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

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Lazy Programmer Inc.,Lazy Programmer Team

11:39:47

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1 - Introduction and Outline

1 - Introduction.mp4

04:00

2 - Outline.mp4

07:25

3 - How to Succeed in this Course.mp4

08:45

4 - Github Link.txt

4 - Where to Get the Code.mp4

03:42

2 - Review

5 - Functions Review.mp4

25:34

6 - Functions Review in Python.mp4

11:27

3 - Limits

7 - What Are Limits.mp4

14:30

8 - Precise Definition of Limit Optional.mp4

07:13

9 - Limit Laws.mp4

04:35

10 - Infinities and Asymptotes.mp4

06:49

11 - Indeterminate Forms.mp4

12:32

12 - Limits in Python.mp4

08:01

13 - Limits with Plotting in Python.mp4

03:35

4 - Derivatives From First Principles

14 - Slopes Tangent Lines and Derivatives.mp4

20:56

15 - More On Tangent Lines Derivative Checking.mp4

14:10

16 - Exercise Quadratic.mp4

03:35

17 - Exercise Cubic.mp4

03:51

18 - Exercise Reciprocal.mp4

03:25

19 - Exercise Root.mp4

05:58

20 - Alternate Notations Higher Order Derivatives.mp4

08:42

21 - Derivative Checking in Python.mp4

03:03

5 - Derivative Rules

22 - Power Rule.mp4

11:50

23 - Constant Multiple Addition Subtraction Rules.mp4

09:52

24 - Exponent Rule.mp4

08:39

25 - Exponent Rule continued.mp4

07:08

26 - Chain Rule.mp4

21:46

27 - Exercises Chain Rule.mp4

10:45

28 - Product and Quotient Rules.mp4

19:45

29 - Exercises Product and Quotient Rules.mp4

12:41

30 - Implicit Differentiation.mp4

10:08

31 - Logarithm Rule.mp4

07:33

32 - Implicit Differentiation Applications.mp4

07:13

33 - Logarithmic Differentiation.mp4

07:55

34 - Exercise Derivatives of Hyperbolic Functions.mp4

08:53

35 - Exercise Sum of Polynomials.mp4

08:10

36 - Exercise Gaussian Variance.mp4

07:30

37 - Exercise Entropy.mp4

06:47

38 - Trigonometric Functions Optional.mp4

11:50

39 - Inverse Trigonometric Functions Optional.mp4

09:30

6 - Applications of Differentiation

40 - Finding the Minimum Maximum.mp4

12:21

41 - Minimum Maximum Clarifications and Examples.mp4

09:52

42 - Second Derivative Test.mp4

03:59

43 - Exercise Minimums and Maximums.mp4

05:33

44 - Exercise Entropy.mp4

06:31

45 - Exercise Gaussian 1.mp4

08:40

46 - Exercise Gaussian 2.mp4

06:38

47 - lHopitals Rule.mp4

06:40

48 - Newtons Method.mp4

08:57

49 - Newtons Method in Python.mp4

08:41

7 - Integration Calculus 2

50 - Integrals Section Introduction.mp4

06:39

51 - Area Under Curve.mp4

10:56

52 - Fundamental Theorem of Calculus pt 1.mp4

22:03

53 - Fundamental Theorem of Calculus pt 2.mp4

08:01

54 - Definite and Indefinite Integrals.mp4

07:22

55 - Exercises Definite Integrals.mp4

14:38

56 - Exercises Indefinite Integrals.mp4

14:16

57 - Exercises Improper Integrals.mp4

14:00

58 - Numerical Integration in Python.mp4

06:58

8 - Vector Calculus in Multiple Dimensions Calculus 3

59 - Functions of Multiple Variables.mp4

12:45

60 - Partial Differentiation.mp4

20:02

61 - The Gradient.mp4

20:01

62 - The Jacobian and Hessian.mp4

16:07

63 - Differentials and Chain Rule in Multiple Dimensions.mp4

14:50

64 - Why is the Gradient the Direction of Steepest Ascent.mp4

12:41

65 - Steepest Ascent in Python.mp4

09:28

66 - Optimization and Lagrange Multipliers pt 1.mp4

24:36

67 - Optimization and Lagrange Multipliers pt 2.mp4

16:49

Description

A Casual Guide for Artificial Intelligence, Deep Learning, and Python Programmers

What You'll Learn?

Limits, limit definition of derivative, derivatives from first principles
Derivative rules (chain rule, product rule, quotient rule, implicit differentiation)
Integration, area under curve, fundamental theorem of calculus
Vector calculus, partial derivatives, gradient, Jacobian, Hessian, steepest ascent
Optimize (maximize or minimize) a function
l'Hopital's Rule
Newton's Method

Who is this for?

Anyone who wants to learn calculus quickly

Students and professionals interested in machine learning and data science but who've gotten stuck on the math

More details

Description
Common scenario: You try to get into machine learning and data science, but there's SOÂ MUCHÂ MATH.
Either you never studied this math, or you studied it so long ago you've forgotten it all.
What do you do?
Well my friends, that is why I created this course.
Calculus is one of the most important math prerequisites for machine learning. It's required to understand probability and statistics, which form the foundation of data science. Backpropagation, the learning algorithm behind deep learning and neural networks, is really just calculus with a fancy name.
If you want to do machine learning beyond just copying library code from blogs and tutorials, you must know calculus.
Normally, calculus is split into 3 courses, which takes about 1.5 years to complete.
Luckily, I've refined these teachings into just the essentials, so that you can learn everything you need to know on the scale of hours instead of years.
This course will cover Calculus 1 (limits, derivatives, and the most important derivative rules), Calculus 2 (integration), and Calculus 3 (vector calculus). It will even include machine learning-focused material you wouldn't normally see in a regular college course. We will even demonstrate many of the concepts in this course using the Python programming language (don't worry, you don't need to know Python for this course). In other words, instead of the dry old college version of calculus, this course takes just the most practical and impactful topics, and provides you with skills directly applicable to machine learning and data science, so you can start applying them today.
Are you ready?
Let's go!

Suggested prerequisites:
Firm understanding of high school math (functions, algebra, trigonometry)
Who this course is for:
Anyone who wants to learn calculus quickly
Students and professionals interested in machine learning and data science but who've gotten stuck on the math

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Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering.Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

Lazy Programmer Team

Instructor's Courses

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.I received my first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering.Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School.Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

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