MGMT6560 Fall 19
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Schedule
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Session 1
Session 2
Session 3
Session 4
Session 5
Session 6
Session 7
Session 8
Session 9
Session 10
Session 11
Session 12
Session 13
Session 14
Session 15
Session 16
Session 17
Session 18
Session 19
Session 20
Session 21
Session 22
Session 23
Session 24
Session 25
Session 26
Session 27
Session 28
Session 29
Notebooks
Python Overview
Basic Data Structures
Numpy
Pandas
Conditional-Loops
Functions
Null Values
Groupby and Pivot Tables
Kaggle Baseline
Twitter
Web Mining
Visualizations - Seaborn
Strings - Regular Expressions
Feature Dummies
Matplotlib
The Simplest Neural Network with Numpy
Train Test Split
Introduction to Logistic Regression
K Nearest Neighbor
Assignment 5
Introduction to R
Local Files
Data Structures
Dataframes
Functions
Conditional-Loops
Aggregation and Merge
Tidyvere
Titanic
Regression - Matrix
Boston Housing
Ridge and Lasso
Stats Models
PCA
PCA Alt
Cluster Analysis
Feature Selection and Importance
Titanic Feature Creation
Corpus Simple
Scikit Learn Text
What's Cooking Python
Bag of Popcorn Bag of Words
Sentiment
Overview of NLP
FAST.ai NLP
Intoduction to MapReduce
Introduction to Spark
Introduction to Time Series
Rossman Store Sales
Neural Networks
Tensors
Pytorch IRIS
Covnet
Pytorch Mnist
Regression
Titanic FastAI
Ludwig
Evaluation
TF-Keras
TF-training
TF-data
TF-CNN
TF-RNN
TF-NLP
TF-Autoencoder and Gan
Assignments
Grading
Google Colab
Dropbox - Presentations/Files
Github - Class Content
Github - Assignments
Piazza (Section 2 Communications)
Slack (Section 1 Communications)
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Automl and Modeling Packages
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Session 24
Session 26
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