ms-machine-learning

Repository for Microsoft Machine Learning (Microsoft Learn)

Sections

1. exploratory-data-analysis
2. regression
3. classification
4. clustering
5. deep-learning

Notebooks

1. exploratory-data-analysis

   • 0c3_Titanic_Dataset_Find_and_Visualize_Missing_Data.ipynb
   • 0c5_Titanic_Dataset_Visualizing_Different_Types_of_Data.ipynb
   • 0c7_Titanic_Dataset_One_Hot_Vectors.ipynb
   • 011_Data_Exploration_NumPy_and_Pandas.ipynb
   • 012_Data_Exploration_Visualize_Data.ipynb
   • 013_Data_Exploration_Real_World_Data.ipynb
   • 01_Flights_Challenge.ipynb

2. regression

   • 0a3_Training_and_Running_Your_First_Model.ipynb
   • 0a5_Datasets_in_Python.ipynb
   • 0a7_Using_a_Trained_Model_on_New_Data.ipynb
   • 0b3_Supervised_Learning.ipynb
   • 0b5_Supervised_Learning_using_Different_Cost_Functions.ipynb
   • 0b7_Gradient_Descent.ipynb
   • 1a3_Train_a_Simple_Linear_Regression_Model.ipynb
   • 1a5_Train_a_Multiple_Linear_Regression_Model.ipynb
   • 1a7_Fitting_a_Polynomial_Curve.ipynb
   • 021_Regression_Training.ipynb
   • 022_Regression_Experimenting.ipynb
   • 023_Regression_Tuning.ipynb
   • 02_Real_Estate_Regression_Challenge.ipynb

3. classification

   • 2a3_Build_a_Simple_Logistic_Regression_Model.ipynb
   • 2a5_Assessing_a_Logistic_Regression_Model.ipynb
   • 2a7_Improving_a_Logistic_Regression_Model.ipynb
   • 031_Classification_Binary.ipynb
   • 032_Classification_Metrics.ipynb
   • 033_Classification_Multiclass.ipynb

4. clustering

   • 041_Clustering_Introduction.ipynb
   • 042_Clustering_Kmeans_and_Hierarchical.ipynb
   • 04_Clustering_Challenge.ipynb

5. deep-learning

   • 05aDeep_Neural_Networks(TensorFlow).ipynb
   • 05aDeep_Neural_Networks(PyTorch).ipynb
   • 05bConvolutional_Neural_Networks(TensorFlow).ipynb
   • 05bConvolutional_Neural_Networks(PyTorch).ipynb
   • 05cTransfer_Learning(TensorFlow).ipynb
   • 05cTransfer_Learning(PyTorch).ipynb
   • 05Safari_CNN_Challenge(TensorFlow).ipynb
   • 05Safari_CNN_Challenge(PyTorch).ipynb