In the field of polymer informatics, utilizing machine learning (ML) techniques to evaluate the glass transition temperature Tg and other properties of polymers has attracted extensive attention. This data-centric approach is much more efficient and practical than the laborious experimental measurements when encountered a daunting number of polymer structures. Various ML models are demonstrated to perform well for Tg prediction. Nevertheless, they are trained on different data sets, using different structure representations, and based on different feature engineering methods. Thus, the critical question arises on selecting a proper ML model to better handle the Tg prediction with generalization ability. To provide a fair comparison of different ML techniques and examine the key factors that affect the model performance, we carry out a systematic benchmark study by compiling 79 different ML models and training them on a large and diverse data set. The three major components in setting up an ML model are structure representations, feature representations, and ML algorithms. In terms of polymer structure representation, we consider the polymer monomer, repeat unit, and oligomer with longer chain structure. Based on that feature, representation is calculated, including Morgan fingerprinting with or without substructure frequency, RDKit descriptors, molecular embedding, molecular graph, etc. Afterward, the obtained feature input is trained using different ML algorithms, such as deep neural networks, convolutional neural networks, random forest, support vector machine, LASSO regression, and Gaussian process regression. We evaluate the performance of these ML models using a holdout test set and an extra unlabeled data set from high-throughput molecular dynamics simulation. The ML model’s generalization ability on an unlabeled data set is especially focused, and the model’s sensitivity to topology and the molecular weight of polymers is also taken into consideration. This benchmark study provides not only a guideline for the Tg prediction task but also a useful reference for other polymer informatics tasks.

Machine Learning Courses Market Outlook

The global market size of Machine Learning (ML) courses is witnessing substantial growth, with market valuation expected to reach $3.1 billion in 2023 and projected to soar to $12.6 billion by 2032, exhibiting a robust CAGR of 16.5% over the forecast period. This rapid expansion is fueled by the increasing adoption of artificial intelligence (AI) and machine learning technologies across various industries, the rising need for upskilling and reskilling in the workforce, and the growing penetration of online education platforms.

One of the most significant growth factors driving the ML courses market is the escalating demand for AI and ML expertise in the job market. As industries increasingly integrate AI and machine learning into their operations to enhance efficiency and innovation, there is a burgeoning need for professionals with relevant skills. Companies across sectors such as finance, healthcare, retail, and manufacturing are investing heavily in training programs to bridge the skills gap, thus driving the demand for ML courses. Additionally, the rapid evolution of technology necessitates continuous learning, further bolstering market growth.

Another crucial factor contributing to the market's expansion is the proliferation of online education platforms that offer flexible and affordable ML courses. Platforms like Coursera, Udacity, edX, and Khan Academy have made high-quality education accessible to a global audience. These platforms offer an array of courses tailored to different skill levels, from beginners to advanced learners, making it easier for individuals to pursue continuous learning and career advancement. The convenience and flexibility of online learning are particularly appealing to working professionals and students, thereby driving the market's growth.

The increasing collaboration between educational institutions and technology companies is also playing a pivotal role in the growth of the ML courses market. Many universities and colleges are partnering with leading tech firms to develop specialized curricula that align with industry requirements. These collaborations help ensure that the courses offered are up-to-date with the latest technological advancements and industry standards. As a result, students and professionals are better equipped with the skills needed to thrive in a technology-driven job market, further propelling the demand for ML courses.

On a regional level, North America holds a significant share of the ML courses market, driven by the presence of numerous leading tech companies and educational institutions, as well as a highly skilled workforce. The region's strong emphasis on innovation and technological advancement is a key driver of market growth. Additionally, Asia Pacific is emerging as a lucrative market for ML courses, with countries like China, India, and Japan witnessing increased investments in AI and ML education and training. The rising internet penetration, growing popularity of online education, and government initiatives to promote digital literacy are some of the factors contributing to the market's growth in this region.

Self-Supervised Learning, a cutting-edge approach in the realm of machine learning, is gaining traction as a pivotal element in the development of more autonomous AI systems. Unlike traditional supervised learning, which relies heavily on labeled data, self-supervised learning leverages unlabeled data to train models, significantly reducing the dependency on human intervention for data annotation. This method is particularly advantageous in scenarios where acquiring labeled data is costly or impractical. By enabling models to learn from vast amounts of unlabeled data, self-supervised learning enhances the ability of AI systems to generalize from limited labeled examples, thereby improving their performance in real-world applications. The integration of self-supervised learning techniques into machine learning courses is becoming increasingly important, as it equips learners with the knowledge to tackle complex AI challenges and develop more robust models.

Course Type Analysis

The Machine Learning Courses market is segmented by course type into online courses, offline courses, bootcamps, and workshops. Online courses dominate the segment due to their accessibility, flexibility, and cost-effectiveness. Platforms like Coursera and Udacity have democratized access to high-quality ML education, enabling lear

Machine learning (ML) methods provide a pathway to accurately predict molecular properties, leveraging patterns derived from structure–property relationships within materials databases. This approach holds significant importance in drug discovery and materials design, where the rapid, efficient screening of molecules can accelerate the development of new pharmaceuticals and chemical materials for highly specialized target application. Unsupervised and self-supervised learning methods applied to graph-based or geometric models have garnered considerable traction. More recently, transformer-based language models have emerged as powerful tools. Nevertheless, their application entails considerable computational resources, owing to the need for an extensive pretraining process on a vast corpus of unlabeled chemical data sets. To this end, we present a semisupervised strategy that harnesses substructure vector embeddings in conjunction with a ML-based feature selection workflow to predict various molecular and drug properties. We evaluate the efficacy of our modeling methodology across a diverse range of data sets, encompassing both regression and classification tasks. Our findings demonstrate superior performance compared to most existing state-of-the-art algorithms, while offering advantages in terms of balancing model accuracy with computational requirements. Moreover, our approach provides deeper insights into feature interactions that are essential for model interpretability. A case study is conducted to predict the lipophilicity of chemical molecules, exemplifying the robustness of our strategy. The result underscores the importance of meticulous feature analysis and selection over a mere reliance on predictive modeling with a high degree of algorithmic complexity.