Author: itzAmrit

Demographic Methods across the Tree of Life by Roberto Salguero-Gómez (Gomez) and Marlène Gamelon
Requirements: .PDF reader, 61 MB
Overview: Demography is everywhere in our lives: from birth to death. Indeed, the universal currencies of survival, development, reproduction, and recruitment shape the performance of all species, from microbes to humans. The number of techniques for demographic data acquisition and analyses across the
entire tree of life (microbes, fungi, plants, and animals) has drastically increased in recent decades. These developments have been partially facilitated by the advent of technologies such as GIS and drones, as well as analytical methods including Bayesian statistics and high-throughput molecular
analyses. However, despite the universality of demography and the significant research potential that could emerge from unifying: (i) questions across taxa, (ii) data collection protocols, and (iii) analytical tools, demographic methods to date have remained taxonomically siloed and methodologically
disintegrated. This is the first book to attempt a truly unified approach to demography and population ecology in order to address a wide range of questions in ecology, evolution, and conservation biology across the entire spectrum of life.
Genre: Non-Fiction > Educational

Download Instructions:
https://ouo.io/d9ZPSG
https://ouo.io/jB32qC

Applied Machine Learning Solutions with Python: Production-ready ML Projects Using Cutting-edge Libraries and Powerful Statistical Techniques (English Edition) by Siddhanta Bhatta
Requirements: .ePUB, .PDF reader, 16 MB
Overview: This book discusses how to apply machine learning to real-world problems by utilizing real-world data. In this book, you will investigate data sources, become acquainted with data pipelines, and practice how machine learning works through numerous examples and case studies.

The book begins with high-level concepts and implementation (with code!) and progresses towards the real-world of ML systems. It briefly discusses various concepts of Statistics and Linear Algebra. You will learn how to formulate a problem, collect data, build a model, and tune it. You will learn about use cases for data analytics, computer vision, and natural language processing. You will also explore nonlinear architecture, thus enabling you to build models with multiple inputs and outputs. You will get trained on creating a machine learning profile, various machine learning libraries, Statistics, and FAST API.

Throughout the book, you will use Python to experiment with machine learning libraries such as Tensorflow, Scikit-learn, Spacy, and FastAI. The book will help train our models on both Kaggle and our datasets.
Genre: Non-Fiction > Tech & Devices

Download Instructions:
https://ouo.io/Tkqwa5

https://ouo.io/xtLaMP