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

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

Generative AI
4.7/5
$39.99
$199.99
80% OFF!
  • All levels
  • 98 Lectures
  • 16h 44m
  • English
  • Lifetime access, certificate of completion (shareable on LinkedIn, Facebook, and Twitter), Q&A forum, subtitles in English
Login or signup to
register for this course

Course Description

Common scenario: You try to get into machine learning and data science, but there's SO MUCH 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)

Lectures

  • 12 sections
  • 98 lectures
  • 16h 44m total length
Introduction
Preview
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
Taylor Expansion Section Summary
03:25
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
What is the Appendix?
02:48
Where to get discount coupons and FREE deep learning material
05:49
PDF Notes

Reviews

4.7

1,282 reviews for this course

5 Stars
(63%)
4 Stars
(36%)
3 Stars
(1%)
2 Stars
(0%)
1 Stars
(0%)

Testimonials and Success Stories

student-avatar

H. Z.

Machine Learning Research Scientist
flag-usa
United States

“I am one of your students. Yesterday, I presented my paper at ICCV 2019. You have a significant part in this, so I want to sincerely thank you for your in-depth guidance to the puzzle of deep learning. Please keep making awesome courses that teach us!”

5.0
student-avatar

Wade J.

Data Scientist
flag-usa
United States

“I just watched your short video on “Predicting Stock Prices with LSTMs: One Mistake Everyone Makes.” Giggled with delight.

You probably already know this, but some of us really and truly appreciate you. BTW, I spent a reasonable amount of time making a learning roadmap based on your courses and have started the journey.

Looking forward to your new stuff.”

5.0
student-avatar

Kris M.

Data Scientist
flag-usa
United States

“Thank you for doing this! I wish everyone who call’s themselves a Data Scientist would take the time to do this either as a refresher or learn the material. I have had to work with so many people in prior roles that wanted to jump right into machine learning on my teams and didn’t even understand the first thing about the basics you have in here!!

I am signing up so that I have the easy refresh when needed and the see what you consider important, as well as to support your great work, thank you.”

5.0
student-avatar

Steve M.

Machine Learning Research Scientist
flag-usa
United States

“I have been intending to send you an email expressing my gratitude for the work that you have done to create all of these data science courses in Machine Learning and Artificial Intelligence. I have been looking long and hard for courses that have mathematical rigor relative to the application of the ML & AI algorithms as opposed to just exhibit some 'canned routine' and then viola here is your neural network or logistical regression.

Your courses are just what I have been seeking. I am a retired mathematician, statistician and Supply Chain executive from a large Fortune 500 company in Ohio. I also taught mathematics, statistics and operations research courses at a couple of universities in Northern Ohio.

I have taken many courses and have enjoyed the journey, I am not going to be critical of any of the organizations from whom I have taken courses. However, when I read a review about one of your courses in which the student was complaining that one would need a PhD in Mathematics to understand it, I knew this was the course (or series of courses) that I wanted. (Having advanced degrees in mathematics, I knew that it was highly unlikely that a PhD would actually be required.)”

5.0
student-avatar

Saurabh W.

Data Scientist
flag-india
India

“Hi Sir I am a student from India. I've been wanting to write a note to thank you for the courses that you've made because they have changed my career. I wanted to work in the field of data science but I was not having proper guidance but then I stumbled upon your "Logistic Regression" course in March and since then, there's been no looking back. I learned ANNs, CNNs, RNNs, Tensorflow, NLP and whatnot by going through your lectures. The knowledge that I gained enabled me to get a job as a Business Technology Analyst at one of my dream firms even in the midst of this pandemic. For that, I shall always be grateful to you. Please keep making more courses with the level of detail that you do in low-level libraries like Theano.”

5.0
student-avatar

David P.

Financial Analyst
flag-usa
United States

“I just wanted to reach out and thank you for your most excellent course that I am nearing finishing.

And, I couldn't agree more with some of your "rants", and found myself nodding vigorously!

You are an excellent teacher, and a rare breed.

And, your courses are frankly, more digestible and teach a student far more than some of the top-tier courses from ivy leagues I have taken in the past.

(I plan to go through many more courses, one by one!)

I know you must be deluged with complaints in spite of the best content around That's just human nature.

Also, satisfied people rarely take the time to write, so I thought I will write in for a change. :)”

5.0
student-avatar

P. C.

Deep Learning Research Scientist
flag-china
China

“Hello, Lazy Programmer!

In the process of completing my Master’s at Hunan University, China, I am writing this feedback to you in order to express my deep gratitude for all the knowledge and skills I have obtained studying your courses and following your recommendations.

The first course of yours I took was on Convolutional Neural Networks (“Deep Learning p.5”, as far as I remember). Answering one of my questions on the Q&A board, you suggested I should start from the beginning – the Linear and Logistic Regression courses. Despite that I assumed I had already known many basic things at that time, I overcame my “pride” and decided to start my journey in Deep Learning from scratch.

Course by course, I was renewing the basics and the prerequisites. Thus, in several months, after every day studying under your guidance, I was able to gain enough intuitions and practical skills in order to begin progressing in my research. Having a solid background, it was just a pleasure to read all the relevant papers in the field as well as to make all the experiments needed for achieving my goal – creating a high-performance CNN for offline HCCR.

I believe, the professionalism of any teacher can be estimated by the feedback received from their students, and it’s of the utmost importance for me to thank you, Lazy Programmer!

I want you to know, in spite, that we have never actually met and you haven’t taught me privately, I consider you one of my greatest Teachers.

The most important things I have learned from you (some in the hard way, though) beside many exciting modern Deep Learning/AI techniques and algorithms are:

1) If one doesn’t know how to program something, one doesn’t understand it completely.

2) If one is not honest with oneself about one’s prior knowledge, one will never succeed in studying more advanced things.

3) Developing skills in BOTH Math and Programming is what makes one a good student of this major.

I am still studying your courses, and am certain I will ask you more than just a few technical questions regarding their content, but I already would like to say, that I will remember your contribution to my adventure in the Deep Learning field, and consider it as big as one of such great scientists’ as Andrew Ng, Geoffrey Hinton, and my supervisor.

Thank you, Lazy Programmer! 非常感谢您,Lazy 老师!

If you are interested, you can find my first paper’s preprint here:

https://arxiv.org/abs/xxx”

5.0
student-avatar

Dima K.

Data Scientist
flag-ukraine
Ukraine

“By the way, if you are interested to hear. I used the HMM classification, as it was in your course (95% of the script, I had little adjustments there), for the Customer-Care department in a big known fintech company. to predict who will call them, so they can call him before the rush hours, and improve the service. Instead of a poem, I Had a sequence of the last 24 hours' events that the customer had, like: "Loaded money", "Usage in the food service", "Entering the app", "Trying to change the password", etc... the label was called or didn't call. The outcome was great. They use it for their VIP customers. Our data science department and I got a lot of praise.”

5.0
student-avatar

Andres Lopez C.

Data Engineer
flag-usa
United States

“This course is exactly what I was looking for. The instructor does an impressive job making students understand they need to work hard in order to learned. The examples are clear, and the explanations of the theory is very interesting.”

5.0
student-avatar

Mohammed K.

Machine Learning Engineer
flag-germany
Germany

“Thank you, I think you have opened my eyes. I was using API to implement Deep learning algorithms and each time I felt I was messing out on some things. So thank you very much.”

5.0
student-avatar

Tom P.

Machine Learning Engineer
flag-usa
United States

“I have now taken a few classes from some well-known AI profs at Stanford (Andrew Ng, Christopher Manning, …) with an overall average mark in the mid-90s. Just so you know, you are as good as any of them. But I hope that you already know that.

I wish you a happy and safe holiday season. I am glad you chose to share your knowledge with the rest of us.”

5.0
Start learning today

Join 30 day bootcamp for free

4.7/5 from — 600k+ learners