Distance learning Course in Engineering & Technology

See Distance learning Course Studies in Engineering & Technology 2017

Engineering & Technology

Bringing the classroom to the student by means of a strategically developed curriculum, dedicated expert faculty and up-to-date educational technology, distance learning courses are an excellent alternative to commuting to campus or relocating abroad. Distance learning courses in Engineering and Technology are offered in a number of fields and disciplines, from Architecture and Urban Planning to Computer Science and Information Technology. Students seeking professional development to enhance or build new qualifications may be interested in reading more about distance learning courses in Engineering and Technology.

The duration of distance learning courses in Engineering and Technology can range from a few days to a year, and may be pursued on a full time or part time basis. Hosted at accredited universities in Australia, Singapore, the USA and around the world, credits earned in distance learning courses in Engineering and Technology may be transferable to other programs such as Bachelor's or Master's degrees. Upon successful completion of a distance learning course in an Engineering and Technology discipline, graduates may be granted a certificate or diploma in recognition of their achievement.

If you are interested in learning more about distance learning courses in Engineering and Technology, scroll down and click to read more, today!

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Course in Machine Learning (Intermediate)

Coursera
Online Part time 8 months Open Enrollment USA USA Online

This Specialization provides a case-based introduction to the exciting, high-demand field of machine learning. You’ll learn to analyze large and complex datasets, build applications that can make predictions from data, and create systems that adapt and improve over time. [+]

Best Distance learning Course Studies in Engineering & Technology 2017. This Specialization provides a case-based introduction to the exciting, high-demand field of machine learning. You’ll learn to analyze large and complex datasets, build applications that can make predictions from data, and create systems that adapt and improve over time. In the final Capstone Project, you’ll apply your skills to solve an original, real-world problem through implementation of machine learning algorithms. Courses Machine Learning Foundations: A Case Study Approach Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: - Identify potential applications of machine learning in practice. - Describe the core differences in analyses enabled by regression, classification, and clustering. - Select the appropriate machine learning task for a potential application. - Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. - Represent your data as features to serve as input to machine learning models. - Assess the model quality in terms of relevant error metrics for each task. - Utilize a dataset to fit a model to analyze new data. - Build an end-to-end application that uses machine learning at its core. - Implement these techniques in Python. Machine Learning: Regression Case Study - Predicting Housing Prices In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression. In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data -- such as outliers -- on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets. Learning Outcomes: By the end of this course, you will be able to: - Describe the input and output of a regression model. - Compare and contrast bias and variance when modeling data. - Estimate model parameters using optimization algorithms. - Tune parameters with cross validation. - Analyze the performance of the model. - Describe the notion of sparsity and how LASSO leads to sparse solutions. - Deploy methods to select between models. - Exploit the model to form predictions. - Build a regression model to predict prices using a housing dataset. - Implement these techniques in Python. Machine Learning: Classification Case Studies: Analyzing Sentiment & Loan Default Prediction In our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...). In our second case study for this course, loan default prediction, you will tackle financial data, and predict when a loan is likely to be risky or safe for the bank. These tasks are an examples of classification, one of the most widely used areas of machine learning, with a broad array of applications, including ad targeting, spam detection, medical diagnosis and image classification. In this course, you will create classifiers that provide state-of-the-art performance on a variety of tasks. You will become familiar with the most successful techniques, which are most widely used in practice, including logistic regression, decision trees and boosting. In addition, you will be able to design and implement the underlying algorithms that can learn these models at scale, using stochastic gradient ascent. You will implement these technique on real-world, large-scale machine learning tasks. You will also address significant tasks you will face in real-world applications of ML, including handling missing data and measuring precision and recall to evaluate a classifier. This course is hands-on, action-packed, and full of visualizations and illustrations of how these techniques will behave on real data. We've also included optional content in every module, covering advanced topics for those who want to go even deeper! Learning Objectives: By the end of this course, you will be able to: - Describe the input and output of a classification model. - Tackle both binary and multiclass classification problems. - Implement a logistic regression model for large-scale classification. - Create a non-linear model using decision trees. - Improve the performance of any model using boosting. - Scale your methods with stochastic gradient ascent. - Describe the underlying decision boundaries. - Build a classification model to predict sentiment in a product review dataset. - Analyze financial data to predict loan defaults. - Use techniques for handling missing data. - Evaluate your models using precision-recall metrics. - Implement these techniques in Python (or in the language of your choice, though Python is highly recommended). Machine Learning: Clustering & Retrieval Case Studies: Finding Similar Documents A reader is interested in a specific news article and you want to find similar articles to recommend. What is the right notion of similarity? Moreover, what if there are millions of other documents? Each time you want to a retrieve a new document, do you need to search through all other documents? How do you group similar documents together? How do you discover new, emerging topics that the documents cover? In this third case study, finding similar documents, you will examine similarity-based algorithms for retrieval. In this course, you will also examine structured representations for describing the documents in the corpus, including clustering and mixed membership models, such as latent Dirichlet allocation (LDA). You will implement expectation maximization (EM) to learn the document clusterings, and see how to scale the methods using MapReduce. Learning Outcomes: By the end of this course, you will be able to: - Create a document retrieval system using k-nearest neighbors. - Identify various similarity metrics for text data. - Reduce computations in k-nearest neighbor search by using KD-trees. - Produce approximate nearest neighbors using locality sensitive hashing. - Compare and contrast supervised and unsupervised learning tasks. - Cluster documents by topic using k-means. - Describe how to parallelize k-means using MapReduce. - Examine probabilistic clustering approaches using mixtures models. - Fit a mixture of Gaussian model using expectation maximization (EM). - Perform mixed membership modeling using latent Dirichlet allocation (LDA). - Describe the steps of a Gibbs sampler and how to use its output to draw inferences. - Compare and contrast initialization techniques for non-convex optimization objectives. - Implement these techniques in Python. Machine Learning: Recommender Systems & Dimensionality Reduction Case Study: Recommending Products How does Amazon recommend products you might be interested in purchasing? How does Netflix decide which movies or TV shows you might want to watch? What if you are a new user, should Netflix just recommend the most popular movies? Who might you form a new link with on Facebook or LinkedIn? These questions are endemic to most service-based industries, and underlie the notion of collaborative filtering and the recommender systems deployed to solve these problems. In this fourth case study, you will explore these ideas in the context of recommending products based on customer reviews. In this course, you will explore dimensionality reduction techniques for modeling high-dimensional data. In the case of recommender systems, your data is represented as user-product relationships, with potentially millions of users and hundred of thousands of products. You will implement matrix factorization and latent factor models for the task of predicting new user-product relationships. You will also use side information about products and users to improve predictions. Learning Outcomes: By the end of this course, you will be able to: - Create a collaborative filtering system. - Reduce dimensionality of data using SVD, PCA, and random projections. - Perform matrix factorization using coordinate descent. - Deploy latent factor models as a recommender system. - Handle the cold start problem using side information. - Examine a product recommendation application. - Implement these techniques in Python. Machine Learning Capstone: An Intelligent Application with Deep Learning Have you ever wondered how a product recommender is built? How you can infer the underlying sentiment from reviews? How you can extract information from images to find visually-similar products to recommend? How you construct an application that does all of these things in real time, and provides a front-end user experience? That’s what you will build in this course! Using what you’ve learned about machine learning thus far, you will build a general product recommender system that does much more than just find similar products You will combine images of products with product descriptions and their reviews to create a truly innovative intelligent application. You’ve probably heard that Deep Learning is making news across the world as one of the most promising techniques in machine learning, especially for analyzing image data. With every industry dedicating resources to unlock the deep learning potential, to be competitive, you will want to use these models in tasks such as image tagging, object recognition, speech recognition, and text analysis. In this capstone, you will build deep learning models using neural networks, explore what they are, what they do, and how. To remove the barrier introduced by designing, training, and tuning networks, and to be able to achieve high performance with less labeled data, you will also build deep learning classifiers tailored to your specific task using pre-trained models, which we call deep features. As a core piece of this capstone project, you will implement a deep learning model for image-based product recommendation. You will then combine this visual model with text descriptions of products and information from reviews to build an exciting, end-to-end intelligent application that provides a novel product discovery experience. You will then deploy it as a service, which you can share with your friends and potential employers. Learning Outcomes: By the end of this capstone, you will be able to: - Explore a dataset of products, reviews and images. - Build a product recommender. - Describe how a neural network model is represented and how it encodes non-linear features. - Combine different types of layers and activation functions to obtain better performance. - Use pretrained models, such as deep features, for new classification tasks. - Describe how these models can be applied in computer vision, text analytics and speech recognition. - Use visual features to find the products your users want. - Incorporate review sentiment into the recommendation. - Build an end-to-end application. - Deploy it as a service. - Implement these techniques in Python. [-]

Information Security Management

Fanshawe College
Campus or Online Full time 1 year September 2017 Canada London

Information Security Management is a graduate program designed to provide students with a holistic approach to information systems security as it applies to business. [+]

Information Security Management Offered as an online distance education program and in a face-to-face classroom format * Secure your future in the fastest-growing sector! Information Security Management is a graduate program designed to provide students with a holistic approach to information systems security as it applies to business. The program will provide a broad understanding of security concepts and concentrate on industry best practices for information security. You will gain the knowledge required to manage both the strategic and operational aspects of information security, and develop the skills to identify vulnerabilities to business implementing effective safeguards to minimize risks to acceptable levels. During the year, you will learn detailed practical aspects of security such as risk analysis, vulnerability testing, writing security policies, implementing access controls, and performing security audits. Graduates would obtain positions such as security analysts, chief security officer, information security auditor, network security analyst, IT technical security specialist, and threat-risk/vulnerability security specialist. * Classes are delivered online and face-to-face on campus. Full-time or part-time students have the option to watch live and interactive webcasts or attend classes face-to-face on campus. All webcasts will be recorded and archived so you can review material and learn at your own pace. Students will require access to a computer and the Internet, preferably high-speed if attending online, and students will require a suitable laptop if attending face-to-face classes. Academic School: Information Technology Campus: London Campus (LC) Program Delivery: Day Time, Full Time, Online Program Type: Certificate, Graduate Available to International Students?: Yes Admission Requirements A Two- or Three-Year College Diploma, or a Degree OR Acceptable combination of related work experience and post-secondary education as judged by the College to be equivalent to the above English Language Requirements Applicants whose first language is not English will be required to demonstrate proficiency in English by one of the following methods: A Grade 12 College Stream or University Stream English credit from an Ontario Secondary School, or equivalent, depending on the program's Admission Requirements Test of English as a Foreign Language (TOEFL) test with a minimum score of 570 for the paper-based test (PBT), or 88 for the Internet-based test (iBT), with test results within the last two years International English Language Testing System (IELTS) test with an overall score of 6.5 with no score less than 6.0 in any of the four bands, with test results within the last two years Canadian Academic English Language (CAEL) test with an overall score of 70 with no score less than 60 in any of the four bands, with test results within the last two years An English Language Evaluation (ELE) at Fanshawe College with a minimum score of 75% in all sections of the test, with test results within the last two years Recommended Academic Preparation Previous business or IT post-secondary education and/or training and/or work experience. Applicant Selection Criteria Where the number of eligible applicants exceeds the available spaces in the program, the Applicant Selection Criteria will be: Preference for Permanent Residents of Ontario Receipt of Application by February 1st (After this date, Fanshawe College will consider applicants on a first-come, first-served basis until the program is full) Achievement in the Admission Requirements Other Information Classes are delivered online and face-to-face on campus. Full-time and part-time students have the option to watch live and interactive webcasts or attend classes face-to-face on campus for most courses. All webcasts will be recorded and archived so students can review material and learn at their own pace. Students will require access to a computer and the Internet, preferably high-speed if attending online, and students will require a suitable laptop as per specs if attending in face-to-face classes. Approximate Costs A laptop computer is to be purchased by the student attending the face-to-face classroom format. A laptop or computer with microphone, speaker and internet connection is required for the student attending online. The cost of the equipment is included in the General Expenses stated in the Fee Schedule. A CONNECT lab fee of $75.00 per academic term is included in the Additional Program Fees stated in the Fee Schedule. This fee helps cover costs associated with the delivery of the CONNECT mobile computing program. The CONNECT lab fee is OSAP eligible. Students should not purchase a laptop computer or software until the College publishes the recommended configuration, models, software titles and versions for that academic year. Career Opportunities This program will prepare graduates to take advantage of the growing opportunities in the information security field. Graduates will be an asset to any information technology support department or specifically as part of a security team, in virtually all sectors of business. Subsequently, with the right experience base, graduates can expect to find opportunities as security analysts and consultants, or in security management roles. They would obtain positions such as security analysts, information security auditor, network security analyst, IT technical security specialist, and threat-risk/vulnerability security specialist. [-]

Cambridge A-Level

Brickfields Asia College
Campus or Online Full time 11 - 18 months January 2017 Malaysia Kuala Lumpur Petaling Jaya + 1 more

Brickfields Asia College (BAC), a Cambridge International Associate Partner is proud to offer the CIE A-Level programme which would enable students to progress into degrees in Accounting, Business Management, Commerce, Economics, Finance, ITC, Communication, Law, Marketing and Mass Communication amongst others. [+]

Best Distance learning Course Studies in Engineering & Technology 2017. The University of Cambridge International Examinations (CIE) is the world’s largest provider of international qualifications and their A-Level programme is the most widely recognised Pre-University qualification in the world. Students in over 150 countries pursue CIE examinations each year to gain entry into prestigious universities worldwide, including those in the UK, USA, Canada, New Zealand and Australia. Brickfields Asia College (BAC), a Cambridge International Associate Partner is proud to offer the CIE A-Level programme which would enable students to progress into degrees in Accounting, Business Management, Commerce, Economics, Finance, ITC, Communication, Law, Marketing and Mass Communication amongst others. This last year 4 of our students achieved the honour of being "Top Student in the World"!! Students should consider whether the Standard or Express Route will suit them better. With the Express Route, students can complete their A-Levels in just 11 months, from January to November and are able to start their first year Degree in Business or Law in the January and actually begin their second year of their Degree Course in the September, saving a year on their overall studies - Graduate faster. We generally restrict students who take this option to 2 A-Levels, but this is the normal entrance level to our UK university partners where in most cases students in Law or Business study their first two years in Malaysia then go the the UK for their final year, unless they would prefer to study all three years in just one country. However for those wishing to take 3 or more subjects the Standard Route will be the best option and this takes around 18 months to complete, after which time students can decide whether to continue their studies with us or attend another university, either way we will be happy to advise you of the best option. [-]

Design Course Room Acoustics

laSalle Barcelona Postgraduate
Campus or Online Full time September 2017 Spain Barcelona

Spaces speak, respond, host the sounds that occur in them; the magnified and enhanced. The spaces scream, attack, amplify the noise, dirty word, create echoes and distorted messages or music. [+]

Spaces speak, respond, host the sounds that occur in them; the magnified and enhanced. The spaces scream, attack, amplify the noise, dirty word, create echoes and distorted messages or music. - How can we control the action of architecture, materials and shapes on the sounds of space? How good sound spaces designed? - How we solve the acoustic shocks caused by inadequate space? - How the acoustics of a room is measured? Control of reverb, intelligibility, noise control; in short, the acoustic quality of a room due to laws and few requirements that form the core of architectural acoustics. What do you prepare? The Acoustic Design course Salas prepares to offer an effective solution to issues such as: How can we control the action of architecture, materials and shapes on the sounds of space? How good sound spaces designed? How we solve the acoustic shocks caused by inadequate space? How the acoustics of a room is measured? To whom it May concern? The course of Acoustic Design Meeting It is aimed at engineers, architects, installers and other professional public branches. also aimed at those who want to know the laws governing the behavior of sound within an architectural space, to design the appropriate acoustical treatment to each specification, solve problems arising from poor design, learn to choose the right materials and know how to measure acoustic quality of a room. The program includes an intensive module Introduction to acoustics for those who do not have any training in this field. Admission One of the key development Program Masters in La Salle factors are the people who join the program: selected people responding to criteria curriculum, according to academic, functional, sectoral and geographical dimensions that foster the enrichment of the collective. The admission process in La Salle aims to select the most suitable candidates for each program and to ensure the level and quality of coexistence and communication between participants, which is a distinctive feature of the style of La Salle. The admission process for the academic year is now open and the candidates who begin the process as soon as possible because the admissions period remains open until all seats limit established for each academic program is recommended. Cost and Financing: Request information and send you a complete dossier with information on resources, banks and associates. Scholarships and grants: Request information and receive Programs Scholarships and Grants. [-]

Network and System Administration

Noroff Online Studies
Campus or Online Full time 1 year January 2017 Norway Kristiansand Oslo Bergen Sandnes + 3 more

Network and System Administration is a technical course focusing on Microsoft and Unix technologies, network infrastructure and server operations. This education is suited for those seeking many future opportunities in the IT sector... [+]

Best Distance learning Course Studies in Engineering & Technology 2017. Network and System Administration is a technical course focusing on Microsoft and Unix technologies, network infrastructure and server operations. This education is suited for those seeking many future opportunities in the IT sector. First semester Project Management Introduction to Information Security Operating and File Systems Microsoft based Technologies Second semester Unix based technologies Network infrastructure Study project Course information Next startup: 8. Mar. 2016 online, August 2016 on-campus. Campuses: Oslo, Bergen, Stavanger, Kristiansand and Online Studies Duration: 1 year Price online: NOK 33.000,- per semester Price on-campus: NOK 54.750,- per semester. NOK 1.500,- registration fee. Approvals: Approved for loans and grants from the State Educational Loan Fund. Approved by NOKUT. Fagskolepoeng: 60 ECVET Admission requirements Three-year upper secondary education or a vocational education certificate. [-]

Network and IT Security

Noroff Online Studies
Campus or Online Full time 1 year January 2017 Norway Kristiansand Oslo Bergen Sandnes + 3 more

Network and IT Security is a technical field of expertise focusing on system administration, programming, security and digital investigation. The course is suited for those looking for a solid IT education... [+]

Network and IT Security is a technical field of expertise focusing on system administration, programming, security and digital investigation. The course is suited for those looking for a solid IT education. Course information Network and IT Security is a two year Vocational course. A student who has completed Network and System Administration can enter the second year directly. Next startup: 8. Mar. 2016 online, August 2016 on-campus. Campuses: Oslo, Bergen, Stavanger, Kristiansand and Online Studies Duration: 2 years Price online: NOK 33.000,- per semester Price on-campus: NOK 54.750,- per semester. NOK 1.500,- registration fee. Approvals: Approved for loans and grants from the State Educational Loan Fund. Approved by NOKUT. Credits: 120 ECVET Admission requirements Three-year upper secondary education or a vocational education certificate. [-]

Advanced Web Design

National University, School of Business and Management
Campus or Online Part time September 2017 USA La Jolla Los Angeles Orange Camarillo San Bernardino South Bay Terraces Redding Henderson USA Online San Diego + 9 more

This course introduces current principles of Internet Application Development beyond visually appealing user-interfaces. [+]

Best Distance learning Course Studies in Engineering & Technology 2017. This course introduces current principles of Internet Application Development beyond visually appealing user-interfaces. Specific design concepts will be applied to an advanced web-design or web-conceptualization during a class project. The course focuses on the principles of HTML, JAVA and XML application programming. Practical exercises will be conducted throughout the course. [-]

Course in E-Business Information and Knowledge Systems

National University, School of Business and Management
Campus or Online Full time September 2017 USA La Jolla Los Angeles Orange Camarillo San Bernardino South Bay Terraces Redding Henderson USA Online San Diego + 9 more

The course introduces the data-information-knowledge-intelligence chain and its relevance to E-Business profitability and growth. [+]

The course introduces the data-information-knowledge-intelligence chain and its relevance to E-Business profitability and growth. It includes a study of the role and deployment of data models, database systems, data warehouses and business intelligence.

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