Download Data Science and Machine Leaning principles for science 2024
Data Science and Machine Leaning principles for science udemy video course download, and the course 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.
This course is divided into 6 main chapters, which are:
- 1. Introduction: Here, we’ll introduce the course and its main features, including course content and how to watch the course
- 2. Concepts of DS/ML: Here we’ll explore basic concepts, such as variables, data scaling, training, datasets, data visualization, and etc.
- 3. Classification: In 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 science
- 4. Regression: In this chapter, we’ll discuss briefly linear regression and multiple linear regression, with its main concepts, and examples using a scientific context
- 5. Clustering: In this session, we’ll discuss standard clustering and hierarchical clustering, and a few examples for science
- 6. Neural networks: Here, 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.
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
Course content
- Course introduction
- Concepts about data, variables, and intro to ML
- Machine Learning: classification
- Machine Learning: regression
- Machine Learning: clustering
- Introduction to neural networks
- Conclusion
Course details
- Video quality: MP4 | Video: h264, 1280 × 720
- Audio quality: Audio: AAC, 44.1 KHz, 2 Ch
- Video duration: 3h 48m
- Number of lessons: 07 sections, 45 lectures
- Language: English
- Compressed file size: 950 MB
