Mathematics for Machine Learning and Data Science Specialization
Master the Toolkit of AI and Machine Learning. Mathematics for Machine Learning and Data Science is a beginner-friendly Specialization where you’ll learn the fundamental mathematics toolkit of machine learning: calculus, linear algebra, statistics, and probability.
https://www.coursera.org/specializations/mathematics-for-machine-learning-and-data-science
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.
Skills you’ll gain
- Category: Bayesian Statistics
- Bayesian Statistics
- Category: Mathematics
- Mathematics
- Category: Linear Regression
- Linear Regression
- Category: Calculus
- Calculus
- Category: Machine Learning
- Machine Learning
- Category: Probability
- Probability
Advance your subject-matter expertise
- Learn in-demand skills from university and industry experts
- Master a subject or tool with hands-on projects
- Develop a deep understanding of key concepts
- Earn a career certificate from DeepLearning.AI
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 actually 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
- Identity 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
1. Linear Algebra for Machine Learning and Data Science
What you’ll learn
- Represent data as vectors and matrices and identify their properties using concepts of singularity, rank, and linear independence
- Apply common vector and matrix algebra operations like dot product, inverse, and determinants
- Express certain types of matrix operations as linear transformation, and apply concepts of eigenvalues and eigenvectors to machine learning problems
Skills you’ll gain:
Eigenvalues And Eigenvectors
Linear Equation
Determinants
Machine Learning
Linear Algebra
2. Calculus for Machine Learning and Data Science
What you will learn:
- Analytically optimize different types of functions commonly used in machine learning using properties of derivatives and gradients
- Approximately optimize different types of functions commonly used in machine learning
- Visually interpret differentiation of different types of functions commonly used in machine learning
- Perform gradient descent in neural networks with different activation and cost functions
Skills you’ll gain
Calculus
Machine Learning
Newton’S Method
Gradient Descent|
Mathematical Optimization
3. Probability & Statistics for Machine Learning & Data Science
What you’ll learn
- Describe and quantify the uncertainty inherent in predictions made by machine learning models
- Visually and intuitively understand the properties of commonly used probability distributions in machine learning and data science
- Apply common statistical methods like maximum likelihood estimation (MLE) and maximum a priori estimation (MAP) to machine learning problems
- Assess the performance of machine learning models using interval estimates and margin of errors
Skills you’ll gain
Probability And Statistics
Machine Learning (ML) Algorithms
Statistical Analysis
Probability
Statistical Hypothesis Testing
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