1st Edition
Fundamentals of Machine Learning for Life Sciences A Simple, Step-by-Step Approach with Examples in R
1 What is machine learning? 2 Basic setup 3 Machine learning in practice 4 Linear regression 5 Polynomial regression 6 Logistic regression 7 K-Nearest Neighbors 8 Support Vector Machines 9 Decision trees and forests 10 Neural networks and deep learning 11 What to do next? 12 References 13 Index
Biography
Dr. Ankur Awadhiya (b. 1987) is an officer of the Indian Forest Service and an en-gineer by training. He received his B.Tech and Ph.D. from IIT Kanpur, earned his AIGNFA (Master’s in Forestry) from the Indira Gandhi National Forest Academy, and completed an Honours Post Graduate Diploma in Advanced Wildlife Management from the Wildlife Institute of India.
He has pursued advanced specializations in Artificial Intelligence from Oxford, Mind and Decision Making from Cambridge, and Applied Machine Learning from Johns Hopkins. He also holds professional certifications in Data Science, Com-puter Science, and Artificial Intelligence from Harvard, along with a MicroMasters in Data, Economics, and Development Policy from MIT.
As Deputy Director at the Forest Survey of India, Dr. Awadhiya works at the inter-section of technology and ecology, integrating Artificial Intelligence and Machine Learning into natural resource monitoring and management. His interests span biodiversity conservation, forest management, and scientific research. A prolific writer, he has over fifty publications and holds two patents.
Dr. Awadhiya is the recipient of several prestigious honors, including the NTSE Scholarship, KVPY Fellowship, Vanyaprani Sanrakshan Puraskar, Shri P. Srinivas Memorial Prize, K. P. Sagriya Shreshta Vaniki Puraskar, and the S. K. Seth Prize. Beyond his professional life, he enjoys teaching, photography, painting, filmmak-ing, and creative writing.
