[ FreeCourseWeb ] Udemy - Mathematical Foundations of Machine Learning (Update) torrent

Mathematical Foundations of Machine Learning (Update)

MP4 | Video: h264, 1280x720 | Audio: AAC, 44100 Hz
Language: English | Size: 3.79 GB | Duration: 10h 4m
What you'll learn
Understand the fundamentals of linear algebra, a critical subject underlying all ML algorithms and data science models
Manipulate tensors using all three of the most important Python tensor libraries: NumPy, TensorFlow, and PyTorch
How to apply all of the essential vector and matrix operations for machine learning and data science
Reduce the dimensionality of complex data to the most informative elements with eigenvectors, SVD, and PCA
Solve for unknowns with both simple techniques (e.g., elimination) and advanced techniques (e.g., pseudoinversion)
Be able to more intimately grasp the details of cutting-edge machine learning papers

Requirements
All code s will be in Python so experience with it or another object-oriented programming language would be helpful for following along with the hands-on examples.
Familiarity with secondary school-level mathematics will make the class easier to follow along with. If you are comfortable dealing with quantitative information -- such as understanding charts and rearranging simple equations -- then you should be well-prepared to follow along with all of the mathematics.

Description
To be a good data scientist, you need to know how to use data science and machine learning libraries and algorithms, such as Scikit-learn, 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 Mathematical Foundations of Machine Learning comes in.

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