├── Classification ├── week 1 - Linear Classifiers │ ├── Week 1 – Linear Classifiers mobile.pdf │ ├── Week 1 – Linear Classifiers.docx │ └── Week 1 – Linear Classifiers.pdf ├── week 2 – Learning Parameters of Logistic Regression │ ├── Week 2 – Learning Parameters of Logistic Regression mobile.pdf │ ├── Week 2 – Learning Parameters of Logistic Regression.docx │ └── Week 2 – Learning Parameters of Logistic Regression.pdf ├── week 3 - Decision Trees │ ├── Classification Week 3 - Decision Trees Mobile.pdf │ ├── Classification Week 3 - Decision Trees.docx │ └── Classification Week 3 - Decision Trees.pdf ├── week 4 - Overfitting in Decision Trees │ ├── Classification Week 4 Overfitting in Decision Trees Mobile.pdf │ ├── Classification Week 4 Overfitting in Decision Trees.docx │ └── Classification Week 4 Overfitting in Decision Trees.pdf ├── week 5 - Boosting │ ├── Classification Week 5 – Boosting mobile.pdf │ ├── Classification Week 5 – Boosting.docx │ └── Classification Week 5 – Boosting.pdf ├── week 6 - Precision-Recall │ ├── Week 6 Precision and Recall mobile.pdf │ ├── Week 6 Precision and Recall.docx │ └── Week 6 Precision and Recall.pdf └── week 7 - Scaling to Huge Datasets and Online Learning │ ├── Week 7 Scaling to Huge Datasets mobile.pdf │ ├── Week 7 Scaling to Huge Datasets.docx │ └── Week 7 Scaling to Huge Datasets.pdf ├── Clustering and Retrieval ├── week 2 - Nearest Neighbor Search │ ├── Clustering and Retrieval – Week 2 – k Nearest Neighbors Search mobile.pdf │ ├── Clustering and Retrieval – Week 2 – k Nearest Neighbors Search.docx │ └── Clustering and Retrieval – Week 2 – k Nearest Neighbors Search.pdf ├── week 3 - Clustering with k-means │ ├── Clustering and Retrieval - Week 3 - Clustering with k-means.docx │ └── Clustering and Retrieval - Week 3 - Clustering with k-means.pdf ├── week 4 - Mixture Models │ ├── Clustering and Retrieval - Week 4 - Mixture Models - mobile.pdf │ ├── Clustering and Retrieval - Week 4 - Mixture Models.docx │ └── Clustering and Retrieval - Week 4 - Mixture Models.pdf ├── week 5 - Mixed Membership Modeling via Latent Dirichlet Allocation │ ├── Clustering and Retrieval - Week 5 - Mixed Membership Modeling via Latent Dirichlet Allocation - mobile.pdf │ ├── Clustering and Retrieval - Week 5 - Mixed Membership Modeling via Latent Dirichlet Allocation.docx │ └── Clustering and Retrieval - Week 5 - Mixed Membership Modeling via Latent Dirichlet Allocation.pdf └── week 6 - Hierarchical Clustering │ ├── Clustering and Retrieval - Week 6 - Hierarchical Clustering - mobile.pdf │ ├── Clustering and Retrieval - Week 6 - Hierarchical Clustering.docx │ └── Clustering and Retrieval - Week 6 - Hierarchical Clustering.pdf ├── LICENSE ├── README.md └── Regression ├── week 1 - Simple Linear Regression ├── Simple Linear Regression - mobile.pdf ├── Simple Linear Regression.docx └── Simple Linear Regression.pdf ├── week 2 - Multiple Regression ├── Multiple Linear Regression - mobile.pdf ├── Multiple Linear Regression.docx └── Multiple Linear Regression.pdf ├── week 3 - Assessing Model Performance ├── Regression - Assessing Performance of the Model - mobile.pdf ├── Regression - Assessing Performance of the Model.docx └── Regression - Assessing Performance of the Model.pdf ├── week 4 - Ridge Regression ├── Ridge Regression - mobile.pdf ├── Ridge Regression.docx └── Ridge Regression.pdf ├── week 5 - Feature Selection ├── Feature Selection – Lasso Regression - mobile.pdf ├── Feature Selection – Lasso Regression.docx └── Feature Selection – Lasso Regression.pdf └── week 6 - Non-parametric Regression ├── Non-parametric regression - mobile.pdf ├── Non-parametric regression.docx └── Non-parametric regression.pdf /Classification/week 1 - Linear Classifiers/Week 1 – Linear Classifiers mobile.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ezward/machinelearningnotes/712493c65da442cc70c884e1c0e1eff70a0778ef/Classification/week 1 - Linear Classifiers/Week 1 – Linear Classifiers mobile.pdf -------------------------------------------------------------------------------- /Classification/week 1 - 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UNLESS REQUIRED BY LAW, NO COPYRIGHT HOLDER OR CONTRIBUTOR WILL 199 | BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, OR CONSEQUENTIAL 200 | DAMAGES ARISING IN ANY WAY OUT OF THE USE OF THE PACKAGE, EVEN IF 201 | ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 202 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # machinelearningnotes 2 | ## Notes for the Coursera Machine Learning Specialization created by the University of Washingtion 3 | 4 | These notes were useful to me and I hope they will be useful to others that that take these UofWa courses or other courses in Machine Learning. 5 | 6 | Based on knowledged gained in the Clustering and Retrieval course, I've implemented a few clustering algorithms with an interactive web UI in this repo; https://github.com/Ezward/clustering-javascript. It is written in simple JavaScript and so should be easy to understand. It includes an implementation of DBSCAN, which was not covered extensively in the course. 7 | 8 | ## Downloading 9 | The best way to download the files is to clone the repository. Then when an update is posted you can simply do a pull to get all the updates. Also, if you find a 'bug', you can fix it and submit a pull request so the fix gets into the repo. 10 | 11 | If you do not want to clone, you can download all files by selecting the ***Download Zip*** button from the repository's home page (this page). 12 | 13 | To download an individual file, navigate to the file and select it. Wait for the preview to render and then select the ***Raw*** button or, in the case of Word docs, select the ***View Raw*** link in the preview area. You will be offered the file download dialog. 14 | 15 | For mobile users, you may have to view the desktop version of github.com; the mobile web does not seem to handle these files correctly. On android, if you are offered a choice of apps after selecting the file, choose your browser so it will render the preview. If you choose Adobe Acrobat, it will not correctly render the pdf. 16 | 17 | ***Please note:*** these notes have not been edited or vetted by anyone at this point. It is possible that they may have errors or misinformation in them. If you find errors, omissions or misinformation, please contribute fixes. I am hoping that the community can use and improve these notes so that they will become a a solid resource for the specialization. 18 | 19 | ## Contributing 20 | The repository contains the Microsoft Word files created with Microsoft Word for Mac 2011, version 14.6.0 as well as pdfs suitable for printing and for reading on a phone. The best way to contribute changes is to clone the repository, edit the Word file (I have track changes turned on) and submit a pull request. I can then merge in changes to the master branch. 21 | 22 | If you don't want to go through all that, but you find a problem, please consider submitting an issue by selecting the 'Issues' tab on this page and selecting the green "New Issue" button. 23 | 24 | 25 | -------------------------------------------------------------------------------- /Regression/week 1 - Simple Linear Regression/Simple Linear Regression - mobile.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ezward/machinelearningnotes/712493c65da442cc70c884e1c0e1eff70a0778ef/Regression/week 1 - Simple Linear Regression/Simple Linear Regression - mobile.pdf -------------------------------------------------------------------------------- /Regression/week 1 - Simple Linear Regression/Simple Linear Regression.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ezward/machinelearningnotes/712493c65da442cc70c884e1c0e1eff70a0778ef/Regression/week 1 - 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