Asmi Ariv

Education

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  Deeplearning.ai (Coursera)

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  Deep Learning Specialization Deep Learning and Artificial Intelligence 96.42%

  The Deep Learning Specialization is designed to prepare learnersto participate in the development of cutting-edge AI technology,and to understand the capability, the challenges, and theconsequences of the rise of deep learning. Through fiveinterconnected courses, learners develop a profound knowledgeof the hottest AI algorithms, mastering deep learning from itsfoundations (neural networks) to its industry applications(Computer Vision, Natural Language Processing… Mehr anzeigen The Deep Learning Specialization is designed to prepare learnersto participate in the development of cutting-edge AI technology,and to understand the capability, the challenges, and theconsequences of the rise of deep learning. Through fiveinterconnected courses, learners develop a profound knowledgeof the hottest AI algorithms, mastering deep learning from itsfoundations (neural networks) to its industry applications(Computer Vision, Natural Language Processing, SpeechRecognition, etc.). Weniger anzeigen

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  Stanford University and Deeplearning.ai via Coursera

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  Machine Learning Specialization Machine Learning 100%

  Modern machine learning concepts, including supervised learning (linear regression, logisticregression, neural networks, decision trees), unsupervised learning (clustering, anomaly detection), recommender systems, and reinforcement learning including the best practices for building machine learning models. Practical skills to apply machine learning techniques to challenging real-world problems.

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  KJ Somaiya, SIMSR

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  Diploma Journalism & Mass Communications
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  CSR-CoE (MeT & ICSM in collaboration with IICA, MoCA)

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  Advance PG Certification in CSR Corporate Social Responsibility A+ (above 75%)
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  Duke University

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  Data Analysis with R, a 5-course specialization by Duke University on Coursera Data Science 95.36%

  This Specialization included conceptual understanding of the unified nature of statistical inference, frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis.

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  University of Sedona

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  Doctor of Philosophy - PhD Metaphysical Counseling
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  The Johns Hopkins University

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  Data Science Specialization through Coursera Data Science 96.8%

  The course which spans across a year includes the following courses:1. The Data Scientist’s Toolbox (R, RStudio, GitHub, etc.)2. R Programming3. Getting and Cleaning Data4. Exploratory Data Analysis5. Reproducible Research6. Statistical Inference7. Regression Models8. Practical Machine Learning9. Developing Data Products10. Data Science Capstone

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  Indian Institute of Technology, Hyderabad

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  Advanced Business Analytics Data Science Completed

  The course involves a regress study of all the important areas of business analytics as an application of data science in the business world. The course covered all the topics including data cleaning, exploratory data analysis, descriptive and predictive data analytics, model building based on data mining, text mining using statistical and machine learning techniques.

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  Stanford University

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  Machine Learning by Stanford University on Coursera Machine Learning and Data Mining 99.6%

  This course provides training on machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).

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  Sri Sathya Sai Institute of Higher Learning

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  B.Sc. Physics (Hons) Outstanding (4.53/5 CGPA)