Mathematical Foundation For Machine Learning and AI | Data Science Online Course by Udemy

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Learn the core mathematical concepts for machine learning and learn to implement them in R and python

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Mathematical Foundation For Machine Learning and AI

ArtificialIntelligence has gained importance in the last decade with a lotdepending on the development and integration of AI in our dailylives. The progress that AI has already made is astounding with theself-driving cars, medical diagnosis and even betting humans atstrategy games like Go and Chess. Thefuture for AI is extremely promising and it isnt far from when wehave our own robotic companions. This has pushed a lot of developersto start writing codes and start developing for AI and ML programs. However, learning to write algorithms for AI and ML isnt easy andrequires extensive programming and mathematical knowledge. Mathematicsplays an important role as it builds the foundation for programmingfor these two streams. And in this course, weve covered exactlythat. We designed a complete course to help you master themathematical foundation required for writing programs and algorithmsfor AI and ML. Thecourse has been designed in collaboration with industry experts tohelp you breakdown the difficult mathematical concepts known to maninto easier to understand concepts. The course covers three mainmathematical theories: Linear Algebra, Multivariate Calculus andProbability Theory. LinearAlgebra Linear algebra notation is used in Machine Learningto describe the parameters and structure of different machinelearning algorithms. This makes linear algebra a necessity tounderstand how neural networks are put together and how they areoperating. It covers topics suchas: Scalars, Vectors, Matrices, TensorsMatrix NormsSpecial Matrices and VectorsEigenvalues and EigenvectorsMultivariateCalculus This is used to supplement the learning part ofmachine learning. It is what is used to learn from examples, updatethe parameters of different models and improve the performance. It covers topics suchas: DerivativesIntegralsGradientsDifferential OperatorsConvex OptimizationProbabilityTheory The theories are used to make assumptions about theunderlying data when we are designing these deep learning or AIalgorithms. It is important for us to understand the key probabilitydistributions, and we will cover it in depth in this course. It covers topics suchas: Elements of ProbabilityRandom VariablesDistributionsVariance and ExpectationSpecial Random VariablesThecourse also includes projects and quizzes after each section to helpsolidify your knowledge of the topic as well as learn exactly how touse the concepts in real life. Atthe end of this course, you will not have not only the knowledge tobuild your own algorithms, but also the confidence to actually startputting your algorithms to use in your next projects. Enrollnow and become the next AI master with this fundamentals course!

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