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Machine Learning, Deep Learning Practical Projects

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Machine learning scripts in Python Jupyter notebook


Project I-1 Boston_housing:

The project uses supervised learning algorithms to train models with Boston Housing dataset in order to make prediction of prices for clients who intend to purchase houses. Dataset: Boston Housing Prices and Features (e.g. numbers of rooms, student-to-teacher ratios, etc.) from Kaggle Project steps brief summary:

  • Load Dataset
  • Training and testing data splitting
  • Train model using DecisionTreeRegressor
  • Evaluate the model with GridSearchCV cross validation
  • Make prediction
  • Justify if more features should be included.

Project I-2 Find_donors:

Project steps:

  • Evaluate three classifiers (e.g. SVC, Statistical Gradient Descent classifier, essemble methods-adaboost, logistic regression, etc.) by training the models with training data and computing accuracy_score and f0.5_score for model performance on training data and testing data.
  • The best model is selected based on computing time, and model performance (accuracy_score, f_score - β=0.5).
  • After best model selected, GridSearchCV is used for hyperparameter tuning and model optimization.

Project brief summary:

  • Unsupervised learning problem (e.g. hierarchical clustering, Gaussian Mixture Models, complete- and average-link clustering, etc.)
  • Project steps:
  • Data Preprocessing: log transform, outlier detection (points falling out of 1.5 IQR and reasonably remove a few)
  • PCA: dimensionality reduction.
  • Clustering: K-Means v.s. GaussianMixture Model; use of Silhouette score as evaluation metrics.
  • Visualizaton, Prediction: pca.inverse_transform for centers of clusters, predict selected samples and compare with raw data.

Machine learning with Matlab/Octave

ML in Matlab/Octave


DL

Project III: Deep Learning Practice Projects

Project IV: Deep Learning Specialization


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