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Sprint Instructions for scikit-learn Vol 2

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Scikit-learn sprint instructions

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Channel Intro - Applied Machine Learning

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Applied ML 2020 - 21 - Time Series and Forecasting

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Applied ML 2020 - 20 - Advanced neural networks

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Applied ML 2020 - 19 - Keras and Convolutional neural nets

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Applied ML 2020 - 18 - Neural Networks

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Applied ML 2020 - 17 - Word vectors and document embeddings

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Applied ML 2020 - 16 - Topic models for text data

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Applied ML 2020 - 15 - Working with Text Data

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Applied ML 2020 - 14 - Clustering and Mixture Models

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Applied ML 2020 - 13 - Dimensionality reduction

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Applied ML 2020 - 12 - AutoML (plus some feature selection)

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Applied ML 2020 - 11 - Model Inspection and Feature Selection

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Applied ML 2020 - 10 - Calibration, Imbalanced data

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Applied ML 2020 - 09 - Model Evaluation and Metrics

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Applied ML 2020 - 08 - Gradient Boosting

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Applied ML 2020 - 07 - Decision Trees and Random Forests

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Applied ML 2020 - 06 - Linear Models for Classification

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Applied ML 2020 - 05 - Linear Models for Regression

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Applied ML 2020 - 04 - Preprocessing

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Applied ML 2020 - 03 Supervised learning and model validation

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Applied ML 2020 - 02 Visualization and matplotlib

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Applied ML 2020 - 01 Introduction

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WiMLDS Sprint Instructions NYC Sprint August 2019

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Applied Machine Learning 2019 - Lecture 25 - Recommender Systems (Nicolas Hug)

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Applied Machine Learning 2019 - Lecture 24 - Recap and summary

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Applied Machine Learning 2019 - Lecture 23 - Basics of Time Series

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Applied Machine Learning 2019 - Lecture 22 - Advanced Neural Networks

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Applied Machine Learning 2019 - Lecture 21 - Keras and Convolutional Neural Networks

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