If you are interested to become a Machine Learning Specialist, you are in the right place, because here we have the best LinkedIn course that you will love it. Machine Learning proves to be the future of our civilization, something that will help us to elevate our achievements to the next level, and explore new things, and all in all increase the quality of our life. The job positions in Machine Learning areas are one of the highest paying in the whole IT industry due to the fact that it requires knowledge in Mathematics, Statistics, Computer Science, and Software Engineering all combined. Now, to gain knowledge in all of these fields can be time-consuming due to all of those are sciences in themselves. However, there are huge corporations that have a huge need for experts in these areas and do not have the time that it takes to create these experts as we've already mentioned.

The recitation sessions in the first weeks of the class will give an overview of the expected background. Notes and reading assignments will be posted periodically on the course Web site.

Due to the transfer learning nature of BERT model, researchers have achieved better performance than base BERT by further pre-training the original BERT on a huge domain-specific corpus. Due to the special nature of mathematical texts which often contain math equations and symbols, the original BERT model pre-trained on general English context will not fit Natural Language Processing (NLP) tasks in mathematical education well. Therefore, we propose MathBERT, a BERT pre-trained on large mathematical corpus including pre-k to graduate level mathematical content to tackle math-specific tasks. In addition, We generate a customized mathematical vocabulary to pre-train with MathBERT and compare the performance to the MathBERT pre-trained with the original BERT vocabulary. We select three important tasks in mathematical education such as knowledge component, auto-grading, and knowledge tracing prediction to evaluate the performance of MathBERT. Our experiments show that MathBERT outperforms the base BERT by 2-9\% margin. In some cases, MathBERT pre-trained with mathematical vocabulary is better than MathBERT trained with original vocabulary.To our best knowledge, MathBERT is the first pre-trained model for general purpose mathematics education tasks.

With the capability of accurately representing a functional relationship between the inputs of a physical system's model and output quantities of interest, neural networks have become popular for surrogate modeling in scientific applications. However, as these networks are over-parameterized, their training often requires a large amount of data. To prevent overfitting and improve generalization error, regularization based on, e.g., $\ell_1$- and $\ell_2$-norms of the parameters is applied. Similarly, multiple connections of the network may be pruned to increase sparsity in the network parameters. In this paper, we explore the effects of sparsity promoting $\ell_1$-regularization on training neural networks when only a small training dataset from a high-fidelity model is available. As opposed to standard $\ell_1$-regularization that is known to be inadequate, we consider two variants of $\ell_1$-regularization informed by the parameters of an identical network trained using data from lower-fidelity models of the problem at hand. These bi-fidelity strategies are generalizations of transfer learning of neural networks that uses the parameters learned from a large low-fidelity dataset to efficiently train networks for a small high-fidelity dataset. We also compare the bi-fidelity strategies with two $\ell_1$-regularization methods that only use the high-fidelity dataset. Three numerical examples for propagating uncertainty through physical systems are used to show that the proposed bi-fidelity $\ell_1$-regularization strategies produce errors that are one order of magnitude smaller than those of networks trained only using datasets from the high-fidelity models.

Apply these concepts to vision tasks such as automatic image captioning and object tracking, and build a robust portfolio of computer vision projects.

Demand for quantitative talent is growing at incredible rates. Data-driven traders are now responsible for more than 30% of all US stock trades by investors (or about $1 trillion USD worth of investments, up from 14% in 2013). This scenario represents incredible opportunity for individuals eager to apply cutting-edge technologies to trading and finance. Whether you want to pursue a new job in finance, launch yourself on the path to a quant trading career, or master the latest AI applications in trading and quantitative finance, this program will give you the opportunity to build an impressive portfolio of real-world projects. You will build financial models on real data, and work on your own trading strategies using natural language processing, recurrent neural networks, and random forests.

In this chapter, we investigate whether unique, optimal decision boundaries can be found. In order to do so, we first have to revisit several fundamental mathematical principles. Regularization is a mathematical tool, which allows us to find unique solutions even for highly ill-posed problems. In order to use this trick, we review norms and how they can be used to steer regression problems. Rosenblatt's Perceptron and Multi-Layer Perceptrons which are also called Artificial Neural Networks inherently suffer from this ill-posedness.

In this course, we are going to show you how to SUPERCHARGE your Power BI skills, and learn to create INTERACTIVE Dashboards and INCREDIBLE reports. Microsoft Power BI is the easiest to use reporting, data analysis, and interactive dashboard tool available today! Microsoft Power BI is available for free – all you need to do is sign up! There is no coding required! Power BI allows you to do that but it's not necessary.

Machine Learning and Artificial Intelligence is considered a game changer. It is the biggest shift of the decade in Machine Learning.

Graph Neural Network This course will provide complete introductory materials for learning Graph Neural Network. By finishing this course you get a good understanding of the topic both in theory and practice. This means you will see both math and code. Description In recent years, Graph Neural Network (GNN) has gained increasing popularity in various domains due to its great expressive power and outstanding performance. Graph structures allow us to capture data with complex structures and relationships, and GNN provides us the opportunity to study and model this complex data representation for tasks such as classification, clustering, link prediction, and robust representation.