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

Math 0-1: Calculus for Data Science & Machine Learning, available at $69.99, has an average rating of 4.78, with 85 lectures, based on 1126 reviews, and has 6771 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone who wants to learn calculus quickly or Students and professionals interested in machine learning and data science but who've gotten stuck on the math It is particularly useful for Anyone who wants to learn calculus quickly or Students and professionals interested in machine learning and data science but who've gotten stuck on the math.

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

Summary

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

Price: $69.99

Average Rating: 4.78

Number of Lectures: 85

Number of Published Lectures: 85

Number of Curriculum Items: 85

Number of Published Curriculum Objects: 85

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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 Should Attend

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

Target Audiences

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

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)

Course Curriculum

Chapter 1: Introduction and Outline

Lecture 1: Introduction

Lecture 2: Outline

Lecture 3: How to Succeed in this Course

Lecture 4: Where to Get the Code

Chapter 2: Review

Lecture 1: Functions Review

Lecture 2: Functions Review in Python

Chapter 3: Limits

Lecture 1: What Are Limits?

Lecture 2: Precise Definition of Limit (Optional)

Lecture 3: Limit Laws

Lecture 4: Infinities and Asymptotes

Lecture 5: Indeterminate Forms

Lecture 6: Limits in Python

Lecture 7: Limits with Plotting in Python

Lecture 8: Limits Section Summary

Chapter 4: Derivatives From First Principles

Lecture 1: Slopes, Tangent Lines, and Derivatives

Lecture 2: More On Tangent Lines, Derivative Checking

Lecture 3: Exercise: Quadratic

Lecture 4: Exercise: Cubic

Lecture 5: Exercise: Reciprocal

Lecture 6: Exercise: Root

Lecture 7: Alternate Notations & Higher Order Derivatives

Lecture 8: Derivative Checking in Python

Lecture 9: Derivatives Section Summary

Chapter 5: Derivative Rules

Lecture 1: Power Rule

Lecture 2: Constant Multiple, Addition, Subtraction Rules

Lecture 3: Exponent Rule

Lecture 4: Exponent Rule (continued)

Lecture 5: Chain Rule

Lecture 6: Exercises: Chain Rule

Lecture 7: Product and Quotient Rules

Lecture 8: Exercises: Product and Quotient Rules

Lecture 9: Implicit Differentiation

Lecture 10: Logarithm Rule

Lecture 11: Implicit Differentiation Applications

Lecture 12: Logarithmic Differentiation

Lecture 13: Exercise: Derivatives of Hyperbolic Functions

Lecture 14: Exercise: Sum of Polynomials

Lecture 15: Exercise: Gaussian Variance

Lecture 16: Exercise: Entropy

Lecture 17: Trigonometric Functions (Optional)

Lecture 18: Inverse Trigonometric Functions (Optional)

Lecture 19: Derivative Rules Section Summary

Chapter 6: Applications of Differentiation

Lecture 1: Finding the Minimum / Maximum

Lecture 2: Minimum / Maximum Clarifications and Examples

Lecture 3: Second Derivative Test

Lecture 4: Exercise: Minimums and Maximums

Lecture 5: Exercise: Entropy

Lecture 6: Exercise: Gaussian 1

Lecture 7: Exercise: Gaussian 2

Lecture 8: l'Hopital's Rule

Lecture 9: Newton's Method

Lecture 10: Newton's Method in Python

Lecture 11: Applications Section Summary

Chapter 7: Integration (Calculus 2)

Lecture 1: Integrals: Section Introduction

Lecture 2: Area Under Curve

Lecture 3: Fundamental Theorem of Calculus (pt 1)

Lecture 4: Fundamental Theorem of Calculus (pt 2)

Lecture 5: Definite and Indefinite Integrals

Lecture 6: Exercises: Definite Integrals

Lecture 7: Exercises: Indefinite Integrals

Lecture 8: Exercises: Improper Integrals

Lecture 9: Numerical Integration in Python

Lecture 10: Integration Section Summary

Chapter 8: Vector Calculus in Multiple Dimensions (Calculus 3)

Lecture 1: Functions of Multiple Variables

Lecture 2: Partial Differentiation

Lecture 3: The Gradient

Lecture 4: The Jacobian and Hessian

Lecture 5: Differentials and Chain Rule in Multiple Dimensions

Lecture 6: Why is the Gradient the Direction of Steepest Ascent?

Lecture 7: Steepest Ascent in Python

Lecture 8: Optimization and Lagrange Multipliers (pt 1)

Lecture 9: Optimization and Lagrange Multipliers (pt 2)

Lecture 10: Vector Calculus Section Summary

Chapter 9: Setting Up Your Environment (Appendix/FAQ by Student Request)

Lecture 1: Pre-Installation Check

Lecture 2: Anaconda Environment Setup

Lecture 3: How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

Lecture 4: Where To Get the Code Troubleshooting

Lecture 5: How to use Github & Extra Coding Tips (Optional)

Chapter 10: Effective Learning Strategies (Appendix/FAQ by Student Request)

Lecture 1: Math Order for Machine Learning & Data Science

Lecture 2: Can YouTube Teach Me Calculus? (Optional)

Lecture 3: Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?

Lecture 4: What order should I take your courses in? (part 1)

Lecture 5: What order should I take your courses in? (part 2)

Chapter 11: Appendix / FAQ Finale

Lecture 1: What is the Appendix?

Lecture 2: BONUS

Instructors

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer
Lazy Programmer Team
Artificial Intelligence and Machine Learning Engineer

Rating Distribution

1 stars: 1 votes
2 stars: 4 votes
3 stars: 12 votes
4 stars: 402 votes
5 stars: 707 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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