Welcome to Natural Language Processing and Capstone Assignment. In this course we will begin with an Recognize how technical and business techniques can be used to deliver business insight, competitive intelligence, and consumer sentiment. The course concludes with a capstone assignment in which you will apply a wide range of what has been covered in this specialization.
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Spécialisation Data Science FundamentalsUniversité de Californie à IrvineÀ propos de ce cours
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Cours 4 sur 4 dans le
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Anglais
Sous-titres : Anglais
Ce que vous allez apprendre
Applications of natural language processing
Basics of social media analytics
Future trends and possibilities in data science
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 4 sur 4 dans le
Niveau intermédiaire
Approx. 5 heures pour terminer
Anglais
Sous-titres : Anglais
Offert par
Université de Californie à Irvine
Since 1965, the University of California, Irvine has combined the strengths of a major research university with the bounty of an incomparable Southern California location. UCI’s unyielding commitment to rigorous academics, cutting-edge research, and leadership and character development makes the campus a driving force for innovation and discovery that serves our local, national and global communities in many ways.
Programme du cours : ce que vous apprendrez dans ce cours
1 heure pour terminer
Natural Language Processing I
Welcome to Module 1, Natural Language Processing I. In this module we will begin with an introduction to text analytics, or natural language processing (NLP). We will explore the numerous applications of NLP and discuss one of the most popular applications - sentiment analysis.
1 heure pour terminer
1 vidéo (Total 3 min), 2 lectures
1 heure pour terminer
Natural Language Processing II
Welcome to Module 2, Natural Language Processing II. In this module we will continue our exploration of natural language processing with a review of topic modeling and one of the most effective topic detection techniques currently in use - Latent Dirichlet allocation (LDA). In addition, we will define several technical terms and concepts commonly used in text mining.
1 heure pour terminer
2 lectures
1 heure pour terminer
The Past, Present, and Future of Data Science I
Welcome to Module 3, Past, Present, and Future of Data Science I. In this module we will provide a historical perspective of the terminology applied to data analytics, as well as a forward-looking discussion of several key trends emerging in data science. We will also explore several leading-edge enablers and enhancers of data science, including deep learning, explainable AI, and automated machine learning.
1 heure pour terminer
1 vidéo (Total 4 min), 2 lectures
2 heures pour terminer
The Past, Present, and Future of Data Science II
Welcome to Module 4, Past, Present, and Future of Data Science II. In this module we will continue our exploration of new practices in data science and predictive modelling, including model ensembles, sensor technologies and IoT, geospatial analytics, and cloud computing. We will conclude this program with an activity to bring everything you’ve learned in this program together to develop a data analytics plan.
2 heures pour terminer
2 lectures
À propos du Spécialisation Data Science Fundamentals
This specialization demystifies data science and familiarizes learners with key data science skills, techniques, and concepts. The course begins with foundational concepts such as analytics taxonomy, the Cross-Industry Standard Process for Data Mining, and data diagnostics, and then moves on to compare data science with classical statistical techniques. The course also provides an overview of the most common techniques used in data science, including data analysis, statistical modeling, data engineering, manipulation of data at scale (big data), algorithms for data mining, data quality, remediation and consistency operations.
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