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.