Applied Machine Learning from zero

These are all the posts where I described what I have learned about machine learning & Python (no better way to learn a new language than coupling it with a new topic).
This is a work in progress.

Introduction: data, ML and Python

  1. Big data, data science and machine learning explained
  2. Measures of central tendency / list and tuples
  3. The median / sort a list
  4. The mode / dictionary and loops
  5. Deviation measures / power operator
  6. The coefficient of variation / exceptions
  7. Automatic documentation
  8. Rounding error
  9. Automatic tests
  10. Plotting a bar chart
  11. Plotting on the web
  12. Quartiles and summary statistics / array subsets
  13. Visualize quartiles
  14. Introduction to NumPy
  15. Introduction to Pandas
  16. Read and clean data with Python pandas
  17. Scatterplot
  18. Covariance / list comprehension

Predict

Regression to predict continuous values

  1. Linear regression / vectors operations
  2. Gradient descent / matrix operations
  3. Linear Regression applied: Moneyball
  4. Multiple variable linear regression
  5. Inference in regression: confidence intervals and more
  6. Features selection
  7. Outliers
  8. Non linear regression
  9. Evaluate models

Regression to predict discrete values

  1. Linear Regression with qualitative predictors
  2. Logistic regression
  3. Example: TBD
  4. Model Pipeline

Regression to predict time series

  1. A basic example

Classify

  1. Text Processing intro / collections, set, lambda, regex, files
  2. Example: classify an article
  3. Example: sentiment analysis
  4. K-neighbours algorithm
  5. SVC. When classification speed is more critical than training speed.

Neural Networks

  1. Perceptron / generators
  2. Neural Network intro, the back-propagation
  3. Deep Learning
  4. Example: classify an image

Connect

Cluster and similarity

  1. Unsupervised learning
  2. Example: retrieve documents

Advanced time series

  1. Bayesian models
  2. Hidden Markov Models

Interact

Recommendation systems

  1. Example: movies / books / products

Reason

  1. Software agents

Extra: Scale ML

  1. Distributed Computing: Count Words using Apache Spark
  2. Azure examples

Python arguments

  1. … / iterables, generator functions
  2. … / slice
  3. …/ functions with any number of arguments / attaching metadata
  4.  … / anonymous or inline functions, lambda
  5. …/ decorators
  6. … / Modules, classes, objects, inheritance, composition
  7. … / functional programming , map and reduce, lambda, pipelines, closure
  8. …/ concurrency, threads
  9. … / profiling and timing