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
This course includes:
- 2 hours on-demand video
- Full lifetime access
- Access on mobile and TV
- Certificate of completion
- 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.
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 whichever problem you have at hand.
To be an excellent data scientist, you need to know how those libraries and algorithms work.
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 Structures
- Section 2: Tensor Operations
- Section 3: Matrix Properties
- Section 4: Eigenvectors and Eigenvalues
- Section 5: Matrix Operations for Machine Learning
(Note that, as this is initially being offered as a free course, Udemy limits us to two hours of videos so we only get halfway through Section 2. For a sneak preview of what’s to come in the remaining sections, check out the course’s code repository in GitHub — a link is provided in the videos.)
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? Enroll now!
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!)
Note :This course will expire soon enroll now to get life time access
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