Smart Analytics, Machine Learning, and AI on GCP

Start Date: 02/23/2020

Course Type: Common Course

Course Link: https://www.coursera.org/learn/smart-analytics-machine-learning-ai-gcp

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About Course

Incorporating machine learning into data pipelines increases the ability of businesses to extract insights from their data. This course covers several ways machine learning can be included in data pipelines on Google Cloud Platform depending on the level of customization required. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces AI Platform Notebooks and BigQuery Machine Learning. Also, this course covers how to productionalize machine learning solutions using Kubeflow. Learners will get hands-on experience building machine learning models on Google Cloud Platform using QwikLabs.

Course Syllabus

Big Data Analytics with Cloud AI Platform Notebooks
Summary

Deep Learning Specialization on Coursera

Course Introduction

Smart Analytics, Machine Learning, and AI on GCP In this course, we will focus on the core concepts and technologies that make using analytics easy. We will cover topics such as how analytics are implemented, why so much of data is collected, and how companies use analytics to identify and address their data-gathering challenges. We will also explain how machine learning is implemented, and why it takes so long. We will introduce the Analytics Stream tool, which allows analytics to be performed on demand. We will also introduce the Analytics Data Pipeline, which allows analytics to be performed on-premises or in a data-center. We will cover the different types of analytics and the data structures used to implement them. We will cover various data-processing techniques, including how GCP is implemented as a DynamoDB, as well as machine learning and streaming. We will also explain the use of Big Data in analytics so that when new data streams arise, they can be automatically adapted to meet the needs. We will also explain how analytics is implemented in Hadoop, Spark, and Node.js on Google Cloud Platform. We will cover installation and activation of analytics services, as well as how to use the Analytics Console to perform analysis. We will also introduce the BigQuery technology that is used for streaming data. We will also describe the basic algorithmic and data-flow analysis that is used in Google Machine Learning. This course is the third in a series on the use of machine learning in cybersecurity. Note: This course uses the Web Analytics

Course Tag

Related Wiki Topic

Article Example
Learning analytics In “The State of Learning Analytics in 2012: A Review and Future Challenges” Rebecca Ferguson tracks the progress of analytics for learning as a development through:
Learning analytics Gašević, Dawson, and Siemens argue that computational aspects of learning analytics need to be linked with the existing educational research if the field of learning analytics is to deliver to its promise to understand and optimize learning.
Machine learning Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to "produce reliable, repeatable decisions and results" and uncover "hidden insights" through learning from historical relationships and trends in the data.
Learning analytics In some prominent cases like the inBloom disaster even full functional systems have been shut down due to lack of trust in the data collection by governments, stakeholders and civil rights groups. Since then, the Learning Analytics community has extensively studied legal conditions in a series of experts workshops on 'Ethics & Privacy 4 Learning Analytics' that constitute the use of trusted Learning Analytics. Drachsler & Greller released a 8-point checklist named DELICATE that is based on the intensive studies in this area to demystify the ethics and privacy discussions around Learning Analytics.
Learning analytics Chatti, Muslim and Schroeder note that the aim of Open Learning Analytics (OLA) is to improve learning effectiveness in lifelong learning environments. The authors refer to OLA as an ongoing analytics process that encompasses diversity at all four dimensions of the learning analytics reference model.
Learning analytics A systematic overview on learning analytics and its key concepts is provided by Chatti et al. (2012) and Chatti et al. (2014) through a reference model for learning analytics based on four dimensions, namely data, environments, context (what?), stakeholders (who?), objectives (why?), and methods (how?).
Learning analytics The ethics of data collection, analytics, reporting and accountability has been raised as a potential concern for Learning Analytics (e.g.,), with concerns raised regarding:
Learning analytics "Learning analytics is the use of intelligent data, learner-produced data, and analysis models to discover information and social connections for predicting and advising people's learning."
Fractal Analytics Fractal Analytics is based on Artificial Intelligence and Machine Learning techniques. Their products include Customer Genomics; Trial Run, a cloud-based business platform; text mining suite: dCrypt; and Centralized Analytics Environment (CAE), a collaborative workbench built on the KNIME Server.
Learning analytics Much of the software that is currently used for learning analytics duplicates functionality of web analytics software, but applies it to learner interactions with content. Social network analysis tools are commonly used to map social connections and discussions (see Social network analysis software). Some examples of learning analytics software tools:
Learning analytics The definition and aims of Learning Analytics are contested. One earlier definition discussed by the community suggested that
Predictive analytics The approaches and techniques used to conduct predictive analytics can broadly be grouped into regression techniques and machine learning techniques.
Learning analytics The first graduate program focused specifically on learning analytics was created by Dr. Ryan Baker and launched in the Fall 2015 semester at Teachers College - Columbia University. The program description states that "data about learning and learners are being generated today on an unprecedented scale. The fields of learning analytics (LA) and educational data mining (EDM) have emerged with the aim of transforming this data into new insights that can benefit students, teachers, and administrators. As one of world's leading teaching and research institutions in education, psychology, and health, we are proud to offer an innovative graduate curriculum dedicated to improving education through technology and data analysis."
Learning analytics In a discussion of the history of analytics, Cooper highlights a number of communities from which learning analytics draws techniques, including:
Learning analytics It has been pointed out that there is a broad awareness of analytics across educational institutions for various stakeholders, but that the way 'learning analytics' is defined and implemented may vary, including:
Learning analytics Differentiating the fields of educational data mining (EDM) and learning analytics (LA) has been a concern of several researchers. George Siemens takes the position that educational data mining encompasses both learning analytics and academic analytics, the former of which is aimed at governments, funding agencies, and administrators instead of learners and faculty. Baepler and Murdoch define academic analytics as an area that ""...combines select institutional data, statistical analysis, and predictive modeling to create intelligence upon which learners, instructors, or administrators can change academic behavior"". They go on to attempt to disambiguate educational data mining from academic analytics based on whether the process is hypothesis driven or not, though Brooks questions whether this distinction exists in the literature. Brooks instead proposes that a better distinction between the EDM and LA communities is in the roots of where each community originated, with authorship at the EDM community being dominated by researchers coming from intelligent tutoring paradigms, and learning anaytics researchers being more focused on enterprise learning systems (e.g. learning content management systems).
Learning analytics It shows ways to design and provide privacy conform Learning Analytics that can benefit all stakeholders. The full DELICATE checklist is publicly available here.
Active learning (machine learning) Recent developments are dedicated to hybrid active learning and active learning in a single-pass (on-line) context, combining concepts from the field of Machine Learning (e.g., conflict and ignorance) with adaptive, incremental learning policies in the field of Online machine learning.
International Conference on Machine Learning The International Conference on Machine Learning (ICML) is the leading international academic conference in machine learning, attracting annually more than 2000 participants from all over the world. It is supported by the International Machine Learning Society (IMLS).
International Conference on Machine Learning The conference attracts leading innovations in the field of machine learning. ICML is a top tier conference, and is one of the two most influential conferences in Machine Learning (along with Conference on Neural Information Processing Systems).