xgboost代码回归matlab-Data-Science-Learning:入门数据科学笔记和资源

# Data Science ##### Exploratory, training and resource code for many Data Science usage cases in python 3.6 ## Resources Learning Sequence | Title | Link | Notes ----------------- | ----- | ---- | ----- 1 | Data Types for Data Science | [iPython Notebook](https://github.com/cschellenberger/Data-Science-Learning/blob/master/Data%20Types%20for%20Data%20Science.ipynb) | General overview of datatypes in Python 2 | Unix Shell Commands for Data Science | [DataCamp Course](https://www.datacamp.com/courses/introduction-to-shell-for-data-science) | Fundamentals of using unix commands 3 | Git Introduction | [DataCamp Course](https://www.datacamp.com/courses/introduction-to-git-for-data-science) | General commands for commiting, staging, deleting, and working with history 4 | Intro to Data Science | [iPython Notebook](https://github.com/cschellenberger/Data-Science-Learning/blob/master/Data%20Science%20Intro.ipynb) | Fundamentals of Python and an introduction to the Data Science stack 5 | Data Science Toolbox Part 1 | [iPython Notebook](https://github.com/cschellenberger/Data-Science-Learning/blob/master/Python%20Data%20Science%20Toolbox%20(Part%201).ipynb) | Data wrangling, computation, visualization, and statistical practices 6 | Introduction to Data Visualization | [iPython Notebook](https://github.com/cschellenberger/Data-Science-Learning/blob/master/Introduction%20to%20Data%20Visualization%20in%20Python.ipynb) | Matplotlib introduction to main data plots and customization 7 | Pandas Foundation | [iPython Notebook](https://github.com/cschellenberger/Data-Science-Learning/blob/master/Pandas%20Foundations.ipynb) | Detailed technical foundation in using the pandas package for data wrangling and visualization 8 | Manipulating Dataframes with Pandas | [iPython Notebook](https://github.com/cschellenberger/Data-Science-Learning/blob/master/Manipulating%20DataFrames%20with%20pandas.ipynb) | Techniques for working with general DataFrame processes 9 | Merging DataFrames with Pandas | [iPython Notebook](https://github.com/cschellenberger/Data-Science-Learning/blob/master/Merging%20DataFrames.ipynb) | Working with multiple related DataFrames 10 | Python Data Science Toolbox (Part 2) | [iPython Notebook](https://github.com/cschellenberger/Data-Science-Learning/blob/master/Python%20Data%20Science%20Toolbox%20(Part%202).ipynb) | Iterators and generators 11 | Importing Data in Python (Part 1) | [iPython Notebook](https://github.com/cschellenberger/Data-Science-Learning/blob/master/Importing%20Data%20in%20Python%20(Part%201).ipynb) | Importing Flat Files, MATLAB, Strata, and SQL data 12 | Importing Data in Python (Part 2) | [iPython Notebook](https://github.com/cschellenberger/Data-Science-Learning/blob/master/Importing%20Data%20in%20Python%20(Part%202).ipynb) | Importing using URLs and APIs 13 | SQL Beginner&Intermediate Tutorial | [Mode Report](https://modeanalytics.com/cschellenberger/reports/00ebaa5e3f8e) | Basic syntax, logical operators, and joins 14 | Statistical Thinking (Part 1 & 2) | [iPython Notebook](https://github.com/cschellenberger/Data-Science-Learning/blob/master/Statistical%20Thinking.ipynb) | Statistical EDA, Hypothesis Testing 15 | Bias and Regression | [Markdown](https://github.com/cschellenberger/Data-Science-Learning/blob/master/Bias%20and%20Regression.md) | Intro to Statistical Learning + Machine Learning principles 16 | Capability of a Model | [Markdown](https://github.com/cschellenberger/Data-Science-Learning/blob/master/Capability%20of%20a%20Model.md) | A look at different ML methods and algorithms 17 | Supervised Learning | [iPython Notebook](https://github.com/cschellenberger/Data-Science-Learning/blob/master/Supervised%20Learning%20with%20scikit-learn.ipynb) | Regression and Classification ML Techniques 18 | SVM and Trees | [iPython Notebook](https://github.com/cschellenberger/Data-Science-Learning/blob/master/SVM%20and%20Trees.ipynb) | Support Vector Machines and Trees 19 | sklearn Supervised Learning | [iPython Notebook](https://github.com/cschellenberger/Data-Science-Learning/blob/master/Supervised%20Learning%20with%20scikit-learn.ipynb) | Introduction to Classifiers and Supervised Problems 20 | Bayesian Methods | [iPython Notebook](https://github.com/cschellenberger/Data-Science-Learning/blob/master/Bayesian%20Methods-Text%20Data.ipynb) | Bayesian Techniques for Text Data 21 | Unsupervised Learning | [iPython Notebook](https://github.com/cschellenberger/Data-Science-Learning/blob/master/Clustering.ipynb) | Clustering Techniques 22 | Amazon Web Services - SageMaker | [iPython Notebook](https://github.com/cschellenberger/Data-Science-Learning/blob/master/AWS%20SageMaker%20Tutorial.ipynb) | Amazon ML Platform Example 23 | Network Analysis in Python | [iPython Notebook](https://github.com/cschellenberger/Data-Science-Learning/blob/master/Network%20Analysis%20in%20Python.ipynb) | Graph Analysis in Python 24 | Deep Learning Intro | [iPython Notebook](https://github.com/cschellenberger/Data-Science-Learning/blob/master/Deep%20Learning%20Introduction.ipynb) | Basics of DL with Keras 25 | Data Science YouTube Presentation Notes | [Markdown](https://github.com/cschellenberger/Data-Science-Learning/blob/master/Review%20and%20Notes.md) | YouTube Data Science [Playlist](https://www.youtube.com/playlist?list=PLDgEmOFD-gTZTn0AvPYo2rhGMLJMCjVHz) 26 | Introduction to PySpark | [iPython Notebook](https://github.com/cschellenberger/Data-Science-Learning/blob/master/PySpark.ipynb) | DataTables, SQL queries, Machine Learning Example 27 | NLP Fundamentals | [iPython Notebook](https://github.com/cschellenberger/Data-Science-Learning/blob/master/NLP%20Fundamentals%20in%20Python.ipynb) | Regular Expressions, Basic Methods, and Applications of NLP 28 | SQL for Data Science | [iPython Notebook](https://github.com/cschellenberger/Data-Science-Learning/blob/master/SQL%20for%20Data%20Science.ipynb) | Advanced Joins and Subqueries 29 | Intro to Databases | [iPython Notebook](https://github.com/cschellenberger/Data-Science-Learning/blob/master/Introduction%20to%20Databases.ipynb) | Programmatically work with databases through sqlalchemy 30 | Interactive Visualizations - Bokeh | [iPython Notebook](https://github.com/cschellenberger/Data-Science-Learning/blob/master/Interactive%20Data%20Visualization%20with%20Bokeh.ipynb) | Generate local and server visualization applications 31 | Machine Learning with XGBoost | [iPython Notebook](https://github.com/cschellenberger/Data-Science-Learning/blob/master/Extreme%20Gradient%20Boosting%20with%20XGBoost.ipynb) | Classification, Regression, and Pipeline methods with the versatile XGBoost library 32 | Writing Efficient Python Code | [iPython Notebook](https://github.com/cschellenberger/Data-Science-Learning/blob/master/Writing%20Efficient%20Python%20Code.ipynb) | Analyzing and understanding efficiencies in Python 33 | Analyzing Police Activity with pandas | [iPython Notebook](https://github.com/cschellenberger/Data-Science-Learning/blob/master/Analyzing%20Police%20Activity%20with%20pandas.ipynb) | An analytical overview or refresher in pandas 34 | Machine Learning with Tree-Based Models in Python | [iPython Notebook](https://github.com/cschellenberger/Data-Science-Learning/blob/master/Machine%20Learning%20with%20Tree-Based%20Models%20in%20Python.ipynb) | Supervised learning models for classificaiton and regression ## Future Learning [Mathematics for Machine Learning: Linear Algebra](https://www.coursera.org/learn/linear-algebra-machine-learning)