Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical And Machine Learning Forecasting Methods

Python for LSTMs, ARIMA, Deep Learning, AI, Support Vector Regression, +More Applied to Time Series Forecasting

Development of high-throughput technologies in molecular biology during the last two decades has contributed to the production of tremendous amounts of data. Microarray and RNA sequencing are two such widely used high-throughput technologies for simultaneously monitoring the expression patterns of thousands of genes. Data produced from such experiments are voluminous (both in dimensionality and numbers of instances) and evolving in nature. Analysis of huge amounts of data toward the identification of interesting patterns that are relevant for a given biological question requires high-performance computational infrastructure as well as efficient machine learning algorithms. Cross-communication of ideas between biologists and computer scientists remains a big challenge.

Perform Machine Learning and Advanced Statistical Analysis On Your Own - Even If You Don't Code! 100% in Excel

Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content.