Hands-on Machine Learning with Python
Implement Neural Network Solutions with Scikit-learn and PyTorch
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Beschreibung
Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning. It introduces tools such as NumPy for numerical processing, Pandas for panel data analysis, Matplotlib for visualization, Scikit-learn for machine learning, and Pytorch for deep learning with Python. It also serves as a long-term reference manual for the practitioners who will find solutions to commonly occurring scenarios.
The book is divided into three sections. The first section introduces you to number crunching and data analysis tools using Python with in-depth explanation on environment configuration, data loading, numerical processing, data analysis, and visualizations. The second section covers machine learning basics and Scikit-learn library. It also explains supervised learning, unsupervised learning, implementation, and classification of regression algorithms, and ensemble learning methods in an easy manner with theoreticaland practical lessons. The third section explains complex neural network architectures with details on internal working and implementation of convolutional neural networks. The final chapter contains a detailed end-to-end solution with neural networks in Pytorch.
After completing Hands-on Machine Learning with Python, you will be able to implement machine learning and neural network solutions and extend them to your advantage.
You will:
- Review data structures in NumPy and Pandas
- Demonstrate machine learning techniques and algorithm
- Understand supervised learning and unsupervised learning
- Examine convolutional neural networks and Recurrent neural networks
- Get acquainted with scikit-learn and PyTorch
- Predict sequences in recurrent neural networks and long short term memory
Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning. It introduces tools such as NumPy for numerical processing, Pandas for panel data analysis, Matplotlib for visualization, Scikit-learn for machine learning, and Pytorch for deep learning with Python. It also serves as a long-term reference manual for the practitioners who will find solutions to commonly occurring scenarios.
The book is divided into three sections. The first section introduces you to number crunching and data analysis tools using Python with in-depth explanation on environment configuration, data loading, numerical processing, data analysis, and visualizations. The second section covers machine learning basics and Scikit-learn library. It also explains supervised learning, unsupervised learning, implementation, and classification of regression algorithms, and ensemble learning methods in an easy manner with theoreticaland practical lessons. The third section explains complex neural network architectures with details on internal working and implementation of convolutional neural networks. The final chapter contains a detailed end-to-end solution with neural networks in Pytorch.
After completing Hands-on Machine Learning with Python, you will be able to implement machine learning and neural network solutions and extend them to your advantage.
What You'll Learn
- Review data structures in NumPy and Pandas
- Demonstrate machine learning techniques and algorithm
- Understand supervised learning and unsupervised learning
- Examine convolutional neural networks and Recurrent neural networks
- Get acquainted with scikit-learn and PyTorch
- Predict sequences in recurrent neural networks and long short term memory
Who This Book Is For
Data scientists, machine learning engineers, and software professionals with basic skills in Python programming.
Explains machine learning process through validation, evaluation, hyperparameter tuning and regularization Discusses neural network architectures for predicting sequences in the form of Recurrent Neural Networks Covers projects with an end-to-end solution with neural networks using Pytorch
Autor*in
Ashwin Pajankar
Themen in »Hands-on Machine Learning with Python«
Data Science Numpy Pandas Matplotlib CNN RNN LSTM Keras TensorFlow PyTorch