Data Science and Machine Leaning principles for Science

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Free Download Data Science and Machine Leaning principles for Science
Published 8/2024
Created by Guilherme Matos Passarini, phD,Jones Granatyr
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 45 Lectures ( 3h 48m ) | Size: 957 MB

Learn the basics and principles of data and machine learning for scientific problems
What you'll learn:
To understand the concepts of data science and machine learning and how they can be used in science
To know the main algorithms used in tasks of classification, regression, and clustering
To know the main architectures of neural networks
To understand how you can use algorithms/analyses in science projects/investigations/studies
Requirements:
Basic math knowledge is desirable
Description:
The course "Understanding DS/ML Concepts for Science" is designed to bridge the gap between traditional scientific disciplines and the rapidly evolving fields of Data Science (DS) and Machine Learning (ML). As the landscape of scientific research increasingly relies on large datasets and complex computational methods, it is essential for scientists to understand how to apply DS and ML techniques to enhance their research.This course provides a comprehensive introduction to the core concepts of Data Science and Machine Learning, specifically tailored for scientists and researchers in fields such as biology, chemistry, physics, and environmental science. Participants will explore the foundational principles of data analysis, including data collection, cleaning, and visualization, before diving into machine learning algorithms that can uncover patterns and make predictions from data.The course does not feature programming, but focuses on basic, theoretical concepts related to the subject. This course is divided into 6 main chapters, which are:1. IntroductionHere, we'll introduce the course and its main features2. Concepts of DS/MLHere we'll explore basic concepts, such as variables, data scaling, training, datasets, data visualization, and etc3. ClassificationIn this chapter, we'll discuss the main algorithms used for classification, such as decision trees, random forests, Naive Bayes, and KNN, with examples on how we could use such algorithms in science4. RegressionIn this chapter, we'll discuss briefly linear regression and multiple linear regression, with its main concepts, and examples using a scientific context5. ClusteringIn this session, we'll discuss standard clustering and hierarchical clustering, and a few examples for science6. Neural networksHere, we introduce the concept of neural networks, their inspirations in biological neurons, and some architectures used, such as Feedforward neural networks (FNN), convolutional neural networks (CNN), recurrent neural networks (RNN), and Hopfield neural networks.
Who this course is for:
The primary target audience for this course are people from STEM interested to understand and use concepts of DS/ML
People from an IT/Computer science interested to know how the algorithms can be used in science projects
Prople from a math background interested to understand concepts of DS/ ML, and science
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