Mathematics for Machine Learning and Data Science Specialization by DeepLearning.AI

Enroll in Mathematics for Machine Learning and Data Science 👉 https://bit.ly/47Hnlzr

This specialization is absolutely jam-packed with foundational machine learning and data science skills and is appropriate for both beginners and advanced AI builders alike.

As Andrew Ng shared in his latest letter of The Batch, “I believe that math isn’t about memorizing formulas; it’s about building a conceptual understanding that will hone your intuition. That’s why Luis Serrano, curriculum architect Anshuman Singh, and their team present these topics using interactive visualizations and hands-on examples. Their explanations of some concepts are the most intuitive I’ve ever seen.”

Here’s a quick breakdown of the key concepts you will learn in Mathematics for Machine Learning and Data Science:

Vectors and Matrices
Matrix product
Linear Transformations
Rank, Basis, and Span
Eigenvectors and Eigenvalues
Derivatives
Gradients
Optimization
Gradient Descent
Gradient Descent in Neural Networks
Newton’s Method
Probability
Random Variables
Bayes Theorem
Gaussian Distribution
Variance and Covariance
Sampling and Point Estimates
Maximum Likelihood Estimation
Bayesian Statistics
Confidence Intervals
Hypothesis Testing

Learn more: https://bit.ly/47Hnlzr

DeepLearning.AI is an education technology company that is empowering the global workforce to build an AI-powered future through world-class education, hands-on training, and a collaborative community.