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Python Machine Learning
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Table of Contents

Table of Contents

  1. Giving Computers the Ability to Learn from Data
  2. Training Simple ML Algorithms for Classification
  3. ML Classifiers Using scikit-learn
  4. Building Good Training Datasets - Data Preprocessing
  5. Compressing Data via Dimensionality Reduction
  6. Best Practices for Model Evaluation and Hyperparameter Tuning
  7. Combining Different Models for Ensemble Learning
  8. Applying ML to Sentiment Analysis
  9. Embedding a ML Model into a Web Application
  10. Predicting Continuous Target Variables with Regression Analysis
  11. Working with Unlabeled Data - Clustering Analysis
  12. Implementing Multilayer Artificial Neural Networks
  13. Parallelizing Neural Network Training with TensorFlow
  14. TensorFlow Mechanics
  15. Classifying Images with Deep Convolutional Neural Networks
  16. Modeling Sequential Data Using Recurrent Neural Networks
  17. GANs for Synthesizing New Data
  18. RL for Decision Making in Complex Environments

About the Author

Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. Some of his recent research methods have been applied to solving problems in the field of biometrics for imparting privacy to face images. Other research focus areas include the development of methods related to model evaluation in machine learning, deep learning for ordinal targets, and applications of machine learning to computational biology. Vahid Mirjalili obtained his Ph.D. in mechanical engineering working on novel methods for large-scale, computational simulations of molecular structures. Currently, he is focusing his research efforts on applications of machine learning in various computer vision projects at the Department of Computer Science and Engineering at Michigan State University. He recently joined 3M Company as a research scientist, where he uses his expertise and applies state-of-the-art machine learning and deep learning techniques to solve real-world problems in various applications to make life better.

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