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 as the programming languages. 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. It further discusses neural network architectures for predicting sequences in form of recurrent neural networks and long short term memory. The final chapter contains a detailed end-to-end solution with neural networks in Pytorch.After completing , you will be able to implement machine learning and neural network solutions and extend them to your advantage.  Data scientists, machine learning engineers, and software professionals with basic skills in Python programming. weiterlesen