Machine Learning Algorithms From Scratch with Python
You must understand algorithms to get good at machine learning. The problem is that they are only ever explained using Math. No longer. In this Ebook, finally cut through the math and learn exactly how machine learning algorithms work. Using clear explanations, simple pure Python code (no libraries!) and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement a suite of linear, nonlinear and ensemble machine learning algorithms from scratch.
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II Linear Algorithms
III Nonlinear Algorithms
IV Ensemble Algorithms
accuracy activation actual apply attribute Backpropagation bagging baseline Calculate called class values classification codebook vectors coefficients column complete Contrived Dataset correct create cross-validation CSV file csv_reader decision tree discover distance epoch error estimate Euclidean distance evaluate Example Example Output example prints expected Extensions filename fold follows function named given implement input integer involves l_rate layer learning rate load load_csv(filename logistic regression machine learning algorithms max_depth mean method min_size n_epoch n_folds neighbors neuron node normalize output output value Perceptron performance predictions prepare probability problem Python random random import records RMSE root row in dataset row[column row[i Running the example sample scores scratch seed separated similar specific split split point stacked standard deviation statistics stdevs step stochastic gradient descent sum_error summaries technique terminal training data training dataset tutorial update variable variance weights yhat zero