60 Ecosystem courses in Bradford delivered Live Online

Duration 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. Python for Data Science ? Using Modules ? Listing Methods in a Module ? Creating Your Own Modules ? List Comprehension ? Dictionary Comprehension ? String Comprehension ? Python 2 vs Python 3 ? Sets (Python 3+) ? Python Idioms ? Python Data Science ?Ecosystem? ? NumPy ? NumPy Arrays ? NumPy Idioms ? pandas ? Data Wrangling with pandas' DataFrame ? SciPy ? Scikit-learn ? SciPy or scikit-learn? ? Matplotlib ? Python vs R ? Python on Apache Spark ? Python Dev Tools and REPLs ? Anaconda ? IPython ? Visual Studio Code ? Jupyter ? Jupyter Basic Commands ? Summary Applied Data Science ? What is Data Science? ? Data Science Ecosystem ? Data Mining vs. Data Science ? Business Analytics vs. Data Science ? Data Science, Machine Learning, AI? ? Who is a Data Scientist? ? Data Science Skill Sets Venn Diagram ? Data Scientists at Work ? Examples of Data Science Projects ? An Example of a Data Product ? Applied Data Science at Google ? Data Science Gotchas ? Summary Data Analytics Life-cycle Phases ? Big Data Analytics Pipeline ? Data Discovery Phase ? Data Harvesting Phase ? Data Priming Phase ? Data Logistics and Data Governance ? Exploratory Data Analysis ? Model Planning Phase ? Model Building Phase ? Communicating the Results ? Production Roll-out ? Summary Repairing and Normalizing Data ? Repairing and Normalizing Data ? Dealing with the Missing Data ? Sample Data Set ? Getting Info on Null Data ? Dropping a Column ? Interpolating Missing Data in pandas ? Replacing the Missing Values with the Mean Value ? Scaling (Normalizing) the Data ? Data Preprocessing with scikit-learn ? Scaling with the scale() Function ? The MinMaxScaler Object ? Summary Descriptive Statistics Computing Features in Python ? Descriptive Statistics ? Non-uniformity of a Probability Distribution ? Using NumPy for Calculating Descriptive Statistics Measures ? Finding Min and Max in NumPy ? Using pandas for Calculating Descriptive Statistics Measures ? Correlation ? Regression and Correlation ? Covariance ? Getting Pairwise Correlation and Covariance Measures ? Finding Min and Max in pandas DataFrame ? Summary Data Aggregation and Grouping ? Data Aggregation and Grouping ? Sample Data Set ? The pandas.core.groupby.SeriesGroupBy Object ? Grouping by Two or More Columns ? Emulating the SQL's WHERE Clause ? The Pivot Tables ? Cross-Tabulation ? Summary Data Visualization with matplotlib ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary Data Science and ML Algorithms in scikit-learn ? Data Science, Machine Learning, AI? ? Types of Machine Learning ? Terminology: Features and Observations ? Continuous and Categorical Features (Variables) ? Terminology: Axis ? The scikit-learn Package ? scikit-learn Estimators ? Models, Estimators, and Predictors ? Common Distance Metrics ? The Euclidean Metric ? The LIBSVM format ? Scaling of the Features ? The Curse of Dimensionality ? Supervised vs Unsupervised Machine Learning ? Supervised Machine Learning Algorithms ? Unsupervised Machine Learning Algorithms ? Choose the Right Algorithm ? Life-cycles of Machine Learning Development ? Data Split for Training and Test Data Sets ? Data Splitting in scikit-learn ? Hands-on Exercise ? Classification Examples ? Classifying with k-Nearest Neighbors (SL) ? k-Nearest Neighbors Algorithm ? k-Nearest Neighbors Algorithm ? The Error Rate ? Hands-on Exercise ? Dimensionality Reduction ? The Advantages of Dimensionality Reduction ? Principal component analysis (PCA) ? Hands-on Exercise ? Data Blending ? Decision Trees (SL) ? Decision Tree Terminology ? Decision Tree Classification in Context of Information Theory ? Information Entropy Defined ? The Shannon Entropy Formula ? The Simplified Decision Tree Algorithm ? Using Decision Trees ? Random Forests ? SVM ? Naive Bayes Classifier (SL) ? Naive Bayesian Probabilistic Model in a Nutshell ? Bayes Formula ? Classification of Documents with Naive Bayes ? Unsupervised Learning Type: Clustering ? Clustering Examples ? k-Means Clustering (UL) ? k-Means Clustering in a Nutshell ? k-Means Characteristics ? Regression Analysis ? Simple Linear Regression Model ? Linear vs Non-Linear Regression ? Linear Regression Illustration ? Major Underlying Assumptions for Regression Analysis ? Least-Squares Method (LSM) ? Locally Weighted Linear Regression ? Regression Models in Excel ? Multiple Regression Analysis ? Logistic Regression ? Regression vs Classification ? Time-Series Analysis ? Decomposing Time-Series ? Summary Lab Exercises Lab 1 - Learning the Lab Environment Lab 2 - Using Jupyter Notebook Lab 3 - Repairing and Normalizing Data Lab 4 - Computing Descriptive Statistics Lab 5 - Data Grouping and Aggregation Lab 6 - Data Visualization with matplotlib Lab 7 - Data Splitting Lab 8 - k-Nearest Neighbors Algorithm Lab 9 - The k-means Algorithm Lab 10 - The Random Forest Algorithm

Power BI is a powerful data visualisation program that allows businesses to monitor data, analyse trends, and make decisions. This course is designed to provide a solid understanding of the reporting side of Power BI, the dashboards, where administrators, and end users can interact with dynamic visuals that communicates information. This course focuses entirely on the creation and design of visualisations in dashboards, including a range of chart types, engaging maps, and different types of tables. Designing dashboards with KPI's (key performance indicators), heatmaps, flowcharts, sparklines, and compare multiple variables with trendlines. This one-day programme focuses entirely on creating dashboards, by using the many visualisation tools available in Power BI. You will learn to build dynamic, user-friendly interfaces in both Power BI Desktop and Power BI Service. 1 Introduction Power BI ecosystem Things to keep in mind Selecting dashboard colours Importing visuals into Power BI Data sources for your analysis Joining tables in Power BI 2 Working with data Utilising a report theme Table visuals Matrix visuals Drilling into hierarchies Applying static filters Group numbers with lists Group numbers with bins 3 Creating visuals Heatmaps in Power BI Visualising time-intelligence trends Ranking categorical totals Comparing proportions View trends with sparklines 4 Comparing variables Insert key performance indicators (KPI) Visualising trendlines as KPI Forecasting with trendlines Visualising flows with Sankey diagrams Creating a scatter plot 5 Mapping options Map visuals Using a filled map Mapping with latitude and longitude Mapping with ArcGIS or ESRI 6 Creating dashboards High-level dashboard Migration analysis dashboard Adding slicers for filtering Promote interaction with nudge prompts Searching the dashboard with a slicer Creating dynamic labels Highlighting key points on the dashboard Customised visualisation tooltips Syncing slicers across pages 7 Sharing dashboards Setting up and formatting phone views Exporting data Creating PDF files Uploading to the cloud Share dashboards in SharePoint online

Duration 3 Days 18 CPD hours This course is intended for This course is designed for IoT practitioners who are looking to improve their skills and knowledge of IoT security and privacy. This course is also designed for students who are seeking the CertNexus Certified Internet of Things Security Practitioner (CIoTSP) certification and who want to prepare for Exam ITS-110. Overview This program will validate that the candidate has the knowledge, skills, and abilities to secure network environments for IoT devices, analyze vulnerabilities and determine reasonable controls against threats, and effectively monitor IoT devices and respond to incidents. This course is designed for practitioners who are seeking to demonstrate a vendor-neutral, cross-industry skill set that will enable them to design, implement, operate, and/or manage a secure IoT ecosystem. Managing IoT Risks Map the IoT Attack Surface Build in Security by Design Securing Web and Cloud Interfaces Identify Threats to IoT Web and Cloud Interfaces Prevent Injection Flaws Prevent Session Management Flaws Prevent Cross-Site Scripting Flaws Prevent Cross-Site Request Forgery Flaws Prevent Unvalidated Redirects and Forwards Securing Data Use Cryptography Appropriately Protect Data in Motion Protect Data at Rest Protect Data in Use Controlling Access to IoT Resources Identify the Need to Protect IoT Implement Secure Authentication Implement Secure Authorization Implement Security Monitoring on IoT Systems Securing IoT Networks Ensure the Security of IP Networks Ensure the Security of Wireless Networks Ensure the Security of Mobile Networks Ensure the Security of IoT Edge Networks Ensuring Privacy Improve Data Collection to Reduce Privacy Concerns Protect Sensitive Data Dispose of Sensitive Data Managing Software and Firmware Risks Manage General Software Risks Manage Risks Related to Software Installation and Configuration Manage Risks Related to Software Patches and Updates Manage Risks Related to IoT Device Operating Systems and Firmware Promoting Physical Security Protect Local Memory and Storage Prevent Physical Port Access

Duration 2 Days 12 CPD hours This course is intended for This in an introductory-level class for intermediate skilled team members. Students should have prior software development experience or exposure, have some basic familiarity with containers, and should also be able to navigate the command line. Overview This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Our engaging instructors and mentors are highly experienced practitioners who bring years of current 'on-the-job' experience into every classroom. Working in a hands-on learning environment led by our expert facilitator, students will explore: What a Kubernetes cluster is, and how to deploy and manage them on-premises and in the cloud. How Kubernetes fits into the cloud-native ecosystem, and how it interfaces with other important technologies such as Docker. The major Kubernetes components that let us deploy and manage applications in a modern cloud-native fashion. How to define and manage applications with declarative manifest files that should be version-controlled and treated like code. Containerization has taken the IT world by storm in the last few years. Large software houses, starting from Google and Amazon, are running significant portions of their production load in containers. Kubernetes is an open-source system for automating deployment, scaling, and management of containerized applications. This is a hands-on workshop style course that teaches core features and functionality of Kubernetes. You will leave this course knowing how to build a Kubernetes cluster, and how to deploy and manage applications on that cluster. Getting Started Our sample application Kubernetes concepts Declarative vs imperative Kubernetes network model First contact with kubectl Setting up Kubernetes Working with Containers Running our first containers on Kubernetes Exposing containers Shipping images with a registry Running our application on Kubernetes Exploring the Kubernetes Dashboard The Kubernetes dashboard Security implications of kubectl apply Scaling a deployment Daemon sets Labels and selectors Rolling updates Next Steps Accessing logs from the CLI Managing stacks with Helm Namespaces Next steps

Tableau is an intuitive and simple tool to learn. This Tableau training course is a jumpstart to getting report writers and analysts who are self-taught or have no previous knowledge to being productive. It covers everything from connecting to data, through to creating interactive dashboards with a range of visualisations in three days. Having a quick turnaround from starting to use Tableau, to getting real, actionable insights means that you get a swift return on your investment. At the end of this course, you will be able to communicate insights more effectively, enabling your organisation to make better decisions, quickly. This accelerated approach is key to getting engagement from within your organisation so everyone can immediately see and feel the impact of the data and insights you create. Our Tableau Desktop Fast Track course combines all of our Foundation (Fundamentals) and Analyst (Intermediate) content into a 3 day live online course with added access to online bonus content of 3 additional modules. Gathering Requirements, Bring Your Own Data and Engaging Users. What do you get? This course is delivered live virtually and has all material provided through our online portal, together with email support and live coaching sessions. The full program includes all of the following elements: 3 days of live and interactive instructor-led sessions delivered by an expert Tableau Trainer 6 weeks access to our live coaching program delivered by expert Tableau coaches 50+ practical exercises to practice what you learn 12 months access to video’s that walk you through the theory and exercise solutions Practical advice, tools and resources for using Tableau in the real world The three additional online modules provide:Clarity on the approach to gathering dashboard requirements in a way that can be translated into dashboard designs.An agile and iterative development process that delivers products that meet user needs more quickly and effectively.An understanding of how end users will interact with dashboards to ensure that designers deliver actionable results. THE SYLLABUS PHASE 1: DESIGN MODULE 1: UNDERSTAND TABLEAU What is possible How does Tableau deal with data Know your way around Review of Type Conversions How do we format charts Dashboard basics – My first Dashboard MODULE 2: TRANSFORM DATA Connecting to and setting up data in Tableau Modifying data attributes How Do I Structure my Data – Groups & Hierarchies, Visual Groups How Tableau Deals with Dates – Using Discrete and Continuous Dates, Custom Dates How do I create calculated fields and why? – Creating Calculated Fields, Types of calculated fields, Row Level v Aggregations, Aggregating dimensions in calculations, Changing the Level of Detail (LOD) of calculations – What, Why, How MODULE 3: GATHER REQUIREMENTS(ONLINE CONTENT ONLY) Brainstorm and assess possible priorities Pitfalls to avoid Gather requirements PHASE 2: DEVELOP MODULE 4: CREATE CHARTS Charts that Compare Multiple Measures – Measure Names and Measure Values, Shared Axis Charts, Dual Axis Charts, Scatter Plots Showing progress over time Creating Tables – Creating Tables, Highlight Tables, Heat Maps Showing Relational & Proportional Data – Pie Charts, Donut Charts, Tree Maps Making things dynamic with parameters MODULE 5: COMBINE DATA Relationships Joining Tables – Join Types, Joining tables within the same database, cross database joins, join calculations Blending – How to create a blend with common fields, Custom defined Field relationships and mismatched element names, Calculated fields in blended data sources Unions – Manual Unions and mismatched columns, Wildcard unions Data Extracts – Creating & Editing Data extracts MODULE 6: ANALYSE INFORMATION Table Calculations Sets, Reference Lines, Trends and Forecasting Answering spatial questions – Mapping, Creating a choropleth (filled) map, Using your own images for spatial analysis, Mapping with spatial files Advanced charts Bar in Bar charts Bullet graphs Creating Bins and Histograms Creating a Box & Whisker plot Viz in Tooltips PHASE 3: DESIGN MODULE 7: BUILD DASHBOARDS Using the Dashboard Interface Device layouts Dashboard Actions – Set actions, Parameter actions Viz in Tooltips for Dashboards Dashboard containers – Horizontal & Vertical containers, Hidden containers Navigate between dashboards Telling data driven stories MODULE 8: BRING YOUR OWN DATA Design Best Practices & Resources Wireframe templates Questions Process Start building and testing MODULE 9: EMPOWER STAKEHOLDERS What is Tableau Server Publishing & permissions How can your users engage with content The Tableau ecosystem Review your progress Your next steps HOW MUCH OF YOUR TIME WILL THIS TAKE? Delegates are also provided 6 weeks access to our Tableau Coaching. We run Live Q&A sessions from 4pm-5pm on a Monday (Connecting to Data and Calculated Fields), 2pm-3pm Friday (Creating Charts) & 4pm-5pm Friday (Dashboard Design). The coaching helps delegates to transition from the theory of using Tableau to its practical use. We’d be expecting them to apply the exercises you’ll be doing during the course, onto your own data after the course. In our experience, this is the best way to increase both understanding and long term memory retention. The live coaching also acts as a troubleshooting platform for any practical issues that delegates need to overcome in the real world. Delegates also have 12 months access to all of the training material covered in the course in the form of an online portal (this includes theory videos, exercise solution videos, exercise materials and even quizzes). We have a growing LinkedIn community that delegates are encouraged to join and participate in. We regularly post useful blog posts and additional training that will enhance the Tableau journey and understanding. We help teams using Tableau to transform in the following ways : From a disjointed understanding of Tableau – To being familiar with Tableau terminology and capability From ad-hoc data uploads and error prone calculations – To reusable data connections and robust metrics From disjointed stakeholder questions – To clear and concise requirements that lead to decision making From being unsure how difficult Tableau will be to learn – To being able to develop standard charts and tables in Tableau with dynamic reporting capabilities From manually combining data for each analytical task – To dynamically combining data from multiple tables for analysis From being unsure how to answer analytical questions and what options there are – To being equipped with multiple actionable, dynamic, analytical use cases From not knowing Why, When and How to create Dashboards or Story’s – To being able to combine analysis to answer complex questions and tell data driven stories From using demo data theory – To Delivering value [Answering questions] on their own data From spending lots of time answering colleagues ad-hoc (data) questions – To empowering stakeholders in answering ad-hoc queries and reducing the time to analyse and steer the business

Duration 3 Days 18 CPD hours This course is intended for Java Fundamentals is designed for tech enthusiasts who are familiar with some programming languages and want a quick introduction to the most important principles of Java. Overview After completing this course, you will be able to: Create and run Java programs Use data types, data structures, and control flow in your code Implement best practices while creating objects Work with constructors and inheritance Understand advanced data structures to organize and store data Employ generics for stronger check-types during compilation Learn to handle exceptions in your code Since its inception, Java has stormed the programming world. Its features and functionalities provide developers with the tools needed to write robust cross-platform applications. Java Fundamentals introduces you to these tools and functionalities that will enable you to create Java programs. The course begins with an introduction to the language, its philosophy, and evolution over time, until the latest release. You'll learn how the javac/java tools work and what Java packages are - the way a Java program is usually organized. Once you are comfortable with this, you'll be introduced to advanced concepts of the language, such as control flow keywords. You'll explore object-oriented programming and the part it plays in making Java what it is. In the concluding lessons, you'll be familiarized with classes, typecasting, and interfaces, and understand the use of data structures, arrays, strings, handling exceptions, and creating generics. Introduction to Java The Java Ecosystem Our First Java Application Packages Variables, Data Types, and Operators Variables and Data Types Integral Data Types Type casting Control Flow Conditional Statements Looping Constructs Object-Oriented Programming Object-Oriented Principles Classes and Objects Constructors The this Keyword Inheritance Overloading Constructor Overloading Polymorphism and Overriding Annotations References OOP in Depth Interfaces Typecasting The Object Class Autoboxing and Unboxing Abstract Classes and Methods Data Structures, Arrays, and Strings Data Structures and Algorithms Strings The Java Collections Framework and Generics Reading Data from Files The Java Collections Framework Generics Collection Advanced Data Structures in Java Implementing a Custom Linked List Implementing Binary Search Tree Enumerations Set and Uniqueness in Set Exception Handling Motivation behind Exceptions Exception Sources Exception Mechanics Best Practices

Learn everything you need to know to be fully competent with Mac iOS. This syllabus takes you around the basics and then on another deep dive into all the elements. Discover things you never knew and speed up your experience using Mac iOS. Module 1: Introduction to Mac iOS and Hardware • Understanding the Mac ecosystem • Overview of Mac hardware components • Navigating the Mac interface Module 2: Mac Operating System (macOS) • Exploring the macOS interface • Customizing system preferences • File management and organization on macOS Module 3: Essential Mac Apps • Using Safari for web browsing • Effective web searching using search engines • Email management with Apple Mail • Calendar and task management with Apple Calendar Module 4: Software Installation and Updates • Installing and updating software applications • Managing and uninstalling programs • App Store and app installations Module 5: Productivity and Collaboration • Using iCloud for cloud-based storage and collaboration • Working with Notes, Reminders, and Messages • Collaborative document editing with iWork Module 6: Multimedia and Creativity • Basic image editing with Photos and Preview • Music creation with GarageBand • Creating multimedia presentations with Keynote Module 7: Troubleshooting and Maintenance • Identifying and resolving common Mac issues • Using Activity Monitor for performance monitoring • Maintenance tasks for macOS Module 8: Mac Security and Privacy • Overview of Mac security features • Online safety and privacy best practices • Protecting personal data and devices Module 9: Advanced Mac Features • Customizing the Dock and Menu Bar • Using Siri for voice commands and search • Continuity features for seamless device integration Module 10: Using AI and Chat GPT • Introduction to AI and Chat GPT technology • Exploring AI-powered features on Mac • Using Chat GPT for productivity and assistance Module 11: Browsing and Search Engines • Effective use of web browsers on macOS • Utilizing search engines for research • Online safety and privacy while browsing Module 12: Cybersecurity • Understanding cybersecurity threats • Protecting against malware and phishing attacks • Secure online practices and password management Module 13: Software Installation and Factory Reset • Installing and updating software applications • Factory resetting a Mac device • Data backup and recovery during resets Module 14: Final Projects and Assessment • Culminating projects showcasing Mac iOS skills • Practical exams assessing Mac software knowledge and skills • Preparing for industry-recognized certifications (optional) Please note that the duration and depth of each module can vary depending on the level of expertise required and the specific needs of the learners. Additionally, it's important to adapt the curriculum to the learners' proficiency levels, whether they are A Level/GCSE students or adult learners with different experience levels.

Duration 3 Days 18 CPD hours This course is intended for This is an introductory-level course designed to teach experienced systems administrators how to install, maintain, monitor, troubleshoot, optimize, and secure Hadoop. Previous Hadoop experience is not required. Overview Working within in an engaging, hands-on learning environment, guided by our expert team, attendees will learn to: Understand the benefits of distributed computing Understand the Hadoop architecture (including HDFS and MapReduce) Define administrator participation in Big Data projects Plan, implement, and maintain Hadoop clusters Deploy and maintain additional Big Data tools (Pig, Hive, Flume, etc.) Plan, deploy and maintain HBase on a Hadoop cluster Monitor and maintain hundreds of servers Pinpoint performance bottlenecks and fix them Apache Hadoop is an open source framework for creating reliable and distributable compute clusters. Hadoop provides an excellent platform (with other related frameworks) to process large unstructured or semi-structured data sets from multiple sources to dissect, classify, learn from and make suggestions for business analytics, decision support, and other advanced forms of machine intelligence. This is an introductory-level, hands-on lab-intensive course geared for the administrator (new to Hadoop) who is charged with maintaining a Hadoop cluster and its related components. You will learn how to install, maintain, monitor, troubleshoot, optimize, and secure Hadoop. Introduction Hadoop history and concepts Ecosystem Distributions High level architecture Hadoop myths Hadoop challenges (hardware / software) Planning and installation Selecting software and Hadoop distributions Sizing the cluster and planning for growth Selecting hardware and network Rack topology Installation Multi-tenancy Directory structure and logs Benchmarking HDFS operations Concepts (horizontal scaling, replication, data locality, rack awareness) Nodes and daemons (NameNode, Secondary NameNode, HA Standby NameNode, DataNode) Health monitoring Command-line and browser-based administration Adding storage and replacing defective drives MapReduce operations Parallel computing before MapReduce: compare HPC versus Hadoop administration MapReduce cluster loads Nodes and Daemons (JobTracker, TaskTracker) MapReduce UI walk through MapReduce configuration Job config Job schedulers Administrator view of MapReduce best practices Optimizing MapReduce Fool proofing MR: what to tell your programmers YARN: architecture and use Advanced topics Hardware monitoring System software monitoring Hadoop cluster monitoring Adding and removing servers and upgrading Hadoop Backup, recovery, and business continuity planning Cluster configuration tweaks Hardware maintenance schedule Oozie scheduling for administrators Securing your cluster with Kerberos The future of Hadoop

Duration 3 Days 18 CPD hours This course is intended for This course is intended for a programmer or web developer who is new to mobile app development in iOS. The student has experience using a computer running Mac OS X and developing applications or websites using object-oriented programming languages and tools, but may not have experience using the languages and tools specific to the iOS development environment. Overview In this course, you will develop, test, and package applications for devices that run the iOS operating system, including iPad and iPhone. You will: •Use Xcode to create and run an iOS application. •Write Objective-C code to enable iOS app user interface elements to interact with users. •Identify and describe common iOS design patterns and user interface standards. •Lay out and program apps to provide navigation among multiple views. •Access data from various locations, including app resources, user preferences, private app storage, and web services. •Enable an app to present graphics and multimedia. •Enable an app to interact well with other apps, the iOS system, and the mobile device it runs on. •Debug an app and implement runtime error handling. •Prepare an app for release, including configuration to support internationalization, and to run on multiple devices and different screen dimensions. This Logical Operations courseware is no longer available on the LO Store, but can be purchased by phone orderIn this course, students will learn how to develop and publish iOS apps, focusing on developing business apps for iPhones and iPads. Using Xcode to Develop an iOS App Set Up and Configure Xcode Create an App Project Create a User Interface Run an App in Simulator Programming in the iOS Development Environment Write Code to Handle User Interaction Organize and Maintain Code Use Predefined Classes Identifying Design Requirements for iOS Apps Design an App to Meet User Expectations iOS Design Patterns and Templates Implementing Multiple View Navigation Create an iOS App with Multiple Views Add a Custom View Controller Class Show Temporary Views Working with Data Select an Appropriate Data Storage Approach Load App Data from Property Lists Access Web Data and Services Store and Retrieve Preferences Working with Graphics and Media Load Graphic Images Draw Graphics Through Code Add Animation Effects Integrating with the App Ecosystem Manage App State Changes Map a Location Support Multiple Devices and Orientations Making Code More Robust and Maintainable Debug an App in Xcode Write Code to Handle Runtime Errors Finalizing an App Enable an App to Support Multiple Languages Prepare an App for Release

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: Developers responsible for developing Deep Learning applications Developers who want to understand concepts behind Deep Learning and how to implement a Deep Learning solution on AWS Overview This course is designed to teach you how to: Define machine learning (ML) and deep learning Identify the concepts in a deep learning ecosystem Use Amazon SageMaker and the MXNet programming framework for deep learning workloads Fit AWS solutions for deep learning deployments In this course, you?ll learn about AWS?s deep learning solutions, including scenarios where deep learning makes sense and how deep learning works. You?ll learn how to run deep learning models on the cloud using Amazon SageMaker and the MXNet framework. You?ll also learn to deploy your deep learning models using services like AWS Lambda while designing intelligent systems on AWS. Module 1: Machine learning overview A brief history of AI, ML, and DL The business importance of ML Common challenges in ML Different types of ML problems and tasks AI on AWS Module 2: Introduction to deep learning Introduction to DL The DL concepts A summary of how to train DL models on AWS Introduction to Amazon SageMaker Hands-on lab: Spinning up an Amazon SageMaker notebook instance and running a multi-layer perceptron neural network model Module 3: Introduction to Apache MXNet The motivation for and benefits of using MXNet and Gluon Important terms and APIs used in MXNet Convolutional neural networks (CNN) architecture Hands-on lab: Training a CNN on a CIFAR-10 dataset Module 4: ML and DL architectures on AWS AWS services for deploying DL models (AWS Lambda, AWS IoT Greengrass, Amazon ECS, AWS Elastic Beanstalk) Introduction to AWS AI services that are based on DL (Amazon Polly, Amazon Lex, Amazon Rekognition) Hands-on lab: Deploying a trained model for prediction on AWS Lambda Additional course details: Nexus Humans Deep Learning on AWS training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Deep Learning on AWS course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.