Machine-Learning-Fundamentals

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Machine-Learning-Fundamentals

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# MACHINE LEARNING FUNDAMENTALS
<img src="https://storage.googleapis.com/ehimages/2017/11/27/img_ea7b6908595c191f0a314cadc1a9682d_1511777531136_processed_original.jpg"></img>
### <a href="https://github.com/Junaidkh/Machine-Learning-Fundamentals/tree/master/Machine%20Learning%20Foundation"> Machine Learning Foundation</a>
 This whole section is done from the <a href="https://www.coursera.org/learn/ml-foundations/home/welcome">University of Washington</a>
 <ol>
 	<li><a href="https://github.com/Junaidkh/Machine-Learning-Fundamentals/blob/master/Machine%20Learning%20Foundation/1%20intro.pdf">Introduction to Machine Learning </a></li>
 <li><a href="https://github.com/Junaidkh/Machine-Learning-Fundamentals/blob/master/Machine%20Learning%20Foundation/Getting%20started%20with%20Jupyter%20Notebook.ipynb">Getting Started with Jupyter Notebook </a></li>
 <li><a href="https://github.com/Junaidkh/Machine-Learning-Fundamentals/blob/master/Machine%20Learning%20Foundation/Turi%20Getting%20Started%20with%20SFrames.ipynb">Getting Started with Turi </a></li>
 </ol>

### <a href="https://github.com/Junaidkh/Machine-Learning-Fundamentals/tree/master/K%20Nearest%20Neighbor"> K-Nearest Neighbor Algorithm </a>
This Section is covered from 2 sources: 
First : IBM -> This provides me theory on KNN very well 
Second: Datacamp -> Datacamp helps me in implementing KNN Technique.
<ol>
 <li><a href="https://github.com/Junaidkh/Machine-Learning-Fundamentals/blob/master/K%20Nearest%20Neighbor/Knn%20notes.pdf">K-NN notes made by me </a></li>
 <li><a href="https://github.com/Junaidkh/Machine-Learning-Fundamentals/blob/master/K%20Nearest%20Neighbor/Datacamp%20chapter1%20K%20Nearest%20Neighbor%20Notes.pdf">Datacamp Notes on KNN </a></li>
 <li><a href="https://github.com/Junaidkh/Machine-Learning-Fundamentals/blob/master/K%20Nearest%20Neighbor/K-Nearest-neighbors-CustCat-py-v1.ipynb">Finding Customer Category using KNN </a></li>
</ol>

### <a href="https://github.com/Junaidkh/Machine-Learning-Fundamentals/tree/master/Linear%20Regression"> Linear Regression </a>
This section is covered from 3 different sources:
First : IBM 
Second : Datacamp 
Third : University of Washington -> This course helps me in understanding the mathematics behind the machine learnign algorithm>
<ol>
	<li><a href="https://github.com/Junaidkh/Machine-Learning-Fundamentals/blob/master/Linear%20Regression/Regression%20selfnotes%201.pdf"> Regression Notes</a></li>
	<li><a href="https://github.com/Junaidkh/Machine-Learning-Fundamentals/blob/master/Linear%20Regression/Datacamp%20Regression%20Notes.pdf">Datacamp Regression Notes</a></li>
	<li><a href="https://github.com/Junaidkh/Machine-Learning-Fundamentals/blob/master/Linear%20Regression/Simple%20Linear%20Regression%20with%20Gradient%20Descent%20Self%20notes.pdf"> Simple Linear Regression using Gradient Descent Notes</a></li>
	<li><a href="https://github.com/Junaidkh/Machine-Learning-Fundamentals/blob/master/Linear%20Regression/Datacamp%20chapter3%20Fine%20Tuning%20Your%20Model%20Notes.pdf">How to Tune your model?</a></li>
	<li><a href="https://github.com/Junaidkh/Machine-Learning-Fundamentals/blob/master/Linear%20Regression/Simple-Linear-Regression-Co2-py-v1.ipynb"> Simple Linear Regression Workbook</a></li>
	<li><a href="https://github.com/Junaidkh/Machine-Learning-Fundamentals/blob/master/Linear%20Regression/Mulitple-Linear-Regression-Co2-py-v1.ipynb">Multiple Linear Regression Workbook</a></li>
	<li><a href="https://github.com/Junaidkh/Machine-Learning-Fundamentals/blob/master/Linear%20Regression/Polynomial-Regression-Co2-py-v1.ipynb">Polynomial Regression Workbook</a></li>
	<li><a href="https://github.com/Junaidkh/Machine-Learning-Fundamentals/blob/master/Linear%20Regression/NoneLinearRegression-py-v1.ipynb">Non-Linear Regression Workbook</a></li>
</ol>

### <a href="https://github.com/Junaidkh/Machine-Learning-Fundamentals/tree/master/Logistic%20Regression"> Logistic Regression </a>
This section is covered from 2 different sources:
First : IBM 
Second : Datacamp 
<ol>
	<li><a href="https://github.com/Junaidkh/Machine-Learning-Fundamentals/blob/master/Logistic%20Regression/Self%20notes%20Logistic%20Regression.pdf"> Logistic Regression Notes </a></li>
	<li><a href="https://github.com/Junaidkh/Machine-Learning-Fundamentals/blob/master/Logistic%20Regression/Hyperparameter%20tuning%20and%20Data%20preprocessing.pdf"> HyperParameter Tuning and Data Preprocessing</a></li>
	<li><a href="https://github.com/Junaidkh/Machine-Learning-Fundamentals/blob/master/Logistic%20Regression/Logistic-Regression-churn-py-v1.ipynb"> Working on Churn data using Logistic Regression </a></li>
</ol>

### <a href="https://github.com/Junaidkh/Machine-Learning-Fundamentals/tree/master/Decision%20Tree"> Decision Tree </a>
IBM 
<ol>
	<li><a href="https://github.com/Junaidkh/Machine-Learning-Fundamentals/blob/master/Decision%20Tree/Decision%20Tree.pdf"> Decision Tree Notes </a></li>
	<li><a href="https://github.com/Junaidkh/Machine-Learning-Fundamentals/blob/master/Decision%20Tree/Decision-Trees-drug-py-v1.ipynb"> Decision Tree notebook</a></li>
</ol>

STAY TUNED MANY MORE MATERIAL IS COMING 
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