Machine Learning & Data Science Foundations Masterclass (3/2021)

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

What you'll learn
Understand the fundamentals of linear algebra, a ubiquitous approach for solving for unknowns within high-dimensional spaces.
Manipulate tensors using the most important Python tensor libraries: NumPy, TensorFlow, and PyTorch
Possess an in-depth understanding of matrices, including their properties, key classes, and critical ML operations
Develop a geometric intuition of what’s going on beneath the hood of ML and deep learning algorithms.
Be able to more intimately grasp the details of cutting-edge machine learning papers

Requirements
All code demos 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 "Machine Learning & Data Science Foundations Masterclass" comes in. Led by deep learning guru Dr. Jon Krohn, the Machine Learning Foundations series provides a firm grasp of the underlying mathematics, such as linear algebra, tensors, and eigenvectors, that operate behind the most important Python libraries, machine learning models, 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 Structures

Section 2: Tensor Operations

Section 3: Matrix Properties

Section 4: Eigenvectors and Eigenvalues

While the above sections constitute a standalone course all on their own, we're not stopping there! We have finished filming additional, intermediate-level linear algebra content (Section 5 on Matrix Operations for Machine Learning) as well as all of the calculus content (Sections 6 through 10). It will all be edited and uploaded in early 2021. We will release all remaining sections of the comprehensive Machine Learning Foundations series into the course as quickly as we can. Ultimately, the course will cover not only linear algebra and calculus, but also 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, Python code demos, and practical exercises to get your math game up to speed!

Are you ready to become an outstanding data scientist? See you in the classroom.

Who this course is for:
You use high-level software libraries (e.g., scikit-learn, Keras, TensorFlow) to train or deploy machine learning algorithms, and would now like to understand the fundamentals underlying the abstractions, enabling you to expand your capabilities
You’re a software developer who would like to develop a firm foundation for the deployment of machine learning algorithms into production systems
You’re a data scientist who would like to reinforce your understanding of the subjects at the core of your professional discipline
You’re a data analyst or A.I. enthusiast who would like to become a data scientist or data/ML engineer, and so you’re keen to deeply understand the field you’re entering from the ground up (very wise of you!)

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