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Learn machine learning fundamentals with Google's fast-paced, practical introduction, featuring video lectures, case studies, and exercises. Explore key concepts, best practices, and deep neural networks with Google researchers and experts.
Sep 12, 2023 · Learn the basics of machine learning (ML) and why it is important for developers. Watch a video lecture and read the rules of ML philosophy and practice.
Mar 2, 2018 · The course develops intuition around fundamental machine learning concepts. What does the course cover? MLCC covers many machine learning fundamentals, starting with loss and gradient descent, then building through classification models and neural nets. The programming exercises introduce TensorFlow.
- Algebra
- Linear Algebra
- Statistics
- Calculus
- Python Programming
- Bash Terminal / Cloud Console
variables,coefficients,and functionsmean, median, outliers,and standard deviationability to read a histogramconcept of a derivative(you won't have to actually calculate derivatives)chain rule(for a full understanding of the backpropagation algorithmfor training neural networks)The following Python basics are covered in The Python Tutorial: 1. defining and calling functions,using positional and keywordparameters 2. dictionaries,lists,sets(creating, accessing, and iterating) 3. for loops,for loops with multiple iterator variables (e.g., for a, b in [(1,2), (3,4)]) 4. if/else conditional blocks andconditional expressions 5....
To run the programming exercises on your local machine or in a cloud console,you should be comfortable working on the command line: 1. Bash Reference Manual 2. Bash Cheatsheet 3. Learn Shell
Learn the critical basics of machine learning in a hands-on course. This is one of the foundational courses that cover machine learning fundamentals and core concepts.
Learn the basics of machine learning with this hands-on course from Google. Explore topics such as problem framing, data preparation, testing, debugging, and more.
Feb 24, 2023 · Learn how to use tf.keras, a high-level API for TensorFlow, to develop and train machine learning models. This lesson covers NumPy, pandas, Colabs, linear regression, loss curves, and hyperparameters.
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