Data science and machine learning in science and engineering
Data science and machine learning in engineering and science
Lectures
Syllabus for 06-642 Data Science and Machine Learning in Chemical Engineering
Data science and machine learning in engineering and science
Introduction to data files
Introduction to Pandas
Intermediate Pandas
Using Pandas DataFrames as a small database
Introduction to data-driven model development
Intermediate scikit-learn
Nonlinear models in sklearn
Decision Trees and Random Forests Examples
Building custom estimators in sklearn
Summary
Index
Assignments
Linear regression
Project
Project check-in
Project presentation
Project proposal
Reading molecular data
Reading data
Analyzing Zoom meeting data
About the book
Build statistics
repository
open issue
Index
Index
C
|
D
|
G
|
K
|
L
|
N
|
P
|
R
|
S
|
T
C
cross-validation
custom estimator
D
Decision tree
G
Gaussian process
K
k-fold
L
LASSO
LinearRegression
lstsq
N
neural network
P
pandas
pipeline
polyfit
PolynomialFeatures
polyval
preprocessing
preprocessing - scale
R
Random forest
regularization
S
scikit-learn
T
test split
train
