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 theoretical and 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.
