5.1 Reading a Dataset from a Tabular Text File (L05: Machine Learning with Scikit-Learn)

Sebastian Raschka · Beginner ·📐 ML Fundamentals ·5y ago
Sebastian's books: https://sebastianraschka.com/books/ Machine learning begins with loading your data into a friendly array format. In this video, we will use pandas' read_csv function to get data into our active Python session before we get to the machine learning part via scikit-learn. Jupyter Notebook: https://github.com/rasbt/stat451-machine-learning-fs20/blob/master/L05/code/05-preprocessing-and-sklearn__notes.ipynb ------- This video is part of my Introduction of Machine Learning course. Next video: https://youtu.be/a1JrNuLsmh4 The complete playlist: https://www.youtube.com/playlis…
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1 Sebastian Raschka - SIteInterlock
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 17 1.1 Course overview (L01: What is Machine Learning)
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 21 2.2 Nearest neighbor decision boundary (L02: Nearest Neighbor Methods)
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 22 2.3 K-nearest neighbors (L02: Nearest Neighbor Methods)
2.3 K-nearest neighbors (L02: Nearest Neighbor Methods)
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 23 2.4 Big O of K-nearest neighbors (L02: Nearest Neighbor Methods)
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 24 2.5 Improving k-nearest neighbors (L02: Nearest Neighbor Methods)
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 25 2.6 K-nearest neighbors in Python (L02: Nearest Neighbor Methods)
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 26 3.1 (Optional) Python overview
3.1 (Optional) Python overview
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 27 3.2 (Optional) Python setup
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 28 3.3 (Optional) Running Python code
3.3 (Optional) Running Python code
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 29 4.1 Intro to NumPy (L04: Scientific Computing in Python)
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 30 4.2 NumPy Array Construction and Indexing (L04: Scientific Computing in Python)
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 31 4.4 NumPy Broadcasting (L04: Scientific Computing in Python)
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 32 4.5 NumPy Advanced Indexing -- Memory Views and Copies (L04: Scientific Computing in Python)
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 34 4.7 Reshaping NumPy Arrays (L04: Scientific Computing in Python)
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 35 4.6 NumPy Random Number Generators (L04: Scientific Computing in Python)
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 ▶ 5.1 Reading a Dataset from a Tabular Text File (L05: Machine Learning with Scikit-Learn)
5.1 Reading a Dataset from a Tabular Text File (L05: Machine Learning with Scikit-Learn)
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 42 5.4 Intro to Scikit-learn (L05: Machine Learning with Scikit-Learn)
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 50 6.5 Gini & Entropy versus misclassification error (L06: Decision Trees)
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 54 7.2 Majority Voting (L07: Ensemble Methods)
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 56 7.4 Boosting and AdaBoost (L07: Ensemble Methods)
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 58 7.6 Random Forests (L07: Ensemble Methods)
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