Mathematics-Basics to Advanced for Data Science And GenAI
Mathematics-Basics to Advanced for Data Science And GenAI
Build Strong math foundation with linear algebra,stats,probability,differential calculus for mastering data science
Product Brand: Udemy
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Udemy Coupon Code for Mathematics-Basics to Advanced for Data Science And GenAI Course. Build Strong math foundation with linear algebra,stats,probability,differential calculus for mastering data science
Created by Krish Naik, KRISHAI Technologies Private Limited | 23 hours on-demand video course
Mathematics Course Overview
Mathematics-Basics to Advanced for Data Science And GenAI
Are you eager to dive into the world of data science but feel overwhelmed by the mathematical concepts involved? Welcome to the “Complete Maths to Learn Data Science” course, your comprehensive guide to mastering the essential mathematical foundations needed to excel in data science and machine learning.
This course is designed to bridge the gap between your current math skills and the level required to understand and implement data science algorithms effectively. Whether you are a beginner or an experienced professional looking to strengthen your mathematical understanding, this course will equip you with the tools you need to succeed.
What you’ll learn
- Master Calculus: Understand derivatives and integrals, and apply them in optimizing machine learning algorithms and data analysis tasks.
- Learn Linear Algebra: Grasp vectors, matrices, and eigenvalues, essential for building and understanding advanced data science models.
- Understand Probability: Dive into probability theory, crucial for making informed predictions and working with uncertainty in data.
- Apply Statistics: Gain practical skills in statistical analysis, helping you make data-driven decisions and interpret results effectively.
Top Mathematics Courses Online for 2024
Math 0-1: Linear Algebra for Data Science & Machine Learning Course
Math 0-1: Linear Algebra for Data Science & Machine Learning
This Math 0-1: Linear Algebra for Data Science & Machine Learning course will cover systems of linear equations, matrix operations (dot product, inverse, transpose, determinant, trace), low-rank approximations, positive-definiteness and negative-definiteness, and eigenvalues and eigenvectors. It will even include machine learning-focused material you wouldn’t normally see in a regular college course, such as how these concepts apply to GPT-4, and fine-tuning modern neural networks like diffusion models (for generative AI art) and LLMs (Large Language Models) using LoRA.
Math 0-1: Calculus for Data Science & Machine Learning Course
Math 0-1: Calculus for Data Science & Machine Learning
This Math 0-1: Calculus for Data Science & Machine Learning 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).
Who this course is for:
- Aspiring Data Scientists: Individuals looking to build a strong mathematical foundation essential for mastering data science and machine learning.
- Data Science Beginners: Those who are new to data science and want to understand the core mathematical concepts that drive data science algorithms.
- Professionals Transitioning into Data Science: Engineers, analysts, or professionals from other fields seeking to acquire the mathematical skills necessary for a career shift into data science.
- Students and Academics: Students pursuing studies in data science, computer science, or related fields who need a comprehensive understanding of mathematics for data science applications.
- Lifelong Learners: Anyone with a passion for learning and a desire to understand how mathematics powers the world of data science, even without prior experience in the field.
- This course is tailored to equip learners with the essential mathematical tools needed to excel in data science, regardless of their current level of expertise.
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