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This course is intended to be an introduction to machine learning for non-technical business professionals. There is a lot of hype around machine learning and many people are concerned that in order to use machine learning in business, you need to have a technical background. For reasons that are covered in this course, that’s not the case. In actuality, your knowledge of your business is far more important than your ability to build an ML model from scratch.
By the end of this course, you will have learned how to: • Formulate machine learning solutions to real-world problems • Identify whether the data you have is sufficient for ML • Carry a project through various ML phases including training, evaluation, and deployment • Perform AI responsibly and avoid reinforcing existing bias • Discover ML use cases • Be successful at ML
Introduction to Cloud Identity serves as the starting place for any new Cloud Identity, Identity/Access Management/Mobile Device Management admins as they begin their journey of managing and establishing security and access management best practices for their organization. This 15-30 hour accelerated, one-week course will leave you feeling confident to utilize the basic functions of the Admin Console to manage users, control access to services, configure common security settings, and much more.
Through a series of introductory lessons, step-by-step hands-on exercises, Google knowledge resources, and knowledge checks, learners can expect to leave this training with all of the skills they need to get started as new Cloud Identity Administrators. Learning Objectives By the end of this course participants will be able to: • Establish a Cloud Identity domain for their organization or personal domain. • Add users in order to practice user lifecycle management. • Modify user permissions to gain an understanding of core Cloud Identity features. • Add mobile devices within the Google Mobile Management module. • Modify mobile management policy sets to gain familiarity with product options. • Navigate the Reports module and practice running reports. • Explore and apply different security protocols to the domain.
Machine learning (ML) is one of the fastest growing areas in technology and a highly sought after skillset in today’s job market. The World Economic Forum states the growth of artificial intelligence (AI) could create 58 million net new jobs in the next few years, yet it’s estimated that currently there are 300,000 AI engineers worldwide, but millions are needed. This means there is a unique and immediate opportunity for you to get started with learning the essential ML concepts that are used to build AI applications – no matter what your skill levels are. Learning the foundations of ML now, will help you keep pace with this growth, expand your skills and even help advance your career.
This course will teach you how to get started with AWS Machine Learning. Key topics include: Machine Learning on AWS, Computer Vision on AWS, and Natural Language Processing (NLP) on AWS. Each topic consists of several modules deep-diving into variety of ML concepts, AWS services as well as insights from experts to put the concepts into practice.
This course introduces the concepts of Artificial Intelligence and Machine learning. We’ll discuss machine learning types and tasks, and machine learning algorithms. You’ll explore Python as a popular programming language for machine learning solutions, including using some scientific ecosystem packages which will help you implement machine learning.
Next, this course introduces the machine learning tools available in Microsoft Azure. We’ll review standardized approaches to data analytics and you’ll receive specific guidance on Microsoft’s Team Data Science Approach. As you go through the course, we’ll introduce you to Microsoft’s pre-trained and managed machine learning offered as REST API’s in their suite of cognitive services. We’ll implement solutions using the computer vision API and the facial recognition API, and we’ll do sentiment analysis by calling the natural language service.
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Learning Objectives This course teaches participants the following skills: ● Identify GCP counterparts for Amazon VPC, subnets, routes, NACLs, IGW, Amazon EC2, Amazon EBS, auto-scaling, Elastic Load Balancing, Amazon S3, Amazon Glacier, Amazon RDS, Amazon Redshift, AWS IAM, and more ● Configure accounts, billing, projects, networks, subnets, firewalls, VMs, disks, auto scaling, load balancing, storage, databases, IAM, and more ● Manage and monitor applications ● Explain feature and pricing model differences ● Locate documentation and training.
This accelerated 6-hour course with labs introduces AWS professionals to the core capabilities of Google Cloud Platform (GCP) in the four technology pillars: networking, compute, storage, and database. It is designed for AWS Solution Architects and SysOps Administrators familiar with AWS features and setup and want to gain experience configuring GCP products immediately. With presentations, demos, and hands-on labs, participants get details of similarities, differences, and initial how-tos quickly. If you prefer to learn fast by doing, this course is for you.
This course is for experienced C programmers who want to program in C++. The examples and exercises require a basic understanding of algorithms and object-oriented software.
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This course provides a deep dive into how to create a chatbot using Dialogflow, augment it with Cloud Natural Language API, and operationalize it using Google Cloud tools.
This course covers AWS services and frameworks including Amazon Rekognition, Amazon SageMaker, Amazon SageMaker GroundTruth, and Amazon SageMaker Neo, AWS Deep Learning AMIs via Amazon EC2, AWS Deep Learning Containers, and Apache MXNet on AWS. The course is comprised of video lectures, hands-on exercise guides, demonstrations, and quizzes.
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