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https://github.com/edyoda/data-science-complete-tutorial

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https://github.com/edyoda/data-science-complete-tutorial

decision-trees feature-selection linear-regression machine-learning nearest-neighbors numpy pandas pipeline scikit-learn

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In person training - https://www.edyoda.com/program/data-scientist-program



# Machine Learning Git Codebook



**Lesson 1 :** [Introduction to Numpy](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/1.%20NumPy.ipynb) [(Video)](https://www.edyoda.com/resources/videolisting/1263/)  

**Lesson 2 :** [Data Wrangling using Pandas](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/2.%20Pandas%20for%20Machine%20Learning.ipynb)  

**Lesson 3 :** [Plotting in Python](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/3.%20Plotting.ipynb)  

**Lesson 4 :** [Linear Models for Regression & Classification](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/4.%20Linear%20Models%20for%20Classification%20%26%20Regression.ipynb)  

**Lesson 5 :** [Preprocessing Data](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/5.%20PreProcessing.ipynb)  

**Lesson 6 :** [Decision Trees](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/6.%20Decision%20Tree.ipynb)  

**Lesson 7 :** [Naive Bayes](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/7.%20Naive%20Bayes.ipynb)  

**Lesson 8 :** [Composite Estimators](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/8.%20Composite%20Estimators%20using%20Pipelines%20%26%20FeatureUnions.ipynb)  

**Lesson 9 :** [Model Selection and Evaluation](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/9.%20Model%20Selection%20%26%20Evaluation.ipynb)  

**Lesson 10 :** [Feature Selection Techniques](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/10.%20Feature%20Selection%20Techniques.ipynb)  

**Lesson 11 :** [Nearest Neighbors](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/11.%20Nearest%20Neighbors.ipynb)  

**Lesson 12 :** [Clustering Techniques](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/12.%20Clustering%20Techniques.ipynb)  

**Lesson 13 :** [Anomaly Detection](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/13.%20Anomaly%20Detection.ipynb)  

**Lesson 14 :** [Support Vector Machines](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/14.%20Support%20Vector%20Machines.ipynb)  

**Lesson 15 :** [Dealing with Imbalanced Classes](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/15.%20Dealing%20with%20Imbalanced%20Classes.ipynb)  

**Lesson 16 :** [Ensemble Methods](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/16.%20Ensemble%20Methods.ipynb)  



## Case Study of Classic ML Problems

**Case 1 :** [Linear Regression](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/LR%20Example.ipynb)  

**Case 2 :** [Cancer Prediction](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/Cancer%20Prediction.ipynb)  

**Case 3 :** [Online Learning](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/Online%20Learning.ipynb)  

**Case 4 :** [Customer Churn Prediction](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/Project%20-%20Customer%20Churn%20Prediction.ipynb)  

**Case 5 :** [Income Prediction](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/Project%20-%20Income%20Prediction.ipynb)  

**Case 6 :** [Predicting Employee Exit](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/Project%20-%20Predicting%20Employee%20Exit.ipynb)  

**Case 7 :** [Face Generation](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/Project%20-%20Face%20Generation.ipynb)  

**Case 8 :** [Finding Similar Houses](https://github.com/zekelabs/data-science-complete-tutorial/blob/master/Project%20-%20Finding%20Similar%20Houses.ipynb)  



## The Free courses available on EdYoda



**Python** - https://www.edyoda.com/course/98 



**Angular** - https://www.edyoda.com/course/1227



**Machine Learning** - https://www.edyoda.com/course/1416 



**Dog Breed Prediction Project** - https://www.edyoda.com/course/1336  



**AI Project - Web application for Object Identification** - https://www.edyoda.com/course/1185  



**Numpy** - https://www.edyoda.com/course/1263 



**Tensorflow** - https://www.edyoda.com/course/99  



**Amazon Web Services** - https://www.edyoda.com/course/1410  



**DevOps** - https://www.edyoda.com/course/100 



**Android** -   

https://www.edyoda.com/course/101  

https://www.edyoda.com/course/1173   



**Deep Reinforcement Learning** - https://www.edyoda.com/course/1421  



**Knowledge Graphs, Deep Learning, Reasoning** - https://www.edyoda.com/course/1420 



**Natural Language Processing** - https://www.edyoda.com/course/1419 



**GAN Miniseries** - https://www.edyoda.com/course/1418



**Implementing Java Api's work** - https://www.edyoda.com/channel/2398/ 



**Introduction to Neural Nets** - https://www.edyoda.com/channel/2500/



**Videos from deep cognition studio** - https://www.edyoda.com/channel/2380/  



## About Us

We want to democratize education and create free quality course content.