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Kevin Markham 2019-11-11 08:51:04 -05:00
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# Introduction to machine learning with scikit-learn
This video series will teach you how to solve machine learning problems using Python's popular scikit-learn library. It was [featured on Kaggle's blog](http://blog.kaggle.com/author/kevin-markham/) in 2015.
There are **9 video tutorials** totaling 4 hours, each with a corresponding **Jupyter notebook**. The notebook contains everything you see in the video: code, output, images, and comments.
This video series will teach you how to solve machine learning problems using Python's popular scikit-learn library. There are **9 video tutorials** totaling 4 hours, each with a corresponding **Jupyter notebook**. The notebook contains everything you see in the video: code, output, images, and comments.
**Note:** The notebooks in this repository have been updated to use Python 3.6 and scikit-learn 0.19.1. The original notebooks (shown in the video) used Python 2.7 and scikit-learn 0.16, and can be downloaded from the [archive branch](https://github.com/justmarkham/scikit-learn-videos/tree/archive). You can read about how I updated the code in this [blog post](https://www.dataschool.io/how-to-update-your-scikit-learn-code-for-2018/).
@ -14,35 +12,35 @@ Once you complete this video series, I recommend enrolling in my online course,
## Table of Contents
1. What is machine learning, and how does it work? ([video](https://www.youtube.com/watch?v=elojMnjn4kk&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=1), [notebook](01_machine_learning_intro.ipynb), [blog post](http://blog.kaggle.com/2015/04/08/new-video-series-introduction-to-machine-learning-with-scikit-learn/))
1. What is machine learning, and how does it work? ([video](https://www.youtube.com/watch?v=elojMnjn4kk&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=1), [notebook](01_machine_learning_intro.ipynb))
- What is machine learning?
- What are the two main categories of machine learning?
- What are some examples of machine learning?
- How does machine learning "work"?
2. Setting up Python for machine learning: scikit-learn and Jupyter Notebook ([video](https://www.youtube.com/watch?v=IsXXlYVBt1M&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=2), [notebook](02_machine_learning_setup.ipynb), [blog post](http://blog.kaggle.com/2015/04/15/scikit-learn-video-2-setting-up-python-for-machine-learning/))
2. Setting up Python for machine learning: scikit-learn and Jupyter Notebook ([video](https://www.youtube.com/watch?v=IsXXlYVBt1M&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=2), [notebook](02_machine_learning_setup.ipynb))
- What are the benefits and drawbacks of scikit-learn?
- How do I install scikit-learn?
- How do I use the Jupyter Notebook?
- What are some good resources for learning Python?
3. Getting started in scikit-learn with the famous iris dataset ([video](https://www.youtube.com/watch?v=hd1W4CyPX58&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=3), [notebook](03_getting_started_with_iris.ipynb), [blog post](http://blog.kaggle.com/2015/04/22/scikit-learn-video-3-machine-learning-first-steps-with-the-iris-dataset/))
3. Getting started in scikit-learn with the famous iris dataset ([video](https://www.youtube.com/watch?v=hd1W4CyPX58&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=3), [notebook](03_getting_started_with_iris.ipynb))
- What is the famous iris dataset, and how does it relate to machine learning?
- How do we load the iris dataset into scikit-learn?
- How do we describe a dataset using machine learning terminology?
- What are scikit-learn's four key requirements for working with data?
4. Training a machine learning model with scikit-learn ([video](https://www.youtube.com/watch?v=RlQuVL6-qe8&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=4), [notebook](04_model_training.ipynb), [blog post](http://blog.kaggle.com/2015/04/30/scikit-learn-video-4-model-training-and-prediction-with-k-nearest-neighbors/))
4. Training a machine learning model with scikit-learn ([video](https://www.youtube.com/watch?v=RlQuVL6-qe8&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=4), [notebook](04_model_training.ipynb))
- What is the K-nearest neighbors classification model?
- What are the four steps for model training and prediction in scikit-learn?
- How can I apply this pattern to other machine learning models?
5. Comparing machine learning models in scikit-learn ([video](https://www.youtube.com/watch?v=0pP4EwWJgIU&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=5), [notebook](05_model_evaluation.ipynb), [blog post](http://blog.kaggle.com/2015/05/14/scikit-learn-video-5-choosing-a-machine-learning-model/))
5. Comparing machine learning models in scikit-learn ([video](https://www.youtube.com/watch?v=0pP4EwWJgIU&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=5), [notebook](05_model_evaluation.ipynb))
- How do I choose which model to use for my supervised learning task?
- How do I choose the best tuning parameters for that model?
- How do I estimate the likely performance of my model on out-of-sample data?
6. Data science pipeline: pandas, seaborn, scikit-learn ([video](https://www.youtube.com/watch?v=3ZWuPVWq7p4&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=6), [notebook](06_linear_regression.ipynb), [blog post](http://blog.kaggle.com/2015/05/28/scikit-learn-video-6-linear-regression-plus-pandas-seaborn/))
6. Data science pipeline: pandas, seaborn, scikit-learn ([video](https://www.youtube.com/watch?v=3ZWuPVWq7p4&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=6), [notebook](06_linear_regression.ipynb))
- How do I use the pandas library to read data into Python?
- How do I use the seaborn library to visualize data?
- What is linear regression, and how does it work?
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- What are some evaluation metrics for regression problems?
- How do I choose which features to include in my model?
7. Cross-validation for parameter tuning, model selection, and feature selection ([video](https://www.youtube.com/watch?v=6dbrR-WymjI&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=7), [notebook](07_cross_validation.ipynb), [blog post](http://blog.kaggle.com/2015/06/29/scikit-learn-video-7-optimizing-your-model-with-cross-validation/))
7. Cross-validation for parameter tuning, model selection, and feature selection ([video](https://www.youtube.com/watch?v=6dbrR-WymjI&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=7), [notebook](07_cross_validation.ipynb))
- What is the drawback of using the train/test split procedure for model evaluation?
- How does K-fold cross-validation overcome this limitation?
- How can cross-validation be used for selecting tuning parameters, choosing between models, and selecting features?
- What are some possible improvements to cross-validation?
8. Efficiently searching for optimal tuning parameters ([video](https://www.youtube.com/watch?v=Gol_qOgRqfA&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=8), [notebook](08_grid_search.ipynb), [blog post](http://blog.kaggle.com/2015/07/16/scikit-learn-video-8-efficiently-searching-for-optimal-tuning-parameters/))
8. Efficiently searching for optimal tuning parameters ([video](https://www.youtube.com/watch?v=Gol_qOgRqfA&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=8), [notebook](08_grid_search.ipynb))
- How can K-fold cross-validation be used to search for an optimal tuning parameter?
- How can this process be made more efficient?
- How do you search for multiple tuning parameters at once?
- What do you do with those tuning parameters before making real predictions?
- How can the computational expense of this process be reduced?
9. Evaluating a classification model ([video](https://www.youtube.com/watch?v=85dtiMz9tSo&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=9), [notebook](09_classification_metrics.ipynb), [blog post](http://blog.kaggle.com/2015/10/23/scikit-learn-video-9-better-evaluation-of-classification-models/))
9. Evaluating a classification model ([video](https://www.youtube.com/watch?v=85dtiMz9tSo&list=PL5-da3qGB5ICeMbQuqbbCOQWcS6OYBr5A&index=9), [notebook](09_classification_metrics.ipynb))
- What is the purpose of model evaluation, and what are some common evaluation procedures?
- What is the usage of classification accuracy, and what are its limitations?
- How does a confusion matrix describe the performance of a classifier?