What you will learn in Mathematics for Machine Learning and Data Science Specialization Course
This specialization offers a comprehensive introduction to the mathematical foundations essential for machine learning and data science, covering linear algebra, calculus, probability, and statistics.
Learners will gain hands-on experience applying mathematical concepts using Python programming in interactive lab exercises.
The courses emphasize intuitive understanding through visualizations, enabling learners to grasp complex mathematical ideas effectively.
By the end of the program, students will be equipped to understand and implement machine learning algorithms with a solid mathematical foundation.
Program Overview
Linear Algebra for Machine Learning and Data Science
⏱️ 4 weeks
Understand vectors, matrices, and their properties such as singularity, rank, and linear independence.
Perform operations like dot product, inverse, and determinants.
Apply concepts of eigenvalues and eigenvectors to machine learning problems, including Principal Component Analysis (PCA).
Calculus for Machine Learning and Data Science
⏱️ 3 weeks
Learn to optimize functions using derivatives and gradients.
Implement gradient descent algorithms in neural networks with various activation and cost functions.
Visualize differentiation and understand its application in machine learning models.
Probability & Statistics for Machine Learning & Data Science
⏱️ 4 weeks
Explore probability distributions and their properties.
Perform exploratory data analysis to identify patterns in datasets.
Quantify uncertainty in predictions using confidence intervals, p-values, and hypothesis testing.
Apply statistical methods like Maximum Likelihood Estimation (MLE) and Maximum A Posteriori (MAP) estimation.
Get certificate
Job Outlook
A strong understanding of mathematics is crucial for careers in machine learning and data science.
Proficiency in linear algebra, calculus, and statistics enhances one’s ability to develop and optimize machine learning models.
Employers value candidates who can bridge the gap between theoretical concepts and practical implementation in data-driven roles.
Specification: Mathematics for Machine Learning and Data Science Specialization
|
