Machine Learning Understanding
I am using the excellent book of Aurélian Géron "Hands-On Machine Learning with Scikit-Learn and TensorFlow", Scikit learn, TensorFlow and Keras documentation & code examples to discover both the ML theory and its techniques. Stack Overflow and Google are always helpful.
Weisstein, Eric W. "Chair Surface." From MathWorld--A Wolfram Web Resource. https://mathworld.wolfram.com/ChairSurface.html
| 9. Credit Card Fraud Detection (Imbalanced Classification)
General: warnings.filterwarnings NumPy: bincount, random.choice, concatenate SkLearn: class_weight |
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| 8. Twitter Airline Sentiment (Text Classification) with GloVe word embeddings General: io.open NumPy: array, fromstring, zeros, argmax matplotlib: filter TensorFlow & Keras: TextVectorization, get_vocabulary, Embedding, sparse_categorical_crossentropy |
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| 7. CIFAR-10 Image Classification using Keras
General: pickle, decode NumPy: reshape, transpose DataFrame: astype matplotlib: imshow TensorFlow & Keras: to_categorical, Conv2D, BatchNormalization, GlobalAveragePooling2D, callbacks.ModelCheckpoint |
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| 6. Boston Housing Regression using Keras
TensorFlow & Keras: layers, Sequential, model.compile, summary, plot_model, model.fit, history, EarlyStopping, model.evaluate, TensorBoard |
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| 5. Avila Dimensionality Reduction
Pandas: to_numeric DataFrame: replace matplotlib: axes3d, view_init, get_cmap, add_subplot SkLearn: PCA, explained_variance_ratio_, LocallyLinearEmbedding, TSNE, make_swiss_roll, MDS, DBSCAN, KMeans |
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| 4. Motion Capture Hand Postures (Ensemble of classifiers)
NumPy: nan matplotlib: scatter SkLearn: SimpleImputer, RidgeClassifier, VotingClassifier, ExtraTreesClassifier |
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| 3. Arcene Cancer Binary Classification using SVM
NumPy: ravel Pandas: concat DataFrame: T, iloc SkLearn: LinearSVC, SVC, RandomForestRegressor |
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| 2. Epileptic Seizure Binary & Multi-class Classification
NumPy: shape, random.permutation, fill_diagonal DataFrame: rename, copy matplotlib: pyplot, figure, legend, matshow SkLearn: SGDClassifier, confusion_matrix, precision_score, recall_score, f1_score, precision_recall_curve, roc_curve, RandomForestClassifier |
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| 1. Bike Sharing Regression
Pandas: read_csv DataFrame: head, describe, hist, plot, drop, corr SkLearn: train_test_split, OneHotEncoder, Pipeline, StandardScaler, LinearRegression, mean_squared_error, DecisionTreeRegressor, fit, predict, cross_val_score, GridSearchCV, RandomizedSearchCV, feature_importances_ |
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| My development environment | |
|---|---|
| Name | Version |
| Python | 3.7.7 |
| scikit-learn | 0.22.1 |
| IPython | 7.15.0 |
| Jupyter notebook | 6.0.3 |
| TensorFlow | 2.1 |