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My journey into understanding Machine Learning through coding with Python, Scikit learn, Numpy, Pandas, Matplotlib, Keras.

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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
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
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
6. Boston Housing Regression using Keras
TensorFlow & Keras: layers, Sequential, model.compile, summary,
plot_model, model.fit, history, EarlyStopping,
model.evaluate, TensorBoard
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
4. Motion Capture Hand Postures (Ensemble of classifiers)
NumPy: nan
matplotlib: scatter
SkLearn: SimpleImputer, RidgeClassifier, VotingClassifier, ExtraTreesClassifier
3. Arcene Cancer Binary Classification using SVM
NumPy: ravel
Pandas: concat
DataFrame: T, iloc
SkLearn: LinearSVC, SVC, RandomForestRegressor
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
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_
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

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My journey into understanding Machine Learning through coding with Python, Scikit learn, Numpy, Pandas, Matplotlib, Keras.

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