Hands-on Machine Learning with Python: Implement Neural Network Solutions with Scikit-learn and PyTorch

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 
  

Data scientists, machine learning engineers, and software professionals with basic skills in Python programming.

Aditya Joshi has worked in data science and machine learning engineering roles since the completion of his MS (By Research) from IIIT Hyderabad. He has conducted tutorials, workshops, invited lectures, and full courses for students and professionals who want to move tothe field of data science. His past academic research publications include works on natural language processing, specifically fine grain sentiment analysis and code mixed text. He has been the organizing committee member and program committee member of academic conferences on data science and natural language processing.