Python code for Udacity Introduction to Machine Learning course. Some code files were written by myself in order to achieve different results from the given tests.

Get started (check course link)

• Download enron mail dataset at: https://www.cs.cmu.edu/~./enron/
• Run: python tools/startup.py
• fabiano_tutoriais folder has some random files which helped me to understand most of the initial concepts

Links:

Class imbalance problem:

• http://www.chioka.in/class-imbalance-problem/

• http://www.marcoaltini.com/blog/dealing-with-imbalanced-data-undersampling-oversampling-and-proper-cross-validation

• https://gallery.cortanaintelligence.com/Experiment/Online-Fraud-Detection-working-with-unbalanced-class-data-1

• https://www.analyticsvidhya.com/blog/2016/03/practical-guide-deal-imbalanced-classification-problems/

• http://storm.cis.fordham.edu/~gweiss/papers/dmin07-weiss.pdf

• http://www.jair.org/media/953/live-953-2037-jair.pdf

• http://abricom.org.br/wp-content/uploads/2016/03/bricsccicbic2013_submission_20.pdf

• http://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/

Scikit contrib on imbalaced data

• https://github.com/scikit-learn-contrib/imbalanced-learn

• http://www.kdnuggets.com/2016/08/learning-from-imbalanced-classes.html

That said, here is a rough outline of useful approaches. These are listed approximately in order of effort:

• Do nothing. Sometimes you get lucky and nothing needs to be done. You can train on the so-called natural (or stratified) distribution and sometimes it works without need for modification.
• Balance the training set in some way:
  • Oversample the minority class.
  • Undersample the majority class.
  • Synthesize new minority classes.
• Throw away minority examples and switch to an anomaly detection framework.
• At the algorithm level, or after it:
  • Adjust the class weight (misclassification costs).
  • Adjust the decision threshold.
  • Modify an existing algorithm to be more sensitive to rare classes.
• Construct an entirely new algorithm to perform well on imbalanced data.

Ensemble method Machine Learning

• https://www.toptal.com/machine-learning/ensemble-methods-machine-learning

Basic Concepts in Machine Learning

• http://machinelearningmastery.com/basic-concepts-in-machine-learning/

Python begginer - Code Academy

• https://www.codecademy.com/courses/python-beginner-pt-BR

Introducing: Machine Learning in R

• https://www.datacamp.com/community/tutorials/machine-learning-in-r#gs.qyPOXoU

Your First Machine Learning Project in R Step-By-Step (tutorial and template for future projects)

• http://machinelearningmastery.com/machine-learning-in-r-step-by-step/

Python vs R for machine learning

• https://datascience.stackexchange.com/questions/326/python-vs-r-for-machine-learning

Pros and Cons of R vs Python Sci-kit learn

• https://www.kaggle.com/getting-started/5243

Should you teach Python or R for data science?

• http://www.dataschool.io/python-or-r-for-data-science/

Unofficial Windows Binaries for Python Extension Packages - Wheels

• http://www.lfd.uci.edu/~gohlke/pythonlibs/

Top 6 errors novice machine learning engineers make

• https://medium.com/towards-data-science/top-6-errors-novice-machine-learning-engineers-make-e82273d394db

Becoming a Machine Learning Engineer | Step 2: Pick a Process

• https://medium.com/towards-data-science/becoming-a-machine-learning-engineer-step-2-pick-a-process-942eef6ba8dd

Becoming a Machine Learning Engineer | Step 3: Pick Your Tool

• https://medium.com/towards-data-science/becoming-a-machine-learning-engineer-step-3-pick-your-tool-da1903a2958f

Parametric and Nonparametric Machine Learning Algorithms

• https://machinelearningmastery.com/parametric-and-nonparametric-machine-learning-algorithms/

ML Mind Map

• https://s3.amazonaws.com/MLMastery/MachineLearningAlgorithms.png?__s=tba8qgdaiyahops6sy3p

My Solution to the Galaxy Zoo Challenge

• http://benanne.github.io/2014/04/05/galaxy-zoo.html

Object Recognition with Convolutional Neural Networks in the Keras Deep Learning Library

• https://machinelearningmastery.com/object-recognition-convolutional-neural-networks-keras-deep-learning-library/

TODO TensorFlow Demo: MNIST for ML Beginners

• https://github.com/IanLewis/tensorflow-examples/blob/master/notebooks/TensorFlow%20MNIST%20tutorial.ipynb

TODO Your First Machine Learning Project in Python Step-By-Step

• https://machinelearningmastery.com/machine-learning-in-python-step-by-step/

Undertand Bayes Theorem (Posterior, Likelihood, Prior and Evidence)

• http://www.lichun.cc/blog/2013/07/understand-bayes-theorem-prior-likelihood-posterior-evidence/

Awesome Machine Learning

• https://github.com/josephmisiti/awesome-machine-learning/blob/master/books.md

How do I learn Machine Learning?

• https://www.quora.com/How-do-I-learn-machine-learning-1

Embrace Randomness in Machine Learning

• https://machinelearningmastery.com/randomness-in-machine-learning/

Redes Neurais Artificiais

• http://conteudo.icmc.usp.br/pessoas/andre/research/neural/

https://pt.stackoverflow.com/questions/192098/como-funciona-uma-rede-neural-artificial https://pt.stackoverflow.com/questions/61187/como-implementar-a-camada-oculta-em-uma-rede-neural-de-reconhecimento-de-caracte https://pt.stackoverflow.com/questions/40135/explicar-o-algoritmo-svr/40149#40149 https://stackoverflow.com/questions/2480650/role-of-bias-in-neural-networks

Análise de Componentes Principais

• http://prorum.com/index.php/1557/estatistica-conhecido-componentes-principais-principal

http://iamtrask.github.io/2015/07/12/basic-python-network/

Deep Learning Book

• http://www.deeplearningbook.org/

Keras Cheat Sheet: Neural Networks in Python

• https://www.datacamp.com/community/blog/keras-cheat-sheet
• https://www.datacamp.com/community/tutorials/deep-learning-python
• https://www.datacamp.com/community/tutorials/keras-r-deep-learning

How to Setup a Python Environment for Machine Learning and Deep Learning with Anaconda

• https://machinelearningmastery.com/setup-python-environment-machine-learning-deep-learning-anaconda/

Building a Student Intervention System

• http://www.ritchieng.com/machine-learning-project-student-intervention/

Machine Learning Algorithms: Which One to Choose for Your Problem

• https://blog.statsbot.co/machine-learning-algorithms-183cc73197c

If you have already closed the Anaconda navigator, open cmd and type jupyter-notebook list.

Then you can kill the port using following commands: netstat -o -n -a | findstr :3000 TCP 0.0.0.0:3000 0.0.0.0:0 LISTENING 3116 taskkill /F /PID 3116

• https://stackoverflow.com/questions/46319617/how-to-stop-jupyter-server-using-anaconda

Comparison of 14 different families of classification algorithms on 115 binary datasets

• https://arxiv.org/pdf/1606.00930.pdf

Machine Learning Algorithms for Classification

• https://www.cs.princeton.edu/~schapire/talks/picasso-minicourse.pdf

In what real world applications is Naive Bayes classifier used?

• https://www.quora.com/In-what-real-world-applications-is-Naive-Bayes-classifier-used

Support Vector Machines and Kernel Methods

• https://www.cs.cmu.edu/~ggordon/SVMs/new-svms-and-kernels.pdf