Skip to content

iamved/Machine-Learning-with-Python

BranchesTags

Folders and files

NameName
Last commit message
Last commit date

Latest commit

History

38 Commits
Coursera-Machine-Learning-Foundations
Coursera-Machine-Learning-Foundations
Coursera-Machine-Learning-Regression
Coursera-Machine-Learning-Regression
Homesite
Homesite
Prudential
Prudential
.gitattributes
.gitattributes
.gitignore
.gitignore
DM-ST3-1.ipynb
DM-ST3-1.ipynb
DM-ST3-2.ipynb
DM-ST3-2.ipynb
DM-ST3-3.ipynb
DM-ST3-3.ipynb
ML-CSCE-685-Clustering-Evaluation.ipynb
ML-CSCE-685-Clustering-Evaluation.ipynb
ML-CSCE-685-Decision-Trees.ipynb
ML-CSCE-685-Decision-Trees.ipynb
ML-CSCE-685-GaussianNB-Pima-Indians-Diabetes.ipynb
ML-CSCE-685-GaussianNB-Pima-Indians-Diabetes.ipynb
ML-CSCE-685-Introduction.ipynb
ML-CSCE-685-Introduction.ipynb
ML-CSCE-685-K-Nearest-Neighbors.ipynb
ML-CSCE-685-K-Nearest-Neighbors.ipynb
ML-CSCE-685-KMeans-Agglomerative-DBSCAN.ipynb
ML-CSCE-685-KMeans-Agglomerative-DBSCAN.ipynb
ML-CSCE-685-Linear-Regression-Regularization.ipynb
ML-CSCE-685-Linear-Regression-Regularization.ipynb
ML-CSCE-685-Linear-Regression.ipynb
ML-CSCE-685-Linear-Regression.ipynb
ML-CSCE-685-Logistic-Regression.ipynb
ML-CSCE-685-Logistic-Regression.ipynb
ML-CSCE-685-MNIST-Evaluation-of-Classifiers.ipynb
ML-CSCE-685-MNIST-Evaluation-of-Classifiers.ipynb
ML-CSCE-685-NaiveBayes-Spam-Classification.ipynb
ML-CSCE-685-NaiveBayes-Spam-Classification.ipynb
ML-CSCE-685-Neural-Networks-With-Keras.ipynb
ML-CSCE-685-Neural-Networks-With-Keras.ipynb
ML-CSCE-685-Performance-Comparison-of-Ensemble-Learning-On-Different-UCI-ML-Datasets.ipynb
ML-CSCE-685-Performance-Comparison-of-Ensemble-Learning-On-Different-UCI-ML-Datasets.ipynb
ML-CSCE-685-Understanding-Validation-And-Learning-Curves.ipynb
ML-CSCE-685-Understanding-Validation-And-Learning-Curves.ipynb
README.md
README.md
UMICH SI650-Sentiment Classification.ipynb
UMICH SI650-Sentiment Classification.ipynb
UMICH-SI650-Sentiment-Classification.ipynb
UMICH-SI650-Sentiment-Classification.ipynb
UMICH_SI650_test_data.txt
UMICH_SI650_test_data.txt
UMICH_SI650_train_data.txt
UMICH_SI650_train_data.txt
blend.py
blend.py

Repository files navigation

Machine-Learning-with-Python

Repository of my Machine Learning Implementations in Python. Collated learning from Coursera, Dato and other Python Tutorials.

Machine Learning Foundations: A Case Study Approach - is part I of the Machine Learning Specialization course in Coursera. It provides an introductory overview of Regression, Classification, Clustering and Similarity, Recommending Products and Deep Learning. Primarily it uses GraphLab Create ML framework to analyse use cases.

About

Machine Learning Implementations in Python

Resources

Readme
Activity

Stars

0 stars

Watchers

1 watching

Forks

0 forks
Report repository

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook100.0%