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1.solution_what_is_regression.ipynb
1.solution_what_is_regression.ipynb
 
 
4.1.10-clustering-challenge.ipynb
4.1.10-clustering-challenge.ipynb
 
 
4.1.2-kmeans.ipynb
4.1.2-kmeans.ipynb
 
 
4.1.3-evaluating-kmeans.ipynb
4.1.3-evaluating-kmeans.ipynb
 
 
4.1.4-hierarchiacal-clustering.ipynb
4.1.4-hierarchiacal-clustering.ipynb
 
 
4.1.5-dbscan.ipynb
4.1.5-dbscan.ipynb
 
 
4.1.6-clustering-gasussian-mixture-models.ipynb
4.1.6-clustering-gasussian-mixture-models.ipynb
 
 
4.1.7-4.1.8-tSNE-pca.ipynb
4.1.7-4.1.8-tSNE-pca.ipynb
 
 
4.1.9-umap.ipynb
4.1.9-umap.ipynb
 
 
5.2.6-time-series-analysis.ipynb
5.2.6-time-series-analysis.ipynb
 
 
5.3.4-activation-loss
5.3.4-activation-loss
 
 
Capstone-5.4.4.pptx
Capstone-5.4.4.pptx
 
 
Untitled0.ipynb
Untitled0.ipynb
 
 
Untitled1.ipynb
Untitled1.ipynb
 
 
base
base
 
 
capston4-1-data-analysis.ipynb
capston4-1-data-analysis.ipynb
 
 
capstone2-supervised-learning
capstone2-supervised-learning
 
 
capstone2-supervisedlearning.pptx
capstone2-supervisedlearning.pptx
 
 
capstone3-unsupervised-learning.ipynb
capstone3-unsupervised-learning.ipynb
 
 
capstone3-unsupervised-learning.pptx
capstone3-unsupervised-learning.pptx
 
 
capstone4-proposal.docx
capstone4-proposal.docx
 
 
data cleaning II asmt
data cleaning II asmt
 
 
dc3-outliers-us-education
dc3-outliers-us-education
 
 
de2
de2
 
 
deep racer log analysis
deep racer log analysis
 
 
eda-model-prep-PID
eda-model-prep-PID
 
 
exploration1-assignment
exploration1-assignment
 
 
fe1-assignment
fe1-assignment
 
 
fe2-assessment
fe2-assessment
 
 
fe2-assignment
fe2-assignment
 
 
l2-knn-challenge
l2-knn-challenge
 
 
l2-linear-regression-with-ols
l2-linear-regression-with-ols
 
 
l3-gradient-boosting
l3-gradient-boosting
 
 
l3-lr-houseprices
l3-lr-houseprices
 
 
l3-lr-temperature
l3-lr-temperature
 
 
l4-analyzing-model-perf
l4-analyzing-model-perf
 
 
l4-interpret-coefficients
l4-interpret-coefficients
 
 
l5-model-comparison-knn-ols
l5-model-comparison-knn-ols
 
 
l5-multiclass-classifiers
l5-multiclass-classifiers
 
 
l5-py-performance-houseprices
l5-py-performance-houseprices
 
 
l5-py-performance-weather
l5-py-performance-weather
 
 
l6-predictions-houseprices
l6-predictions-houseprices
 
 
l7-decision-tree-random-forest
l7-decision-tree-random-forest
 
 
l7-overfitting-and-regularization
l7-overfitting-and-regularization
 
 
l8-solving-regression-problems
l8-solving-regression-problems
 
 
linear-classifiers-assessment
linear-classifiers-assessment
 
 
model-performance-analysis
model-performance-analysis
 
 
readme.md
readme.md
 
 

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Thinkful/Chegg Data Science (DS-401)

Various lesson and project material, migrated from Google Drive/Colab.

Capstone

  • Proposal: capstone4-proposal
  • Presentation: Capstone-5.4.4
  • Notebook: Github

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Thinkful/Chegg Data Science (DS-401) lesson and project material: regression, clustering, time series, capstone.

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education data-science machine-learning jupyter

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