Mathematics for Machine Learning and Data Science Specialization
by DeepLearning.AI
What you’ll get from this course
This is a beginner-friendly course for anyone who wants to develop their mathematical fundamentals for a career in machine learning and data science. A high-school level of mathematics will help learners get the most out of this class.
You will learn:
A deep understanding of what makes algorithms work, and how to tune them for custom implementation.
Statistical techniques that empower you to get more out of your data analysis.
Skills that employers desire, helping you ace machine learning interview questions and land your dream job.
Concepts you will learn:
Data Analysis, Calculus, Vectors and Matrices, Matrix product, Linear Transformations, 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
Syllabus:
Course 1: Linear Algebra for Machine Learning and Data Science.
Course 2: Calculus for Machine Learning and Data Science.
Course 3: Probability & Statistics for Machine Learning & Data Science
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