How to install Numpy, Scipy, Matplotlib, Pandas, PyTorch, and TensorFlow
17:33
How to use Github & Extra Coding Tips (Optional)
11:12
Math Order for Machine Learning & Data Science
16:20
Can YouTube Teach Me Calculus? (Optional)
15:08
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
22:05
What order should I take your courses in? (part 1)
11:19
What order should I take your courses in? (part 2)
16:07
Where to get discount coupons and FREE deep learning material
05:49

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