Mathematics for Machine Learning and Data Science Specialization

What you'll learn

• A deep understanding of the math that makes machine learning algorithms work.
• Statistical techniques that empower you to get more out of your data analysis.

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Specialization - 3-course series

Mathematics for Machine Learning and Data Science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. This beginner-friendly Specialization is where you’ll master the fundamental mathematics toolkit of machine learning.

 

Many machine learning engineers and data scientists need help with mathematics, and even experienced practitioners can feel held back by a lack of math skills. This Specialization uses innovative pedagogy in mathematics to help you learn quickly and intuitively, with courses that use easy-to-follow plugins and visualizations to help you see how the math behind machine learning works.

 

We recommend you have a high school level of mathematics (functions, basic algebra) and familiarity with a programming language (loops, functions, if/else statements, lists/dictionaries, importing libraries). Assignments and labs are written in Python but the course introduces all the machine learning libraries you’ll use.

 

Applied Learning Project

 

By the end of this Specialization, you will be ready to:

• Represent data as vectors and matrices and identify their properties like singularity, rank, and linear independence
• Apply common vector and matrix algebra operations like the dot product, inverse, and determinants
• Express matrix operations as linear transformations
• Apply concepts of eigenvalues and eigenvectors to machine learning problems including Principal Component Analysis (PCA)
• Optimize different types of functions commonly used in machine learning
• Perform gradient descent in neural networks with different activation and cost functions
• Identify the features of commonly used probability distributions
• Perform Exploratory Data Analysis to find, validate, and quantify patterns in a dataset
• Quantify the uncertainty of predictions made by machine learning models using confidence intervals, margin of error, p-values, and hypothesis testing.
• Apply common statistical methods like MLE and MAP

All the information provided is based on the Mathematics for Machine Learning and Data Science | Coursera  from DeepLearning.AI