Calculus for Machine Learning Level 2 - Optimization

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Free Download Calculus for Machine Learning Level 2 - Optimization
Published 8/2026
Created by MLearning Academy
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 20 Lectures ( 3h 18m ) | Size: 763.5 MB

Master gradients, Hessians, Taylor series, optimization algorithms, and probability for machine learning.
What you'll learn
⚡ Understand multivariable calculus concepts used in machine learning.
⚡ Interpret gradients and Hessian matrices geometrically.
⚡ Understand constrained optimization using Lagrange multipliers.
⚡ Explain how Taylor series approximates machine learning models.
⚡ Compare optimization algorithms including Gradient Descent, Momentum, RMSProp, and Adam.
⚡ Connect multivariable integrals and probability to machine learning.
⚡ Build intuition for optimization landscapes and model convergence.
⚡ Understand the mathematical foundations of deep learning optimization.
Requirements
❗ Completion of Calculus for Machine Learning: Level 1 or equivalent knowledge.
❗ Basic algebra and introductory calculus.
❗ Familiarity with vectors and basic linear algebra.
❗ No advanced optimization knowledge is required.
Description
Machine learning is powered by multivariable calculus, optimization, and probability.
This course builds on the foundations introduced in Level 1 and takes you deeper into the mathematics behind modern machine learning and deep learning algorithms.
You will develop an intuitive understanding of gradients, Hessian matrices, Taylor series, constrained optimization, and advanced optimization algorithms such as Momentum, RMSProp, and Adam. Rather than memorizing formulas, you will learn how these mathematical tools explain the behavior of machine learning models during training.
Throughout the course, every concept is connected to practical machine learning applications through visual explanations and geometric intuition. You will also explore multivariable integrals and probability as essential components of modern AI systems.
This course is designed for learners who already understand basic derivatives and want to confidently study optimization and deep learning mathematics.
able PDF notes accompany every lecture to support your learning and future review. By the end of the course, you will have a strong conceptual framework for understanding how machine learning models learn, optimize, and converge.
Each lecture includes practical machine learning context together with downloadable PDF notes to reinforce the concepts presented. By developing intuition before formal mathematics, you will gain confidence in understanding optimization methods used in modern artificial intelligence and deep learning systems.
Who this course is for
⭐ Students continuing their machine learning mathematics journey.
⭐ Machine learning practitioners seeking deeper mathematical understanding.
⭐ Data scientists and AI engineers.
⭐ Computer science, mathematics, and engineering students.
⭐ Anyone who wants to understand optimization beyond basic gradient descent.
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