This repo contains a collection of Jupyter Notebooks to accompany the Udacity Connect Intensive Machine Learning Nanodegree. The code is written for Python 2.7, but should be (mostly) compatible with Python 3.

• Week 1 (wk1/)

  • PythonPractice_1.ipynb: Introduction to Jupyter Notebook and basic Python programming (including data types, if and while loops, list comprehension, lambda expression, etc.).
  • PythonPractice_2.ipynb: Introduction to Numpy, including how to create Numpy Array, built-in methods in Numpy array, array indexing/selection/slicing, broadcasting.
  • PythonPractice_3.ipynb: Introduction to Pandas. Topics include inputting data into DataFrame and getting summary information, selection and indexing, conditional selection with DataFrame, etc.
  • PythonPractice_4.ipynb: Introduction to data visualization with Matplotlib and Seaborn.
  • data/: containing one sample dataset for the notebooks and one for exercise.

• Week 2 (wk2/)

  • SklearnTutorial.ipynb: Introduction to scikit-learn (sklearn) and a step-by-step guide of building a machine learning model with sklearn with the Titanic Survival dataset from Kaggle
  • SklearnTutorial-solution.ipynb: The solution to the SklearnTutorial.ipynb notebook.

• Week 3 (wk3/)

  • RegressionModels.ipynb: Introduction to the implementation and evaluatoin of regression models to predict housing price with sklearn.
  • RegressionModels-solution.ipynb: The solution to the RegressionModels.ipynb

• Week 4 (wk4/)

  • NeuralNets_Miniproject.ipynb: Introduction to the fundamentals of neural networks, the implementation of single layer and multi layer perceptrons, and perceptron with scikit-learn.
  • NeuralNets_Miniproject-solution.ipynb: The solution to the NeuralNets_Miniproject.ipynb notebook.

• Week 5 (wk5/)

  • BayesNLP_Miniproject.ipynb: Introduction to Bayes theorem and application of Bayes theorem in natual language processing. Write Python methods to calculate maximum likelihood of a word based on the preceding word, and build a Bayes classifier that computes with a context the optimal label for a second missing word based on the possible words that could be in the first blank.
  • BayesNLP_Miniproject-solution.ipynb: The solution to the BayesNLP_Miniproject.ipynb notebook.
  • Quiz.pdf: some quiz on supervised learning.

• Week 6 (wk6/)

  • Clustering.ipynb: Perform K-Means clustering on the Enron dataset. Visualize different clusters that form before and after feature scaling. Plot decision boundaries that arise from K-Means clustering using two of the features.
  • Clustering-solution.ipynb: The solution to the Clustering.ipynb notebook.

• Week 7 (wk7/)

  • PCA.ipynb: Perform Principal Component Analysis (PCA) on a large set of features to explain as much of the variance as possible in the data using a smaller set of features. Visualize the eigenfaces (orthonormal basis of components) that result from PCA. The dataset omes from "Labeled Faces in the Wild" (LFW), a database of more than 13,000 face photographs designed for studying the problem of unconstrained face recognition.
  • PCA-solution.ipynb: The solution to the PCA.ipynb notebook.

• Week 8 (wk8/)

  • FeatureSelection.ipynb: Introduction to Chi-square test statistics and Pearson's Chi-square test. Learn how to perform univariate feature selection using the 'SelectKBest' class from scikit-learn. Learn how to do recursive feature elimination using the RFE class and RFE with cross-validation (RFECV) from scikit-learn.
  • FeatureSelection-solution.ipynb: The solution to the FeatureSelection.ipynb notebook.

• Week 9 (wk9/)

  • Kaggle.ipynb: Introduction to the process of creating machine learning solutions to a real world problem. Practice kills such as data analysis and visualization, model building, evaluation and optimization on a real world dataset.

• Week 10 (wk10/)

  • MNIST_Demo.ipynb: Introduction to TensorFlow and Keras. Learn how to build neural networks using TensorFlow and Keras to solve a multiclass classification problem. Compare TensorFlow and Keras.

• Week 11 (wk11/)

  • CNN_Tutorial.ipynb: Build convolutional neural networks with Keras to classify images from the CIFAR-10 dataset.