化学家实用机器学习指南

《化学家实用机器学习指南》是一门专注于应用数据科学解决化学问题的实战课程。课程将带领学习者利用机器学习技术预测分子性质、分类化合物及解析光谱数据，涵盖溶解度预测、HOMO-LUMO能隙计算、混合红外光谱分离等核心应用。通过Python工具库（NumPy、pandas、matplotlib、scikit-learn）的实践操作，学员将掌握化学数据集预处理、分子描述符探索、回归/分类/聚类算法选择与实施等关键技能。课程无需深厚编程基础（建议具备Python入门知识），注重将机器学习与化学研究深度融合，帮助学习者建立数据驱动的研究思维，提升科研产出能力。
MP4 | 视频：h264，1920×1080 | 音频：AAC，44.1 KHz
语言：英语 | 大小：2.39 GB | 时长：3小时58分钟

Machine Learning For Chemists: Practical Applications。Use Data Science to solve chemistry problems: predict molecular properties, classify compounds, and resolve spectra。Machine Learning is transforming the way chemists analyze data, predict properties, and accelerate discoveries in various sub-disciplines of Chemistry. This course is designed to give you the practical skills and confidence to apply machine learning directly to chemical problems.Starting from the basics, you will learn how to preprocess chemical datasets, explore molecular descriptors, and choose the right algorithms for prediction and classification. Step by step, you will apply regression, classification, and clustering methods to real-world chemical examples. You will gain hands-on experience with Python libraries such as NumPy, pandas, matplotlib, and scikit-learn—without being overwhelmed by unnecessary theory.Throughout the course, we’ll connect machine learning concepts to chemistry applications: predicting molecular properties (solubility) , analyzing spectra, classifying compounds, resolving complex spectra of mixture to individual spectra and grouping similar molecules. By the end, you will not only understand how these algorithms work but also know how to implement them for your own research and projects.This is not just a programming course—it’s a transformation in how you approach chemistry. You will leave with a solid foundation in applying ML to chemical data and the ability to contribute to modern, data-driven research.Note: Basic knowledge of Python is recommended for this course。

What you’ll learn
Understand the fundamentals of machine learning and how they apply to chemical problems. Confidently handle real-world chemical datasets for ML applications.
Select and implement supervised and unsupervised learning algorithms for chemistry. Apply regression, classification, and clustering methods to chemical data.
Interpret model performance using evaluation metrics relevant to chemical research. Visualize chemical datasets and model predictions effectively
Use Python libraries (scikit-learn, pandas, numpy) for ML tasks in chemistry, Apply ML to predict molecular properties such as Solubility, HOMO-LUMO gap
Gain practical skills to integrate ML into chemistry. Use ML-driven insights to support research publications and projects.
Separate the spectra (infra-red spectra) of each compound from a mixture of spectra using Machine learning approaches

Requirements
This course is designed for chemists and researchers who want to apply machine learning to chemical problems. To follow along smoothly, some basic knowledge of Python—such as variables, loops, functions, and working with libraries like NumPy or pandas is highly valuable. No advanced coding skills are required, but a beginner-level familiarity with Python will help you focus on the chemistry and machine learning concepts without getting stuck on programming basics. In case you are absolute beginner, i have added some videos by the end of the course where you can get bsic idea. Alternatively, i have a couple of courses on python for chemists which will guide you thoroughly on how to use python in chemistry