Mathematical Foundations of Machine Learning (Updated 09/2021)
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 105 lectures (15h 33m) | Size: 3.9 GB
Essential Linear Algebra and Calculus Hands-On in NumPy, TensorFlow, and PyTorch
What you'll learn:
Understand the fundamentals of linear algebra and calculus, critical mathematical subjects underlying all of machine learning and data science
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)
Appreciate how calculus works, from first principles, via interactive code s in Python
Intimately understand advanced differentiation rules like the chain rule
Compute the partial derivatives of machine-learning cost functions by hand as well as with TensorFlow and PyTorch
Grasp exactly what gradients are and appreciate why they are essential for enabling ML via gradient descent
Use integral calculus to determine the area under any given curve
Be able to more intimately grasp the details of cutting-edge machine learning papers
Develop an understanding of what’s going on beneath the hood of machine learning algorithms, including those used for deep learning
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
Mathematics forms the core of data science and machine learning. Thus, to be the best data scientist you can be, you must have a working understanding of the most relevant math.
Getting started in data science is easy thanks to high-level libraries like Scikit-learn and Keras. But understanding the math behind the algorithms in these libraries opens an infinite number of possibilities up to you. From identifying modeling issues to inventing new and more powerful solutions, understanding the math behind it all can dramatically increasing the impact you can make over the course of your career.
