What is Azure?

Azure is launched by Microsoft in February 2010.

It is a cloud computing platform including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) and also these can be used for services such as analytics, virtual computing, storage, networking, etc.

The Azure cloud platform has more than 200 products and cloud services designed to help the users and to solve today’s challenges and create the future.

What is the use of Azure?

There are 10 uses of Azures –

• Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) capabilities:
• Security Offerings:
• Scalability and Ductility:
• Unmatched Hybrid Capabilities:
• Integrated Environment with Other Microsoft Tools:
• Analytics and Intelligence capabilities:
• Easy learning curve:
• Cost Efficient Platform:
• Interoperability:
• Identity & Access Management (IAM):

Compare Google Cloud Certification Vs Azure Certification Vs AWS

Amazon Web Services (AWS) –

AWS is the most widely used cloud platform, having a very vast experience with an expansive range of services tailored for different use cases. AWS is currently at the top, holding the world’s largest public cloud market share. It has been introduced in 2006 and was the first to offer infrastructure services through the Internet.

AWS incorporates many different cloud computing products and services comprising compute, storage, analytics, databases, networking, mobile, developer tools, management tools, and IoT.

Key Features of AWS

• Reliable, scalable, and low-cost infrastructure platform
• Over 200 fully-featured services
• A flexible and secure cloud computing environment
• 81 availability zones in 25 geographic regions across the world

Microsoft Azure–

Azure is the second-largest Cloud Service Provider after AWS. Microsoft Azure is Microsoft’s public cloud computing platform that was introduced in October 2008 and became available in public in February 2010. It is the biggest competitor of AWS. The platform provides a broad range of products and cloud services to allow businesses to find today’s challenges and create the future. Azure’s product portfolio comprises compute, AI & Machine Learning, analytics, databases, DevOps, IoT, networking, storage, web, and security.

Key Features of Azure

• More than 200 products and cloud services
• 90+ compliance offerings
• 95 percent of the Fortune 500 companies rely on Azure
• Datacentres in 60+ global regions

Google Cloud Platform–

GCP is new to cloud computing offered by Google. Many leading companies over industries keep faith on Google Cloud to solve their business problems. Some of its popular cloud services comprise computing, data analytics, data storage, networking, IoT, and machine learning.

Between all three cloud platforms, GCP provides the low-cost pricing model and has flexible cost-control, enable you to try the different services and features.

Key Features of GCP

• Open source, multi-cloud, and hybrid cloud
• Over 100 products
• A global network spanning across 200+ countries & territories, 85 zones
• Trust and security

Certification

The certification confirms your basic knowledge of cloud services and how those services are offered with Azure. Candidates should be competent to demonstrate a fundamental knowledge of cloud concepts, alongside with Azure services, workloads, security, privacy, pricing, and support.

A Microsoft azure certification allows you to chase a wide range of career options. You can become a cloud architect, a developer or solution architect. Additionally, the certification allows you to work in different industries at different locations.

List of Azure Certification

• Microsoft Certified: Azure – Fundamentals Exam AZ-900
• Microsoft Certified: Azure AI – Fundamentals Exam AI-900
• Microsoft Certified: Azure Data – Fundamentals Exam DP-900
• Microsoft Certified: Azure Administrator – Associate Exam AZ-104
• Microsoft Certified: Azure Developer – Associate Exam AZ-204
• Microsoft Certified: Azure Security Engineer – Associate Exam AZ-500
• Microsoft Certified: Azure AI Engineer – Associate Exam AI-100
• Microsoft Certified: Azure Data Scientist – Associate Exam DP-100
• Microsoft Certified: Azure Data Engineer – Associate Exams DP-200 and DP-201
• Microsoft Certified: Azure Database Administrator – Associate Exam DP-300
• Microsoft Certified Solutions Architect – Expert Exams AZ-303 and AZ-304
• Microsoft Certified: Azure DevOps Engineer – Expert Exam AZ-400

Azure Certification Path

Fundamentals-Level Certifications

• Microsoft Certified: Azure Fundamentals AZ-900
• Microsoft Certified: Azure Data Fundamentals DP-900
• Microsoft Certified: Azure AI Fundamentals AI-900
• Microsoft Security, Compliance, and Identity Fundamentals Azur SC-900

Associate-Level Certifications

• Microsoft Certified: Azure Administrator Associate AZ-104
• Microsoft Certified: Azure Developer Associate AZ-204
• Microsoft Certified: Azure Database Administrator Associate DP-300
• Microsoft Certified: Azure Security Engineer Associate AZ-500
• Microsoft Certified: Azure Data Engineer Associate DP-203
• Microsoft Certified: Azure AI Engineer Associate AI-102
• Microsoft Certified: Azure Stack Hub Operator Associate AZ-600

Expert-Level Certifications

• Microsoft Certified: Azure Solutions Architect Expert AZ-303
• Microsoft Certified: DevOps Engineer Expert AZ-400

Specialty Certifications

• Microsoft Certified: Azure IoT Developer Specialty AZ-220
• Microsoft Certified: Azure for SAP Workloads Specialty AZ-120

Azure Certification Cost

• Azure Fundamentals AZ-900 – ( $99 USD* )
• Azure Data Fundamentals DP-900 – ( $99 USD* )
• Azure AI Fundamentals AI-900 – ( $99 USD* )
• Microsoft Security, Compliance, and Identity Fundamentals Azur SC-900 – ( $99 USD* )
• Azure Administrator Associate AZ-104 – ( $165 USD* )
• Azure Developer Associate AZ-204 – ( $165 USD* )
• Azure Database Administrator Associate DP-300 – ( $165 USD* )
• Azure Security Engineer Associate AZ-500 – ( USD 165.00* )
• Azure Data Engineer Associate DP-203 – ( USD 165* )
• Azure AI Engineer Associate AI-102 – ( $165 USD* )
• Azure Stack Hub Operator Associate AZ-600 – ( $165 USD* )
• Azure Solutions Architect Expert AZ-303 ( USD 165.00* )
• DevOps Engineer Expert AZ-400 ( USD 165.00* )
• Azure IoT Developer Specialty AZ-220 ( USD 165.00* )
• Azure for SAP Workloads Specialty AZ-120 ( USD 165.00* )

Best salary for Azure Certified Professional

The average salary of Azure Solutions Architects in the U.S. $152,142 and Rs 1,575,000 in India.
The Azure Fundamentals certification average salary is $126,653 and Rs. 9,00,000 in India.
Azure Administrator Associate average salary is $125,993 and 3.0 Lakhs to Rs 16.9 Lakhs in India.

Best Azure Certification Tutorials

https://www.tutorialspoint.com/microsoft_azure/cloud_computing_overview.htm

Best Azure Certification Video Tutorials

Best Azure certification excercise dumps

https://www.examtopics.com/exams/microsoft/az-900/view/

https://www.prepaway.com/az-104-exam.html

Best Azure certification Ebooks

https://azure.microsoft.com/en-in/blog/free-ebook-the-developer-s-guide-to-microsoft-azure-now-available/

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Nexus is a repository manager functions as stores, organizes, and distributes Artifacts needed for the development.

This tool is one of the finest ways to continuously control binary, packages and build Artifacts. But this repository is not something like a Git repository where you store the source code, this repository is basically for packages, binaries that you build, like apt packages, yum packages, android packages, NPM packages, Docker packages, etc.

There are two types of version one is the community version and 2^nd is the pro version which you called a professional version. The professional version is for paid and the enterprise community version is free with very limited functions.

From Nexus, developers can fully control access to, and deployment of, every Artifact in a company from a single location, making it easier to allocate software.  Internally, you configure your build to issue Artifacts to Nexus, so they come out there to rest developers. Nexus Repository helps organizations build better, safer software even quicker for free. It’s the only repository manager with free support for all popular formats, like Java, Docker, JavaScript, Ruby, Python, NET, GitLFS, etc.

Why Nexus is used for?

Basically, it is repository management and is used to store any kind of packages.  But there is another use of nexus as well, which is the availability of packages.

When you store the packages in the tools like nexus, they will be available to the entire team. Because of this coordination time is reduced.

Now the security of the packages is very important here. Security of packages means only a few persons should be having the right access or few CI tools. Most of the times CI tools will have the right access to make the nexus. But the rest of the people will not have the right access because the security of the package is also a major concern.

Now if you look at the kind of places where we deploy our applications, most of the applications we are using everywhere are cloud-based applications like making my trip, yatra.com, Swiggy, Ola, FB, etc, software as a service, so security is very important.

If one wrong package gets deployed into the system, operations, production, which will create a lot of problems so not only the support security and runtime security is important but package security is also important.  So that’s one of the reasons where we can use nexus where the security can be enforced.

Storage over the period of time will die but if you store the packages in tools like nexus and Artifactory, etc. they will make sure that the packages they store in a sort of object format, they maintain the integrity of packages, so that way it will not get corrupted over the period of time, so that is a thing which will be taking care by tools is nexus. 

What are the benefits of nexus?

Given below are few points on the benefits of nexus are–

• Speedier Builds

• Your own bandwidth savings

• Predictability and Stability

• Ability to deploy 3rd-party Artifacts

• Control and Auditing

• Ability to Host Internal Repositories

• Ability to Host Public Repositories
• Easy administration and management

Who should enroll for Nexus Certification Course?

The person who wants to be a professional on nexus tool and wants to learn what and how repository manager works as nexus and to take the advantage of nexus advanced features who let a user search, use fields, use lookups, and create basic statistical reports and dashboards in the Nexus Intermediate Enterprise or Nexus Intermediate Cloud Platforms.

 It enhances the value of your resume and you can get leading job posts with the help of this certification in leading MNC’s of the world. The certification is only provided after the successful completion of training and practical-based projects.

What are the alternatives of Nexus?

Given below are the few tools which is an alternative to Nexus.

• Archiva
• Nuget gallery
• PopScrap Pro
• ScrapRight
• ScrapYardPro
• RecyclerGuard
• RazorERP
• RecycleManager
• Makor ERP
• Waste Logics
• Waste & Recycling One
• JFrog Artifactory.
• CircleCI.
• Postman.
• GitHub.
• Amazon Elastic Container Registry (ECR)
• Jenkins.
• CloudBees CI.
• Github Package Registry.

What are the pre-requisites to learn Nexus

      For essential nexus training

• Basic understanding of Linux/Unix system concepts
• Familiarity with Command Line Interface (CLI)
• Familiarity with a Text Editor
• Experience with managing systems/applications/infrastructure or with deployments/automation

For intermediate nexus training

• An understanding of basic marketing concepts.
• Should have a keen interest in engaging in social media constructively.
• Enthusiasm and commanding communication skills

For advanced nexus training

• Basic knowledge of Elastic search/Nexus and would like to expand on their knowledge
• Basic Knowledge of JSON
• Basic of Linux and windows

How to learn Nexus?

Through a certified course and training from well-trained trainers and from a popular and well-known platform like DevOpsSchool which makes the trainee is a perfect use of this Nexus tool. 

DevOpsSchool is the best supply of Nexus courses, training, and certification. Their Nexus trainers and specialists are highly educated with a good rich experience in the Software industry. DevOps has captured a stimulating position within the IT world and has become an important part of software engineering methodology. For enhancing the software quality, reducing cost, and increasing the agility for a quicker release, DevOps and Nexus is playing a very important role therefore finding the best Nexus trainers and training seller is very necessary.

The Nexus courses are designed in certain methods where participants can get advantages with a lot of content in less span of time. DevOpsSchool gives its services of Nexus training and courses online and classroom mode in India and abroad. DevOpsSchool tends to run several public classrooms training programs within Bangalore, Hyderabad, Pune, Mumbai, India, Netherlands, etc.

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In the world of big data, the demand for highly skilled data scientists has never been higher. But the competition for employment within this specialized field of IT is also fierce. That means that data science certification is essential if you aim to develop your career and land one of the top jobs.

In this article, we’ll take a look at the field of data science before then looking at your options for gaining data science certification while being based in India.

What is Data Science?

Historically, the focus has been on effective methods of capturing and then storing large volumes of data. But with effective technology now in place for both of these processes, there are growing demands to be able to analyze the information. 

Data science utilizes a range of tools, algorithms, and machine learning strategies to identify patterns within the raw data.  However, it isn’t just about looking to what’s already happened; the data scientist uses their results to make decisions and predictions as to future events. To do this, three fundamental approaches are used:

1. Predictive Causal Analytics

Being able to predict the likelihood of future events is incredibly important in a range of business sectors. This is where predictive casual analytics come into play to reduce risk factors. If, for example, you run a subscription-based service and you wanted to be able to model the likelihood of clients continuing their subscription payments over a period of time, then you would need to utilize predictive casual analytics to be able to make a judgment for this taking place.

2. Prescriptive Analytics   

This relatively new field within data science develops a model to make decisions and modify dynamic parameters. Applying prescriptive analytics means that the client is provided with predictions and has suggestions for actions to be taken and their associated outcomes.

A hospital is one setting in which prescriptive analytics can make a significant difference in ensuring the most effective use of funds. Analyzing patient data can provide insights into the number of staff needed on shift, the demands on diagnostic equipment, and the volume of medical supplies that will be required.

3. Machine Learning.

There are two aspects of machine learning: firstly, making predictions and then completing pattern discovery. Predictions requires the data to already be available, which will then be used to train the machines. On the other hand, pattern discovery is used in situations where the parameters to make the predictions are not available. This then means that hidden patterns within the dataset need to be identified to then make meaningful predictions.  

What To Look For in a Data Science Certification Course

When you’re committing both your time and money to your education, the course you choose must deliver results. Now while the effort and time you put in will have a significant impact on the likelihood of you achieving certification, you also need to ensure the course provides the following:

World-Class Faculty

Do you know who will be teaching? Do they have relevant and recent industry experience, or are they merely regurgitating information from a textbook? This is an industry that is going through rapid change; it sees new innovations and methodology on what seems to be a daily basis. That means that it’s essential that the faculty are teaching industry best practices.

Delivery Methods

Sometimes it’s just not possible to relocate to another part of India to take a course. Family commitments, funding, travel restrictions can all cause significant problems to the traditional classroom style of learning. So, that means that it’s essential that your data science certification provider can offer a range of delivery methods.  

Online learning has become an accepted way to study and even more so since the Covid-19 pandemic. Ensuring that your provider can offer the right method of delivery for you means that your studies need not be interrupted no matter what else is going on in the world.

Faculty: Student Ratio

When you’re one of many, it can prove challenging to get the individual feedback you need to progress. Does your lecturer know who you are? Do they know your aspirations, the challenges you face, and the support you need? That’s why it’s essential to ask the provider what their faculty: student ratio is. If it’s much over 1:10, then it’s unlikely that you’re going to get the personalized attention you need to be successful.

Program Curriculum

Will you get the opportunity to focus on the topics which are of most interest to you? The ones which are most relevant to gain entry to the sector of your choice? While all courses will have core modules that everyone is required to take, you should also check whether there is an opportunity for electives. These might be in specialized areas such as data mining, robotics, or deep learning applications. Having that knowledge in a niche field of data science could be the key to launching your career in data science.

Application of Knowledge

It’s one thing to be able to discuss techniques, but it’s a whole other thing to be able to effectively apply them to real-life scenarios. Your data science course should have a strong emphasis on the application of your newly developed skillset.

So, you need to look for courses that include activities such as boot camps or hackathons. In these sessions, you’ll have the opportunity to gain hands-on experience, develop your critical thinking abilities and work within a team, all while being up against strict deadlines. After all, they’re precisely the skills that your new employer will want to see.

Next Steps in Achieving Your Data Science Certification

If you’re sure that this is the right career direction for you, then the next step is to begin looking for providers who can offer opportunities for candidates based within India. This may sound straightforward, but every year students find themselves on courses that are just not a great match for their needs.

Perhaps the course doesn’t have collaborations with universities from across the world, limiting opportunities for future study. Or maybe the certification lacks recognition or standing within the industry. The time you spend now on researching courses and finding the right one for you is an essential investment in the success of your career within data science.

The post How To Get Your Data Science Certification In India. appeared first on Artificial Intelligence.

IIT Mandi and Wiley are currently accepting applications for an online postgraduate certification in applied artificial intelligence and machine learning

IIT Mandi and Wiley are currently offering a six-month online postgraduate certification in applied artificial intelligence and machine learning. The course has been designed to give participants an understanding of developing skills in building applications with deep learning, computer vision, natural language processing, image and speech processing that are required for specialist artificial intelligence job roles.

The course will be conducted online, and participants will receive a certificate of completion from IIT Mandi and Wiley upon successfully finishing the program. The last date to submit applications for the course is 20 February 2021, and the program will cost learners Rs 1,00,000 plus GST. Once the applicants’ candidature is approved by IIT Mandi, the course will begin on 27 February 2021.

Who can Apply for IIT Mandi and Wiley Postgraduate Certification Program

The official website says that the course can be beneficial for the following participants:

• Applicants who have completed their BE, BTech, MTech, BCA, or MCAs and want to build a high-impact career in artificial intelligence.
• Existing professionals in any technology, cloud, software development or programming who hope to have a high growth artificial intelligence career in deep learning, NLP, speech processing and computer vision.
• Existing professionals in data engineering, data analysts, statisticians who want to acquire artificial intelligence and machine learning skills.

What the Course Curriculum Consists Of

The course, which will be conducted by IIT Mandi professors, will cover the following aspects:

• Python refresher.
• Building predictive models (ML).
• Data mining and recommender systems.
• Deep learning for artificial intelligence.
• Artificial intelligence for NLP.
• Artificial intelligence for image processing.
• Deep Learning for artificial intelligence.
• Speech recognition and conversational artificial intelligence.
• Computer vision.
• Applications of artificial intelligence in the industry.
• Designing and deploying an artificial intelligence solution.

Those who wish to know more about the course are advised to go through the official website for more information.

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Cloud Ace, Datatonic, Deloitte Consulting, Devoteam, Pandera Systems, Pythian Services and Quantiphi are among the 50-plus Google Cloud partners with employees who’ve already earned the cloud provider’s new Professional Machine Learning Engineer certification.

The certification, unveiled last week, validates cloud professionals’ expertise in designing, building and productionizing machine-learning (ML) models to solve business challenges using Google Cloud technologies, along with their knowledge of proven ML models and techniques.

Finding employees with the right ML skills has been among the top challenges for IT leaders this year, Google Cloud said.

The pre-pandemic tech talent shortage challenged many organizations’ digital transformations, and they’re now playing catch-up, according to a September report from staffing company Robert Half International that highlights IT industry trends and starting salaries that its recruiters expect to see next year.

“They are faced with the need to accelerate their transformation process as well as address technical debt within their organization,” the report said. “Many are seeking technology professionals with expertise in AI and machine learning, cloud computing and robotic process automation.”

Artificial intelligence (AI)/ML specialists are expected to have among the highest median starting salaries for non-executive IT jobs in the U.S. next year, according to the report.

When Ottawa’s Pythian Services heard earlier this year there potentially would be a new ML-related certification coming from Google Cloud, it put its ML team on notice and had them stand by to get it done, according to Vanessa Simmons, vice president of business development for the cloud, data and analytics services company, a Google Cloud Premier Partner.

Pythian currently has two Google Cloud-certified Professional Machine Learning Engineers, with the goal of having its entire team certified. Carlos Timoteo, a Pythian data scientist and machine- learning engineer, has been working with Google Cloud, applying AI Platform, BigQuery and big data services to implement ML solutions for the company’s customers.

Timoteo took his first Google Cloud certification exam, the Google Cloud Professional Data Engineer, in 2017. Since then, he and his work colleagues and fellow data scientists in his circle have been waiting for a well-designed data scientist/ML certification, he said.

“The preparation was not too hard or long given my experience as a data scientist leveraging Google Cloud,” Timoteo said. “I used the provided preparation guide to identify the points I needed to study more to ace the exam, leveraging Google Cloud documentation, the Google Developer Machine Learning Crash Course and a couple books.”

The Google Cloud Professional Machine Learning Engineer certification exam has a big emphasis on engineering ML solutions, according to Timoteo. The data science portion of the exam is more focused on the technique than on the algorithm details, implementation and limitation, he said. Beginners should expect snippets of code in Python and SQL and should learn TensorFlow 2.x and its ecosystem and how to implement TensorFlow 2 on Google Cloud Platform (GCP) in production, he noted.

“The exam is a valuable tool in assessing if the exam taker is able to propose a solution that satisfies many requirements recurrent for a variety of solutions, in several industry verticals,” Timoteo said. “With this added experience, Google and our customers can trust in my team and myself to build the most elegant and suitable solution to satisfy their business demands leveraging Google products and best practices in the market.”

London-based Datatonic, Google Cloud’s 2019 Specialization Partner of the Year for AI and ML, has two employees who earned the new certification.

“The Google Cloud Professional Machine Learning Engineer certificate gives a valuable overview of production ML on Google Cloud Platform, particularly on designing solutions with ML best practices in mind, such as mitigating model bias and utilizing GCP tools to interpret model predictions,” said Julian West, a Datatonic data scientist. “This will be actionable for future projects with clients increasingly conscious of model bias and explainability in their ML projects.”

Three Deloitte Consulting Google Cloud practitioners have earned the new certification.

“Deloitte teamed with Google early on with unwavering commitment to the certification program given their market leadership in AI/ML,” said Tom Galizia, lead commercial partner for Deloitte Consulting‘s Alphabet/Google alliance. “[Google CEO Sundar Pichai] has stated Alphabet overall is an AI-first company, which is clearly reinforced with their broad and deep portfolio of AI/ML/analytics-based technologies at unprecedented cost curves and commitment to the democratization of AI/ML.”

The Google Cloud Professional Machine Learning Engineer certification requires a two-hour exam. The cloud provider recommends candidates have at least three years of industry experience, including one or more years designing and managing solutions using GCP. An ML engineer is proficient in model architecture, data pipeline interaction and metrics interpretation, and requires familiarity with application development, infrastructure management, data engineering and security, according to Google Cloud.

The certification exam evaluates candidates’ abilities to frame ML problems, develop ML models and architect ML solutions. It also assesses their abilities to automate and orchestrate ML pipelines, prepare and process data, and monitor, optimize and maintain ML solutions.

Google Cloud partners also can earn Expertises in AI and ML, including in Google Cloud AI and ML APIs, Contact Center AI, Document AI and Visual Intelligence. Quantiphi, SoftServe and SpringML are among the more than 90 partners with those Expertises.

Partners who earn a Google Cloud Specialization in Machine Learning signal the strongest level of Google Cloud ML proficiency and experience. Accenture, Atos, Deloitte Consulting, DoiT International, Quanitphi and Pythian have achieved those designations.

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You don’t need to work in the marketing department of Facebook or Google to understand the importance of large-scale data analytics when it comes to driving the modern economy. As the primary force behind everything from targeted advertising campaigns to self-driving cars, data analysis stands at the heart of today’s most important and exciting technologies and innovations.

The Deep Learning & Data Analysis Certification Bundle will help you take your analytical skills to the next level so you can land the best and most lucrative positions in your field, and it’s available today for over 95% off at just $39.99.

With eight courses and 30 hours of instruction led by the renowned data scientist Minerva Singh, this bundle will get you up to speed with the latest platforms and methodologies in the interconnected worlds of data analysis, visualization, statistics, deep learning, and more.

Through easy-to-follow lessons that utilize real-world examples, the training courses will walk you through the fundamentals and more advanced elements of YouTube analytics and Google Ads, R programming in the context of machine learning, algorithms that can help you break down data frameworks, statistical models that will allow you to predict future trends, and more.

This training bundle also takes a deep dive into the emerging worlds of artificial neural networks and deep learning platforms, which are being used by major tech companies in order to develop some of the world’s most powerful computing frameworks and technologies.

Land your ideal job in an increasingly data-driven world with help from the Deep Learning & Data Analysis Certification Bundle while it’s on sale for just $39.99—over 95% off its usual price.

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While deep learning is viewed as a small part of the field of artificial intelligence, it’s now a field that is by all accounts growing out of the AI space itself. Deep learning is the advancement of ‘thinking’ computer systems, called neural networks, and using it requires coding procedures unfamiliar to old-school developers. With the assistance of deep learning, we can show our computers to learn for themselves such that it gives us noteworthy outcomes. Furthermore, you get the opportunity to be at the front line, as experts in profound learning are required now like never before.

If you want to learn deep learning and don’t know where to start, we’ve compiled a list of free online courses that can help you learn deep learning.

Creative Applications of Deep Learning with TensorFlow

This course covers the essential segments of deep learning. What it implies, how it works, and creating necessary code to build different algorithms, for example, deep convolutional networks, variational autoencoders, generative adversarial networks, and recurrent neural networks. A significant offering of this course will be to not just see how to build the fundamental segments of these algorithms, yet in addition how to apply them for exploring creative applications. Free and paid choices are available.

Best Deep Learning Course (deepLearning.ai)

This is without a doubt one of the best deep learning affirmations with Andrew Ng himself teaching the subject. The Co-Founder of Global Learning Platform Coursera, Andrew has been the head of Google Brain and Baidu AI group before. Going along with him are teachers from leading institutes like Stanford. In this certification course, you will find out about the foundations of Deep Learning, realize how to build neural networks and understand about machine learning ventures. There will be real time case studies including sign language reading, music generation and natural language processing among others. Alongside all the theory, you will be educated to implement these ideas in Python and TensorFlow.

Deep Learning for Business by Yonsei University

Offered by Yonsei University, Deep Learning for Business shows the nuts and bolts of deep learning and how to implement it in your organization to accelerate outcomes. You will learn how to make business strategies that encourage technical planning on new machine learning and deep learning products.

The course starts with an overview of deep learning products and services, trailed by a module on business with deep learning and machine learning. It additionally covers deep learning Computing Systems and Software, deep learning Neural Networks and deep learning with CNN and RNN. The course finishes with a module on TensorFlow Playgrounds. There’s no expense to learn and you can finish the course in 8 hours.

Deep Learning Certification by IBM (edX)

All through this expert certificate program, you will learn and gain pro Deep Learning abilities through a series of hands-on tasks and projects. Accessible on famous elearning stage edX, the course will come full circle into a Deep Learning capstone project that will help you grandstand your applied abilities to prospective employers. In addition to other things, you will learn central ideas of Deep Learning, including different Neural Networks for both supervised and unsupervised learning. You will likewise figure out how to build and send various types of Deep Architectures including Convolutional Networks, Recurrent Networks just as Autoencoders.

Introduction to Deep Learning by the National Research University Higher School of Economics

Prologue to Deep Learning is the initial segment of the Advanced Machine Learning Specialization from the National Research University Higher School of Economics. It’s intended to assist you with understanding the basics of current neural networks and their application in computer vision and natural language.

The class incorporates video talks, readings and tests. It comprises of 5 modules:

• Introduction to optimization
• Deep learning for images
• Introduction to neural networks
• Deep learning for sequences
• Unsupervised representation learning

You will likewise be entrusted with finishing a task to showcase what you learned in the course.

Complete Guide to TensorFlow for Deep Learning Training with Python (Udemy)

Jose Marcial Portilla has a MS from Santa Clara University and has been teaching Data Science and programming for various years now. His Tensorflow Certification will assist you with figuring out how to utilize Google’s Deep Learning Framework – TensorFlow with Python. He will likewise show you how you can utilize TensorFlow for Image Classification with Convolutional Neural Networks, how to do time series analysis with Recurrent Neural Networks and instruct you to take care of unsupervised learning problems with AutoEncoders. This training has been visited by nearly 20,000 students and has exceptional reviews and ratings.

Applied AI with Deep Learning

Applied AI with Deep Learning is the third course in Advanced Data Science with IBM Specialization. Data Scientist Romeo Kienzler imparts his experience in the field to offer essential insights into deep learning. He covers a few core subjects, including deep learning frameworks, applications, scaling and deployment.

Past the video lectures used to present the material, you’ll be needed to finish readings and tests. The readings supplement the guidance given in the exercises and tests help recognize areas where you may require more work.

Register for free, and you should expect to go through 18 hours working through the course material.

Deep Learning – Google via Udacity

In this course, you’ll build up a clear understanding of profound learning, and build intelligent systems that learn from complex and additionally huge datasets. You will figure out how to tackle new classes of issues that were once thought restrictively challenging, and come to better appreciate the complex nature of human insight as you take care of these equivalent issues easily utilizing deep learning techniques.

Deep Learning Nano Degree Program (Udacity)

People who need to study how to build and apply their own deep neural networks to different issues like image classification and generation, time-series prediction, and model deployment can take help from this nano degree program. This program is exceptionally made for students who are keen on making a profession in machine learning, artificial intelligence, or deep learning. Taking a crack at this program will acquaint you with Deep Learning algorithms, neural networks, and deploying a sentiment analysis model. In the wake of completing the program with given tasks and projects, you will get a certificate of completion that can be shared with your resume and employers.

Natural Language Processing with Deep Learning – Stanford University

The course gives a careful prologue to forefront research in deep learning applied to NLP. On the model side we will cover representations, window-based neural networks, recurrent neural networks, long-short-term-memory models, recursive neural networks, convolutional neural networks as well as some ongoing models including a memory part. Through video lectures and programming tasks students will gain proficiency with the essential designing stunts for making networks work on practical problems

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Vendors of artificial intelligence technology should not be shielded by intellectual property claims and will have to disclose elements of their designs and be able to explain how their offering works in order to establish accountability, according to a leading official from the Cybersecurity and Infrastructure Security Agency.

“I don’t know how you can have a black-box algorithm that’s proprietary and then be able to deploy it and be able to go off and explain what’s going on,” said Martin Stanley, a senior technical advisor who leads the development of CISA’s artificial intelligence strategy. “I think those things are going to have to be made available through some kind of scrutiny and certification around them so that those integrating them into other systems are going to be able to account for what’s happening.”

Stanley was among the speakers on a recent Nextgov and Defense One panel where government officials, including a member of the National Security Commission on Artificial Intelligence, shared some of the ways they are trying to balance reaping the benefits of artificial intelligence with risks the technology poses. 

Experts often discuss the rewards of programming machines to do tasks humans would otherwise have to labor on—for both offensive and defensive cybersecurity maneuvers—but the algorithms behind such systems and the data used to train them into taking such actions are also vulnerable to attack. And the question of accountability applies to users and developers of the technology.

Artificial intelligence systems are code that humans write, but they exercise their abilities and become stronger and more efficient using data that is fed to them. If the data is manipulated, or “poisoned,” the outcomes can be disastrous. 

Changes to the data could be things that humans wouldn’t necessarily recognize, but that computers do.

“We’ve seen … trivial alterations that can throw off some of those results, just by changing a few pixels in an image in a way that a person might not even be able to tell,” said Josephine Wolff, a Tufts University cybersecurity professor who was also on the panel. 

And while it’s true that behind every AI algorithm is a human coder, the designs are becoming so complex, that “you’re looking at automated decision-making where the people who have designed the system are not actually fully in control of what the decisions will be,” Wolff says.   

This makes for a threat vector where vulnerabilities are harder to detect until it’s too late.

“With AI, there’s much more potential for vulnerabilities to stay covert than with other threat vectors,” Wolff said. “As models become increasingly complex it can take longer to realize that something is wrong before there’s a dramatic outcome.”

For this reason, Stanley said an overarching factor CISA uses to help determine what use cases AI gets applied to within the agency, is to assess the extent to which they offer high benefits and low regrets. 

“We pick ones that are understandable and have low complexity,” he said.

Among other things federal personnel need to be mindful of is who has access to the training data. 

“You can imagine you get an award done, and everyone knows how hard that is from the beginning, and then the first thing that the vendor says is ‘OK, send us all your data,’ how’s that going to work so we can train the algorithm?” he said. “Those are the kinds of concerns that we have to be able to address.” 

“We’re going to have to continuously demonstrate that we are using the data for the purpose that it was intended,” he said, adding, “There’s some basic science that speaks to how you interact with algorithms and what kind of access you can have to the training data. Those kinds of things really need to be understood by the people who are deploying them.”

A crucial but very difficult element to establish is liability. Wolff said ideally, liability would be connected to a potential certification program where an entity audits artificial intelligence systems for factors like transparency and explainability. 

That’s important, she said, for answering “the question of how can we incentivize companies developing these algorithms to feel really heavily the weight of getting them right and be sure to do their own due diligence knowing that there are serious penalties for failing to secure them effectively.”

But this is hard, even in the world of software development more broadly. 

“Making the connection is still very unresolved. We’re still in the very early stages of determining what would a certification process look like, who would be in charge of issuing it, what kind of legal protection or immunity might you get if you went through it,” she said. “Software developers and companies have been working for a very long time, especially in the U.S., under the assumption that they can’t be held legally liable for vulnerabilities in their code, and when we start talking about liability in the machine learning and AI context, we have to recognize that that’s part of what we’re grappling with, an industry that for a very long time has had very strong protections from any liability.”

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