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Math 0-1

Calculus for Data Science & Machine Learning

Lazy Programmer Inc. and Lazy Programmer Team

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?

Read more

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)

Enroll now

What's inside

Learning objectives

  • 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

Syllabus

Introduction and Outline
Introduction
Outline
How to Succeed in this Course
Read more
Where to Get the Code
Review
Functions Review
Functions Review in Python
Limits
What Are Limits?
Precise Definition of Limit (Optional)
Limit Laws
Infinities and Asymptotes
Indeterminate Forms
Limits in Python
Limits with Plotting in Python
Limits Section Summary
Derivatives From First Principles
Slopes, Tangent Lines, and Derivatives
More On Tangent Lines, Derivative Checking
Exercise: Quadratic
Exercise: Cubic
Exercise: Reciprocal
Exercise: Root
Alternate Notations & Higher Order Derivatives
Derivative Checking in Python
Derivatives Section Summary
Derivative Rules
Power Rule
Constant Multiple, Addition, Subtraction Rules
Exponent Rule
Exponent Rule (continued)
Chain Rule
Exercises: Chain Rule
Product and Quotient Rules
Exercises: Product and Quotient Rules
Implicit Differentiation
Logarithm Rule
Implicit Differentiation Applications
Logarithmic Differentiation
Exercise: Derivatives of Hyperbolic Functions
Exercise: Sum of Polynomials
Exercise: Gaussian Variance
Exercise: Entropy
Trigonometric Functions (Optional)
Inverse Trigonometric Functions (Optional)
Derivative Rules Section Summary
Applications of Differentiation
Finding the Minimum / Maximum
Minimum / Maximum Clarifications and Examples
Second Derivative Test
Exercise: Minimums and Maximums
Exercise: Gaussian 1
Exercise: Gaussian 2
l'Hopital's Rule
Newton's Method
Newton's Method in Python
Applications Section Summary
Integration (Calculus 2)
Integrals: Section Introduction
Area Under Curve
Fundamental Theorem of Calculus (pt 1)
Fundamental Theorem of Calculus (pt 2)
Definite and Indefinite Integrals
Exercises: Definite Integrals
Exercises: Indefinite Integrals
Exercises: Improper Integrals
Numerical Integration in Python
Integration Section Summary
Vector Calculus in Multiple Dimensions (Calculus 3)
Functions of Multiple Variables
Partial Differentiation
The Gradient
The Jacobian and Hessian
Differentials and Chain Rule in Multiple Dimensions
Why is the Gradient the Direction of Steepest Ascent?
Steepest Ascent in Python
Optimization and Lagrange Multipliers (pt 1)
Optimization and Lagrange Multipliers (pt 2)
Vector Calculus Section Summary
Setting Up Your Environment (Appendix/FAQ by Student Request)
Pre-Installation Check
Anaconda Environment Setup
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
Where To Get the Code Troubleshooting
How to use Github & Extra Coding Tips (Optional)
Effective Learning Strategies (Appendix/FAQ by Student Request)
Math Order for Machine Learning & Data Science
Can YouTube Teach Me Calculus? (Optional)
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
What order should I take your courses in? (part 1)
What order should I take your courses in? (part 2)
Appendix / FAQ Finale
What is the Appendix?
BONUS

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Focuses on essential concepts of calculus for immediate application in machine learning and data science
Provides comprehensive coverage of calculus concepts, including limits, derivatives, integration, and vector calculus
Taught by experienced instructors who specialize in making complex concepts accessible
Emphasizes practical applications of calculus, such as optimization and finding derivatives from first principles
Includes hands-on exercises and demonstrations using Python programming language
May be challenging for learners without a firm understanding of high school math concepts

Save this course

Save Math 0-1: Calculus for Data Science & Machine Learning to your list so you can find it easily later:
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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Math 0-1: Calculus for Data Science & Machine Learning with these activities:
Khan Academy Calculus
Complement your classroom learning with interactive tutorials and videos from Khan Academy, providing additional support and reinforcing key concepts.
Browse courses on Calculus
Show steps
  • Visit the Khan Academy Calculus section.
  • Watch videos and complete exercises on topics covered in class.
  • Use the practice questions to test your understanding.
Read 'Essential Calculus: Early Transcendentals'
Build a solid foundation in the fundamentals of calculus, covering essential concepts like limits, derivatives, and integrals, which will aid in understanding the course content and applications in machine learning.
View Student Solutions Manual, Chapters 1-11 for... on Amazon
Show steps
  • Read Chapter 1 and 2 to review functions and limits.
  • Complete the practice problems at the end of each section.
  • Review the key concepts and formulas discussed in class.
Calculus Mind Map
Create a visual representation of calculus concepts, connecting different topics and improving your overall understanding and retention.
Browse courses on Calculus
Show steps
  • Start with the main concepts of calculus.
  • Draw branches for related subtopics and definitions.
  • Add examples and applications to illustrate each concept.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Study Group
Form a study group with classmates to discuss course material, work on problems together, and provide mutual support, enhancing your learning experience.
Show steps
  • Find a group of classmates who are interested in forming a study group.
  • Decide on a regular meeting time and place.
  • Prepare for each meeting by reviewing the material and identifying areas for discussion.
Calculus Practice Problems
Sharpen your problem-solving skills and deepen your understanding of calculus concepts through regular practice, improving your ability to apply them in machine learning applications.
Browse courses on Calculus
Show steps
  • Find practice problems online or in textbooks.
  • Solve the problems and check your answers.
  • Review your incorrect answers to identify areas for improvement.
Calculus in Python
Apply your understanding of calculus concepts by implementing them in Python, solidifying your grasp of derivatives, integrals, and optimization.
Browse courses on Calculus
Show steps
  • Choose a project idea that involves applying calculus concepts.
  • Create a Python script to implement your project.
  • Test and debug your code to ensure accurate results.
Calculus Workshop
Attend a workshop led by an expert to engage in hands-on exercises, ask questions, and deepen your understanding of complex calculus topics.
Browse courses on Calculus
Show steps
  • Find and register for a calculus workshop in your area.
  • Attend the workshop and actively participate in the exercises.
  • Follow up with the instructor if you have any questions.
Contribute to Open Source Calculus Projects
Engage with the open-source community by contributing to calculus-related projects, deepening your understanding of the subject and building practical skills.
Browse courses on Calculus
Show steps
  • Find open-source calculus projects on platforms like GitHub.
  • Identify an area where you can contribute, such as bug fixes or documentation improvements.
  • Submit a pull request with your contributions and engage in discussions with the project maintainers.

Career center

Learners who complete Math 0-1: Calculus for Data Science & Machine Learning will develop knowledge and skills that may be useful to these careers:

Reading list

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Course image
Effort
14.5 total hours
Via
Udemy
Instructors
Lazy Programmer Inc.
Lazy Programmer Team
Language
English
Good to know
Focuses on essential concepts of calculus for immediate application in machine learning and data science
Provides comprehensive coverage of calculus concepts, including limits, derivatives, integration, and vector calculus
Taught by experienced instructors who specialize in making complex concepts accessible
Emphasizes practical applications of calculus, such as optimization and finding derivatives from first principles
Includes hands-on exercises and demonstrations using Python programming language
May be challenging for learners without a firm understanding of high school math concepts
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