Python Machine Learning: Learn how to build powerful Python machine learning algorithms to generate useful data insights with this data analysis tutorialUnlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics Key Features
If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. |
Contents
| 1 | |
| 17 | |
A Tour of Machine Learning Classifiers Using ScikitLearn | 49 |
Building Good Training Sets Data Preprocessing | 99 |
Compressing Data via Dimensionality Reduction | 127 |
Learning Best Practices for Model Evaluation and Hyperparameter Tuning | 169 |
Combining Different Models for Ensemble Learning | 199 |
Applying Machine Learning to Sentiment Analysis | 233 |
Embedding a Machine Learning Model into a Web Application | 251 |
Predicting Continuous Target Variables with Regression Analysis | 277 |
Working with Unlabeled Data Clustering Analysis | 311 |
Training Artificial Neural Networks for Image Recognition | 341 |
Parallelizing Neural Network Training with Theano | 387 |
| 417 | |
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Common terms and phrases
accuracy activation function AdaBoost Adaline Algorithms for Classification application array artificial neural networks backpropagation calculate Chapter Classifiers Using Scikit-learn clustering coefficient compute cost function cross-validation decision boundary decision tree dimensionality reduction discussed document eigenvalues eigenvectors ensemble epoch error evaluate feature selection following code following figure hidden layer hyperparameters import numpy input k-means kernel PCA learning rate linear regression logistic regression machine learning algorithms Machine Learning Classifiers machine learning models marker='o method movie review nonlinear number of features NumPy numpy as np optimization overfitting parameter perceptron performance plot plt.show preceding code Preprocessing principal component problem Python random forest random_state=0 regression model simple SQLite standardized StandardScaler step stochastic gradient descent subset supervised learning take a look techniques test dataset test set tf-idf Theano Tour of Machine training data training dataset Training Machine Learning training samples training set transform values variable variance y_test y_train