Machine Learning & Data Science Foundations Masterclass | Data Science Online Course by Udemy

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The Theoretical and Practical Foundations of Machine Learning. Master Matrices, Linear Algebra, and Tensors in Python

An excellent training about Data Science

Machine Learning & Data Science Foundations Masterclass

To be a good data scientist, you need to know how to use data science and machine learning libraries and algorithms, such as NumPy, TensorFlow and PyTorch, to solve whatever problem you have at hand. To be an excellent data scientist, you need to know how those libraries and algorithms work under the hood. This is where our course “Machine Learning & Data Science Foundations Masterclass” comes in. Led by deep learning guru Dr. Jon Krohn, this first entry in the Machine Learning Foundations series will give you the basics of the mathematics such as linear algebra, matrices and tensor manipulation, that operate behind the most important Python libraries and machine learning and data science algorithms. The first step in your journey into becoming an excellent data scientist is broken down as follows: Section 1: Linear Algebra Data StructuresSection 2: Tensor OperationsSection 3: Matrix PropertiesWhile the above three sections constitute a standalone course all on their own, we’re not stopping there! We are currently filming additional, intermediate-level linear algebra content (Section 4 on Eigenvectors and Eigenvalues and Section 5 on Matrix Operations for Machine Learning) and are aiming to have all of it released in 2020. In 2021, we will release all remaining sections of the comprehensive Machine Learning Foundations series, which covers not only linear algebra, but also calculus, probability, statistics, algorithms, data structures, and optimization. Enrollment now includes free, unlimited access to all of this future course content – over 25 hours in total. Throughout each of the sections, you’ll find plenty of hands-on assignments and practical exercises to get your math game up to speed! Are you ready to become an excellent data scientist? See you in the classroom.

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