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:

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.

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)

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.

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.

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

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. :)

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. ...

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- Introduction (04:01) (FREE preview available)
- 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)
- 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)
- 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, IPython, Theano, 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