Machine Learning

    Designing and Implementing Production Machine Learning Systems (MLOps)

    This course is an introduction to ML systems in production that will demonstrate and give students exposure to how real production ML systems operate. Using Python, Docker, Kubernetes, Google Cloud and various open-source tools, students will bring the different components of an ML system to life and setup real, automated infrastructure.

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    Natural Language Processing for Production (NLP)

    This course demonstrates a practical and intuitive approach to NLP applications through variety of different use-cases. Essentials and practical fundamentals of NLP methods are presented via generic Python packages including but not limited to Regex, NLTK, SpaCy and Huggingface. The high-level foundations followed by hands-on code examples on a notebook environment will be studied touching on different aspects of NLP from conventional statistical text analytics approaches to the state-of-the-art deep/transfer learning models paired with result interpretations, industry challenges, visualizations and a prototype web application.

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    Data Science with Python: Machine Learning

    This 20-hour Machine Learning with Python course covers all the basic machine learning methods and Python modules (especially Scikit-Learn) for implementing them. The five sessions cover: simple and multiple Linear regressions; classification methods including logistic regression, discriminant analysis and naive bayes, support vector machines (SVMs) and tree based methods; cross-validation and feature selection; regularization; principal component analysis (PCA) and clustering algorithms. After successfully completing of this course, you will be able to explain the principles of machine learning algorithms and implement these methods to analyze complex datasets and make predictions in Python.

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    Data Science with R: Machine Learning

    This 35-hour Machine Learning with R course introduces both the theoretical foundation of machine learning algorithms as well as their practical applications in R. It will introduce you to data mining, performance measures and dimension reduction, regression models, both linear and generalized, KNN and NaΓ―ve Bayes models, tree models, and SVMs as well as the Association Rule for analysis. After successfully completing of this course, you will be able to break down the mathematics behind major machine learning algorithms, explain the principles of machine learning algorithms, and implement these methods to solve real-world problems.

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