#Data Science Lab

###Contents

• Data Science Process
• Data Acquisition
• [Feature Extraction and Transformation](#feature-extraction-and- transformation)
• Model Selection and Evaluation
• Machine Learning Algorithms
• Data Visualization
• References

#Machine Learning Algorithms back to top

Classification and Regression

• Generalized Linear Models [Notebook]
• Logistic Regression [Notebook]
• Support Vector Machine [Notebook]
• Random Forest [Notebook]
• Gradient Boosting Regression Trees [Notebook]
• Naive Bayes [Notebook]

Clustering and Dimensionality Reduction

• k-means [Notebook]
• Principal Component Analysis (PCA) [Notebook]

Recommender Systems

• Collaborative Filtering
• Singular Value Decomposition (SVD)
• Alternating Least Squares (ALS)

#Feature Extraction and Transformation back to top

• Data Wrangling [Notebook]
• TF-IDF
• Word2Vec
• Standard Scaler
• Normalizer
• Encoding Categorical
• Principal Component Analysis

#Data Visualization back to top

• Matplotlib / Seaborn [Notebook]
• plot.ly
• d3.js

#Data Acquisition back to top

• Requests - Web scraping
• Mechanize - Web scraping
• BeautifulSoup - HTML
• Twitter API
• Marvel API
• Database (Relational database/SQL)

#Model Selection and Evaluation back to top

• Cross Validation
• Classification Metrics
• Regression Metrics
• Clustering Metrics
• Learning and Validation Curves

###References back to top

• The Elements of StatisticalLearning
• Pattern Recognition and Machine Learning
• Stanford CS229 - Machine Learning
• Stanford - Probabilistic GraphicalModels
• Harvard CS109 - Data Science
• Caltech - Learning From Data
• Carnegie Mellon University - Machine Learning
• University of Toronto - Neural Networks for Machine Learning

###Data Science Process back to top