Colleagues, the “
Mathematics for Machine Learning Specialization” you will build an intuitive understanding, and relating it to Machine Learning and Data Science. Training modules cover: 1) Linear Algebra - what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them, 2) Multivariate Calculus - look at how to optimize fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting, 3) Dimensionality Reduction with Principal Component Analysis - uses the mathematics from the first two courses to compress high-dimensional data. This course is of intermediate difficulty and will require Python and numpy knowledge. At the end of this specialization you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning. Applied Learning Project: Through the assignments of this specialisation you will use the skills you have learned to produce mini-projects with Python on interactive notebooks, an easy to learn tool which will help you apply the knowledge to real world problems. For example, using linear algebra in order to calculate the page rank of a small simulated internet, applying multivariate calculus in order to train your own neural network, performing a non-linear least squares regression to fit a model to a data set, and using principal component analysis to determine the features of the MNIST digits data set.Enroll today (teams & execs welcome): https://imp.i384100.net/APPX1N
For your listening-reading pleasure:
1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)
2 - “ChatGPT - The Era of Generative Conversational AI Has Begun” (Audible) or (Kindle)
3 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Audible) or (Kindle)
Much career success, Lawrence E. Wilson - AI Academy (share with your team)
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