Machine Learning Data Science Foundations Masterclass
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 whichever problem you have at hand.
What’s behind the machine learning hype? In this nontechnical course, you’ll learn everything you’ve been too afraid to ask about machine learning. There’s no coding required. Handson exercises will help you get past the jargon and learn how this exciting technology powers everything from selfdriving cars to your personal Amazon shopping suggestions. How does machine learning work, when can you use it, and what is the difference between AI and machine learning? They’re all covered. Gain skills in this hugely indemand and influential field, and discover why machine learning is for everyone!
To be an excellent data scientist, you need to know how those libraries and algorithms work.
Machine Learning Data Science Foundations Masterclass
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.
Machine Learning Data Science Foundations Masterclass
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
Throughout each of the sections, you’ll find plenty of handson 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.
In this chapter, we’ll unpack deep learning beginning with neural networks. Next, we’ll take a closer look at two common usecases for deep learning: computer vision and natural language processing. We’ll wrap up the course discussing the limits and dangers of machine learning.
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 cuttingedge machine learning
Who this course is for:
 You use highlevel software libraries (e.g., scikitlearn, 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
 Youâ€™re a software developer who would like to develop a firm foundation for the deployment of machine learning algorithms into production systems
 Youâ€™re a data scientist who would like to reinforce your understanding of the subjects at the core of your professional discipline
 Youâ€™re a data analyst or A.I. enthusiast who would like to become a data scientist or data/ML engineer, and so youâ€™re keen to deeply understand the field youâ€™re entering from the ground up (very wise of you!)
Requirements
 All code demos will be in Python so experience with it or another objectoriented programming language would be helpful for following along with the handson examples.
 Familiarity with secondary schoollevel 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 wellprepared to follow along with all of the mathematics.
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Last Updated 11/2020
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