Book Image

Python Machine Learning by Example, - Third Edition

By : Yuxi (Hayden) Liu
Book Image

Python Machine Learning by Example, - Third Edition

By: Yuxi (Hayden) Liu

Overview of this book

Python Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML). With six new chapters, on topics including movie recommendation engine development with Naïve Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements. At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries. Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP. By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems.
Table of Contents (17 chapters)
15
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16
Index

Implementing logistic regression using TensorFlow

We herein use 90% of the first 300,000 samples for training, the remaining 10% for testing, and assume that X_train_encY_trainX_test_enc, and Y_test contain the correct data:

  1. First, we import TensorFlow, transform X_train_enc and X_test_enc into a numpy array, and cast X_train_encY_trainX_test_enc, and Y_test to float32:
    >>> import tensorflow as tf
    >>> X_train_enc = X_train_enc.toarray().astype('float32')
    >>> X_test_enc = X_test_enc.toarray().astype('float32')
    >>> Y_train = Y_train.astype('float32')
    >>> Y_test = Y_test.astype('float32')
    
  2. We use the tf.data API to shuffle and batch data:
    >>> batch_size = 1000
    >>> train_data = tf.data.Dataset.from_tensor_slices((X_train_enc, Y_train))
    >>> train_data = train_data.repeat().shuffle(5000).batch...