Datascience and Artificial-Neural-Networks practical ressources

Collection of very good notebooks from Harvard classes:

Description here: https://harvard-iacs.github.io/2019-CS109A/

Can be cloned from here: https://github.com/Harvard-IACS/2018-CS109A

in 2018-CS109A-master/content/labs

• CS109a: Introduction to Data Science
  • Lab 1: Introduction to Python and its Numerical Stack
  • Lab 2: Python for Data Collection and Cleaning
    • BeautifulSoup for Scraping
    • Pandas for Data Cleaning
  • Lab 3: Scikit-learn for Regression
  • Lab 4: Multiple and Polynomial Linear Regression
  • Lab 5: Regularization and Cross-Validation
  • Lab 6: Classification and Dimensionality Reduction
  • Lab 7: NumPy for Building Artificial Neural Network and Dealing with Missing Values
  • Lab 8: Discriminant Analysis - A tale of two cities
  • Lab 9: Decision Trees, Bagged Trees, Random Forests and Boosting
  • Lab 10: Keras for Artificial Neural Network
  • Lab 11: Italian Olives

Notebooks on SVM and CNNs (Keras)

• 0-svm_cnn: Support Verctor Machines and CNN using Keras
• 1-cnn_pretrained: VGG16 (imagenet)
• 2-t-sne: T-SNE Vizualization
• 3-Feature extraction: Classification based on extracted features