Are you a working professional and looking for the best advanced data science courses? If yes, then you are in the right place. In this article, you will find the 7 Best Data Science Courses for Working Professionals. To gain data science skills, there are numerous courses available. So, without wasting your time, let's start finding the Best Data Science Courses for Working Professionals– This is a Nano-Degree Program offered by Udacity.

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Learning how to program in Python is not always easy especially if you want to use it for Data science. Indeed, there are many of different tools that have to be learned to be able to properly use Python for Data science and machine learning and each of those tools is not always easy to learn. Then you will definitely love this course. Not only you will learn all the tools that are used for Data science but you will also improve your Python knowledge and learn to use those tools to be able to visualize your projects. This course is structured in a way that you will be able to to learn each tool separately and practice by programming in python directly with the use of those tools.

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.

Machine learning continues to become more available daily, and one exciting development is the straightforward availability of machine learning models since data is at the essence of any machine learning problem. Such data is used for the training, validation, and testing of models, and the performance reports of a machine learning model need to be calculated on the independent test data rather than the training or validation tests. Lastly, the data needs to be split so that all three datasets, like training, test, and validation, can have related statistical characteristics. The first crucial step in a standard machine learning workflow after data cleansing is training -- the method of passing training data to a model to learn to identify patterns. After training, the subsequent step is testing, where we examine how the model performs on data outside of the training set. This workflow is known as model evaluation.

In the supervised learning domain, considering the recent prevalence of algorithms with high computational cost, the attention is steering towards simpler, lighter, and less computationally extensive training and inference approaches. In particular, randomized algorithms are currently having a resurgence, given their generalized elementary approach. By using randomized neural networks, we study distributed classification, which can be employed in situations were data cannot be stored at a central location nor shared. We propose a more efficient solution for distributed classification by making use of a lossy compression approach applied when sharing the local classifiers with other agents. This approach originates from the framework of hyperdimensional computing, and is adapted herein. The results of experiments on a collection of datasets demonstrate that the proposed approach has usually higher accuracy than local classifiers and getting close to the benchmark - the centralized classifier. This work can be considered as the first step towards analyzing the variegated horizon of distributed randomized neural networks.

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.

In adversarial data collection (ADC), a human workforce interacts with a model in real time, attempting to produce examples that elicit incorrect predictions. Researchers hope that models trained on these more challenging datasets will rely less on superficial patterns, and thus be less brittle. However, despite ADC's intuitive appeal, it remains unclear when training on adversarial datasets produces more robust models. In this paper, we conduct a large-scale controlled study focused on question answering, assigning workers at random to compose questions either (i) adversarially (with a model in the loop); or (ii) in the standard fashion (without a model). Across a variety of models and datasets, we find that models trained on adversarial data usually perform better on other adversarial datasets but worse on a diverse collection of out-of-domain evaluation sets. Finally, we provide a qualitative analysis of adversarial (vs standard) data, identifying key differences and offering guidance for future research.

Transformer model architectures have become an indispensable staple in deep learning lately for their effectiveness across a range of tasks. Recently, a surge of "X-former" models have been proposed which improve upon the original Transformer architecture. However, most of these variants make changes only around the quadratic time and memory complexity of self-attention, i.e. the dot product between the query and the key. What's more, they are calculate solely in Euclidean space. In this work, we propose a novel Transformer with Hyperbolic Geometry (THG) model, which take the advantage of both Euclidean space and Hyperbolic space. THG makes improvements in linear transformations of self-attention, which are applied on the input sequence to get the query and the key, with the proposed hyperbolic linear. Extensive experiments on sequence labeling task, machine reading comprehension task and classification task demonstrate the effectiveness and generalizability of our model. It also demonstrates THG could alleviate overfitting.