# What this book covers

*Chapter 1*, *Introducing Machine Learning for Genomics*, provides a brief history of the field of genomics and the practical application of machine learning methods to genomics, in addition to some of the technologies that this book will use.

*Chapter 2*, *Genomics Data Analysis*, gives readers a quick primer on data analysis in genomics. Using the Python programming language, readers will be able to make sense of the vast amounts of genomics data available and extract biological insights.

*Chapter 3*, *Machine Learning Methods for Genomic Applications*, introduces the reader to the two most important machine learning methods (supervised and unsupervised) and some of the important elements of standard machine learning pipelines. It also includes the practical real-world applications of supervised and unsupervised algorithms for genomics data analysis in the life sciences and biotechnology industries.

*Chapter 4*, *Deep Learning for Genomics*, will teach the reader about the fundamental concepts of deep learning, different types of deep learning models, and different deep learning Python libraries.

*Chapter 5*, *Introducing Convolutional Neural Networks for Genomics*, gives the reader a taste of **Convolutional Neural Networks** (**CNNs**), a type of deep neural network that is primarily used for sequence data, and shows how CNNs have superior performance compared to other deep learning methods.

*Chapter 6*, *Recurrent Neural Networks in Genomics*, introduces reinforcement learning techniques such as **Recurrent Neural Networks** (**RNNs**) and LSTMs and shows how they are currently being applied in several applications.

*Chapter 7*, *Unsupervised Deep Learning with Autoencoders*, introduces unsupervised deep learning, different methods of unsupervised deep learning, specifically Autoencoders, and its application in genomics.

*Chapter 8*, *GANs for Improving Models in Genomics*, introduces **Generative Adversarial Networks** (**GANs**) and how they can be used to improve deep neural networks trained on genomics datasets for predictive modeling.

*Chapter 9*, *Building and Tuning Deep Learning Models*, describes how to build and tune machine learning and deep learning models and deploy the final models across various computational systems and several platforms.

*Chapter 10*, *Model Interpretability in Genomics*, introduces the reader to how to interpret machine learning and deep learning models. The model interpretability introduced here helps readers to understand a model’s decision and why businesses are interested in model interpretability for creating trust, gaining profitability, and so on.

*Chapter 11*, *Model Deployment and Monitoring*, teaches the reader how to take the model they built on Google Colab and deploy it for predictions using open source tools such as Streamlit and Hugging Face. In addition, this chapter also describes how to monitor models using advanced tools and how monitoring is a key metric for businesses.

*Chapter 12*, *Challenges, Pitfalls, and Best Practices for Deep Learning in Genomics*, informs the reader of the challenges and pitfalls associated with applying machine learning and deep learning methodologies to genomics applications. It also covers the best practices for building end-to-end machine learning and deep learning models and applying them to genomic datasets.