Learn essential calculus concepts using Python, focusing on data science and machine learning applications. Explore limits, derivatives, and optimization techniques through hands-on coding projects, building practical skills to solve real-world data problems.
Unlock the power of calculus in data science and machine learning through Python in this comprehensive course. You will start by mastering fundamental concepts like limits, derivatives, and integrals, building a strong foundation in mathematical theory. The course progresses to cover more advanced topics like multivariable calculus and optimization techniques, ensuring you have the tools needed for real-world data analysis. Throughout the course, you’ll work with Python libraries such as SymPy, NumPy, and Matplotlib to perform symbolic and numerical calculations. You will also apply these skills in practical scenarios like optimization problems and cost function analysis. By integrating both math and programming, this course prepares you for more advanced applications in data science and machine learning. The course is designed to be project-driven. You’ll build mini-projects, including a derivative calculator and a gradient descent optimizer, to reinforce your understanding. These projects offer practical skills that can be applied directly to machine learning and data science tasks, making the course both comprehensive and hands-on. This course is ideal for anyone looking to strengthen their math skills with practical Python coding. It is particularly beneficial for aspiring data scientists and machine learning enthusiasts who want to build a solid foundation in calculus. Students in computer science, data analytics, or related fields will also find the course useful for applying calculus concepts to real-world data problems. No prior calculus experience is required, but a basic understanding of Python will be helpful. This course takes a hands-on approach to learning calculus through Python, starting with foundational Python skills and progressing to advanced calculus concepts like derivatives, integrals, and optimization. Each section builds on the previous one, ensuring learners gain practical experience with real-world applications in data science and machine learning. This course is based on Applied Calculus for Data Science and Machine Learning with Python, by Ron Erez. This video is licensed and distributed by Packt. All rights reserved. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.
