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Machine Learning notebooks for refreshing concepts.
| Date | Stars |
|---|---|
| 2026-07-31 | 554 |
| 2026-08-04 | 553 |
| 2026-08-06 | 553 |
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# Machine Learning Notebooks
Helpful jupyter noteboks that I compiled while learning Machine Learning and Deep Learning from various sources on the Internet.
## NumPy Basics:
1. [NumPy Basics](http://nbviewer.jupyter.org/github/maykulkarni/Machine-Learning-Notebooks/blob/master/00.%20NumPy%20Basics/1.%20NumPy%20Basics.ipynb)
## Data Preprocessing:
1. [Feature Selection](http://nbviewer.jupyter.org/github/maykulkarni/Machine-Learning-Notebooks/blob/master/01.%20Data%20Preprocessing/1.%20Feature%20Selection.ipynb): Imputing missing values, Encoding, Binarizing.
2. [Feature Scaling](http://nbviewer.jupyter.org/github/maykulkarni/Machine-Learning-Notebooks/blob/master/01.%20Data%20Preprocessing/2.%20Scaling%2C%20Normalizing.ipynb): Min-Max Scaling, Normalizing, Standardizing.
3. [Feature Extraction](http://nbviewer.jupyter.org/github/maykulkarni/Machine-Learning-Notebooks/blob/master/01.%20Data%20Preprocessing/3.%20Feature%20Extraction.ipynb): CountVectorizer, DictVectorizer, TfidfVectorizer.
## Regression
1. Linear & Multiple Regression
* a. [Theory and Derivation](http://nbviewer.jupyter.org/github/maykulkarni/Machine-Learning-Notebooks/blob/master/02.%20Regression/1A.%20Linear%20Regression%20and%20Gradient%20Descent%28Theory%29.ipynb)
* b. [Linear Regression from scratch](http://nbviewer.jupyter.org/github/maykulkarni/Machine-Learning-Notebooks/blob/master/02.%20Regression/1B.%20Linear%20Regression%20and%20Gradient%20Descent%20.ipynb)
* c. [Assumptions in Linear Regression](http://nbviewer.jupyter.org/github/maykulkarni/Machine-Learning-Notebooks/blob/master/02.%20Regression/1C.%20Assumptions%20in%20Linear%20Regression%20and%20Dummy%20variables.ipynb): Assumptions in Linear Regression, Dummy Variable Trap
* d. [Linear Regression using Scikit-learn](http://nbviewer.jupyter.org/github/maykulkarni/Machine-Learning-Notebooks/blob/master/02.%20Regression/1C.%20Simple%20and%20Multiple%20Regression%20using%20Sci-kit%20learn.ipynb): Simple and Multivariable Regression using Scikit-learn.
2. [Backward Elimination](http://nbviewer.jupyter.org/github/maykulkarni/Machine-Learning-Notebooks/blob/master/02.%20Regression/2.%20Backward%20Elimination.ipynb): Method of Backward Elimination, P-values.
3. [Polynomial Regression](http://nbviewer.jupyter.org/github/maykulkarni/Machine-Learning-Notebooks/blob/master/02.%20Regression/3.%20Polynomial%20Regression.ipynb)
4. [Support Vector Regression](http://nbviewer.jupyter.org/github/maykulkarni/Machine-Learning-Notebooks/blob/master/02.%20Regression/4.%20Support%20Vector%20Regression.ipynb)
5. [Decision Tree Regression](http://nbviewer.jupyter.org/github/maykulkarni/Machine-Learning-Notebooks/blob/master/02.%20Regression/5.%20Decision%20Tree%20Regression.ipynb)
6. [Random Forest Regression](http://nbviewer.jupyter.org/github/maykulkarni/Machine-Learning-Notebooks/blob/master/02.%20Regression/6.%20Random%20Forest.ipynb)
7. [Robust Regression using Theil-Sen Regression](http://nbviewer.jupyter.org/github/maykulkarni/Machine-Learning-Notebooks/blob/master/02.%20Regression/8.%20Robust%20Regression%20(TheilSen%20Regressor).ipynb)
8. [Pipelines in Scikit-Learn](http://nbviewer.jupyter.org/github/maykulkarni/Machine-Learning-Notebooks/blob/master/02.%20Regression/9.%20Pipelines%20in%20Sklearn.ipynb)
## Classification
1. Logistic Regression
* a. [Logistic Regression and Gradient Descent](http://nbviewer.jupyter.org/github/maykulkarni/Machine-Learning-Notebooks/blob/master/03.%20Classification/1A.%20Logistic%20Regression%20and%20Gradient%20Descent.ipynb)
* b. [Deriving Logistic Regression](http://nbviewer.jupyter.org/github/maykulkarni/Machine-Learning-Notebooks/blob/master/03.%20Classification/1B.%20Deriving%20Logistic%20Regression%20.ipynb)
* c. [Logistic Regression using Gradient Descent](http://nbviewer.jupyter.org/github/maykulkarni/Machine-Learning-Notebooks/blob/master/03.%20Classification/1C.%20Logistic%20Regression%20using%20GradienExcerpt of 9,707 characters
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matched fp:6d30e49bc123888a, llm:Repository description and README: 'Machine Learning notebooks... compiling while learning Machine Learning and Deep Learning' with notebooks covering NumPy basics, data preprocessing, regression, classification, clustering, deep learning, NLP, reinforcement learning; topics include machine-learning, deep-learning, data-science-notebook, machine-learning-tutorials, etc.
matched fp:6d30e49bc123888a, llm:Repository description and README: 'Machine Learning notebooks... compiling while learning Machine Learning and Deep Learning' with notebooks covering NumPy basics, data preprocessing, regression, classification, clustering, deep learning, NLP, reinforcement learning; topics include machine-learning, deep-learning, data-science-notebook, machine-learning-tutorials, etc.
matched fp:6d30e49bc123888a, llm:Repository description and README: 'Machine Learning notebooks... compiling while learning Machine Learning and Deep Learning' with notebooks covering NumPy basics, data preprocessing, regression, classification, clustering, deep learning, NLP, reinforcement learning; topics include machine-learning, deep-learning, data-science-notebook, machine-learning-tutorials, etc.
matched fp:6d30e49bc123888a, llm:Repository description and README: 'Machine Learning notebooks... compiling while learning Machine Learning and Deep Learning' with notebooks covering NumPy basics, data preprocessing, regression, classification, clustering, deep learning, NLP, reinforcement learning; topics include machine-learning, deep-learning, data-science-notebook, machine-learning-tutorials, etc.