4.4 NumPy Broadcasting (L04: Scientific Computing in Python)
Sebastian's books: https://sebastianraschka.com/books/
One of the cool things about NumPy is that it allows us to "broadcast." Here, that means that it is creating implicit dimensions that allow us to do things that are not possible in the strict mathematical context of linear algebra.
Jupyter notebook: https://github.com/rasbt/stat451-machine-learning-fs20/blob/master/L04/04_scipython__code.ipynb
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This video is part of my Introduction of Machine Learning course.
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Sebastian Raschka - SIteInterlock
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1.4 Notation (L01: What is Machine Learning)
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1.1 Course overview (L01: What is Machine Learning)
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1.5 ML application (L01: What is Machine Learning)
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1.6 ML motivation (L01: What is Machine Learning)
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2.3 K-nearest neighbors (L02: Nearest Neighbor Methods)
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2.5 Improving k-nearest neighbors (L02: Nearest Neighbor Methods)
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2.6 K-nearest neighbors in Python (L02: Nearest Neighbor Methods)
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3.1 (Optional) Python overview
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3.2 (Optional) Python setup
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3.3 (Optional) Running Python code
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4.2 NumPy Array Construction and Indexing (L04: Scientific Computing in Python)
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4.4 NumPy Broadcasting (L04: Scientific Computing in Python)
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4.7 Reshaping NumPy Arrays (L04: Scientific Computing in Python)
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4.6 NumPy Random Number Generators (L04: Scientific Computing in Python)
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4.9 NumPy Linear Algebra Basics (L04: Scientific Computing in Python)
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5.2 Basic data handling (L05: Machine Learning with Scikit-Learn)
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5.3 Object Oriented Programming & Python Classes (L05: Machine Learning with Scikit-Learn)
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5.4 Intro to Scikit-learn (L05: Machine Learning with Scikit-Learn)
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6.2 Recursive algorithms & Big-O (L06: Decision Trees)
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6.3 Types of decision trees (L06: Decision Trees)
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6.5 Gini & Entropy versus misclassification error (L06: Decision Trees)
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7.1 Intro to ensemble methods (L07: Ensemble Methods)
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7.2 Majority Voting (L07: Ensemble Methods)
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7.3 Bagging (L07: Ensemble Methods)
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7.4 Boosting and AdaBoost (L07: Ensemble Methods)
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7.5 Gradient Boosting (L07: Ensemble Methods)
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