587 Machine Learning (ML) courses

Duration 2 Days 12 CPD hours This course is intended for Business Analysts, Technical Managers, and Programmers Overview This intensive training course helps students learn the practical aspects of the R programming language. The course is supplemented by many hands-on labs which allow attendees to immediately apply their theoretical knowledge in practice. Over the past few years, R has been steadily gaining popularity with business analysts, statisticians and data scientists as a tool of choice for conducting statistical analysis of data as well as supervised and unsupervised machine learning. What is R ? What is R? ? Positioning of R in the Data Science Space ? The Legal Aspects ? Microsoft R Open ? R Integrated Development Environments ? Running R ? Running RStudio ? Getting Help ? General Notes on R Commands and Statements ? Assignment Operators ? R Core Data Structures ? Assignment Example ? R Objects and Workspace ? Printing Objects ? Arithmetic Operators ? Logical Operators ? System Date and Time ? Operations ? User-defined Functions ? Control Statements ? Conditional Execution ? Repetitive Execution ? Repetitive execution ? Built-in Functions ? Summary Introduction to Functional Programming with R ? What is Functional Programming (FP)? ? Terminology: Higher-Order Functions ? A Short List of Languages that Support FP ? Functional Programming in R ? Vector and Matrix Arithmetic ? Vector Arithmetic Example ? More Examples of FP in R ? Summary Managing Your Environment ? Getting and Setting the Working Directory ? Getting the List of Files in a Directory ? The R Home Directory ? Executing External R commands ? Loading External Scripts in RStudio ? Listing Objects in Workspace ? Removing Objects in Workspace ? Saving Your Workspace in R ? Saving Your Workspace in RStudio ? Saving Your Workspace in R GUI ? Loading Your Workspace ? Diverting Output to a File ? Batch (Unattended) Processing ? Controlling Global Options ? Summary R Type System and Structures ? The R Data Types ? System Date and Time ? Formatting Date and Time ? Using the mode() Function ? R Data Structures ? What is the Type of My Data Structure? ? Creating Vectors ? Logical Vectors ? Character Vectors ? Factorization ? Multi-Mode Vectors ? The Length of the Vector ? Getting Vector Elements ? Lists ? A List with Element Names ? Extracting List Elements ? Adding to a List ? Matrix Data Structure ? Creating Matrices ? Creating Matrices with cbind() and rbind() ? Working with Data Frames ? Matrices vs Data Frames ? A Data Frame Sample ? Creating a Data Frame ? Accessing Data Cells ? Getting Info About a Data Frame ? Selecting Columns in Data Frames ? Selecting Rows in Data Frames ? Getting a Subset of a Data Frame ? Sorting (ordering) Data in Data Frames by Attribute(s) ? Editing Data Frames ? The str() Function ? Type Conversion (Coercion) ? The summary() Function ? Checking an Object's Type ? Summary Extending R ? The Base R Packages ? Loading Packages ? What is the Difference between Package and Library? ? Extending R ? The CRAN Web Site ? Extending R in R GUI ? Extending R in RStudio ? Installing and Removing Packages from Command-Line ? Summary Read-Write and Import-Export Operations in R ? Reading Data from a File into a Vector ? Example of Reading Data from a File into A Vector ? Writing Data to a File ? Example of Writing Data to a File ? Reading Data into A Data Frame ? Writing CSV Files ? Importing Data into R ? Exporting Data from R ? Summary Statistical Computing Features in R ? Statistical Computing Features ? Descriptive Statistics ? Basic Statistical Functions ? Examples of Using Basic Statistical Functions ? Non-uniformity of a Probability Distribution ? Writing Your Own skew and kurtosis Functions ? Generating Normally Distributed Random Numbers ? Generating Uniformly Distributed Random Numbers ? Using the summary() Function ? Math Functions Used in Data Analysis ? Examples of Using Math Functions ? Correlations ? Correlation Example ? Testing Correlation Coefficient for Significance ? The cor.test() Function ? The cor.test() Example ? Regression Analysis ? Types of Regression ? Simple Linear Regression Model ? Least-Squares Method (LSM) ? LSM Assumptions ? Fitting Linear Regression Models in R ? Example of Using lm() ? Confidence Intervals for Model Parameters ? Example of Using lm() with a Data Frame ? Regression Models in Excel ? Multiple Regression Analysis ? Summary Data Manipulation and Transformation in R ? Applying Functions to Matrices and Data Frames ? The apply() Function ? Using apply() ? Using apply() with a User-Defined Function ? apply() Variants ? Using tapply() ? Adding a Column to a Data Frame ? Dropping A Column in a Data Frame ? The attach() and detach() Functions ? Sampling ? Using sample() for Generating Labels ? Set Operations ? Example of Using Set Operations ? The dplyr Package ? Object Masking (Shadowing) Considerations ? Getting More Information on dplyr in RStudio ? The search() or searchpaths() Functions ? Handling Large Data Sets in R with the data.table Package ? The fread() and fwrite() functions from the data.table Package ? Using the Data Table Structure ? Summary Data Visualization in R ? Data Visualization ? Data Visualization in R ? The ggplot2 Data Visualization Package ? Creating Bar Plots in R ? Creating Horizontal Bar Plots ? Using barplot() with Matrices ? Using barplot() with Matrices Example ? Customizing Plots ? Histograms in R ? Building Histograms with hist() ? Example of using hist() ? Pie Charts in R ? Examples of using pie() ? Generic X-Y Plotting ? Examples of the plot() function ? Dot Plots in R ? Saving Your Work ? Supported Export Options ? Plots in RStudio ? Saving a Plot as an Image ? Summary Using R Efficiently ? Object Memory Allocation Considerations ? Garbage Collection ? Finding Out About Loaded Packages ? Using the conflicts() Function ? Getting Information About the Object Source Package with the pryr Package ? Using the where() Function from the pryr Package ? Timing Your Code ? Timing Your Code with system.time() ? Timing Your Code with System.time() ? Sleeping a Program ? Handling Large Data Sets in R with the data.table Package ? Passing System-Level Parameters to R ? Summary Lab Exercises Lab 1 - Getting Started with R Lab 2 - Learning the R Type System and Structures Lab 3 - Read and Write Operations in R Lab 4 - Data Import and Export in R Lab 5 - k-Nearest Neighbors Algorithm Lab 6 - Creating Your Own Statistical Functions Lab 7 - Simple Linear Regression Lab 8 - Monte-Carlo Simulation (Method) Lab 9 - Data Processing with R Lab 10 - Using R Graphics Package Lab 11 - Using R Efficiently

Machine Learning for Business Professionals This Machine Learning for Business Professionals course provides an in-depth exploration of how machine learning and data science can revolutionise business decision-making. Starting with the basics of R programming, you’ll learn how to manage and visualise data effectively, progressing to more complex machine learning techniques. By the end of this course, you’ll be equipped to apply predictive models and generate insights that can significantly impact business strategies. Course Curriculum Data Science and Machine Learning Course Intro Getting Started with R Data Types and Structures in R Intermediate R Data Manipulation in R Data Visualization in R Creating Reports with R Markdown Building Webapps with R Shiny Introduction to Machine Learning Starting A Career in Data Science Resources (Learn more about this online course)

ITIL® 4 Specialist: Create, Deliver and Support: Virtual In-House Training The ITIL® 4 Specialist: Create, Deliver, and Support module is part of the Managing Professional stream for ITIL® 4. Candidates need to pass the related certification exam for working towards the Managing Professional (MP) designation. This course is based on the ITIL® 4 Specialist: Create, Deliver, and Support exam specifications from AXELOS. With the help of ITIL® 4 concepts and terminology, exercises, and examples included in the course, candidates acquire the relevant knowledge required to pass the certification exam. What You Will Learn The learning objectives of the course are based on the following learning outcomes of the ITIL® 4 Specialist: Create, Deliver, and Support exam specification: Understand how to plan and build a service value stream to create, deliver, and support services Know how relevant ITIL® practices contribute to the creation, delivery, and support across the SVS and value streams Know how to create, deliver, and support services Organization and Culture Organizational Structures Team Culture Continuous Improvement Collaborative Culture Customer-Oriented Mindset Positive Communication Effective Teams Capabilities, Roles, and Competencies Workforce Planning Employee Satisfaction Management Results-Based Measuring and Reporting Information Technology to Create, Deliver, and Support Service Integration and Data Sharing Reporting and Advanced Analytics Collaboration and Workflow Robotic Process Automation Artificial Intelligence and Machine Learning CI / CD Information Model Value Stream Anatomy of a Value Stream Designing a Value Stream Value Stream Mapping Value Stream to Create, Deliver, and Support Services Value Stream for Creation of a New Service Value Stream for User Support Value Stream Model for Restoration of a Live Service Prioritize and Manage Work Managing Queues and Backlogs Shift-Left Approach Prioritizing Work Commercial and Sourcing Considerations Build or Buy Sourcing Models Service Integration and Management

Duration 1 Days 6 CPD hours This course is intended for This course is designed for non-technical business executives who are tasked with making business decisions about emerging technologies in their businesses. Overview You will learn:Blockchain Cloud BasicsIoT OverviewMobility and Ambient ComputingMachine Learning and Deep LearningChatbots, Robotics, and More This course is designed for non-technical business executives looking to learn and understand emerging technologies. Blockchain Cloud BasicsIoT OverviewMobility and Ambient ComputingMachine Learning and Deep LearningChatbots, Robotics, and More

One of the best AI and data science colleges in Coimbatore, SkyAppz Academy, offers state-of-the-art courses in Artificial Intelligence, machine learning, and deep learning. With our courses, beginners and professionals alike can acquire new AI skills through guided projects, lectures by seasoned professionals, and interactive capstone exercises. The offered AI course in Coimbatore also includes advanced IT career support, such as industry-standard certifications, flexible study options, and comprehensive placement assistance. Begin your career in the world of AI today and confidently transform its future.

Our Business Transformation Courses are aimed at empowering business professionals with knowledge and skills needed to transform business operations using modern business techniques, information technology, data analytics, and software tools. Here, we offer a number of foundation, intermediate, practitioner, professional and specialist courses leading to certifications by leading chartered institutes across the world.

Have you ever worried about accidentally using someone else's work in your PowerPoint presentations without proper attribution? Or maybe you want to ensure your academic or professional slides are original and free of plagiarism. Maintaining originality is crucial in both academic and professional settings, and that's where free plagiarism checkers for PowerPoint come to the rescue. These tools help you ensure that your presentations are authentic and that all sources are correctly cited. What is a Plagiarism Checker for PowerPoint? A plagiarism checker for PowerPoint is a digital tool designed to scan the content of your slides for any instances of plagiarism. It compares your text, images, and other content against a vast database of sources to identify any potential matches. These tools use algorithms and extensive databases of web pages, academic papers, and other published content to identify similarities between your presentation and existing sources. Advanced tools may also use artificial intelligence to detect paraphrased content that still closely resembles the original source. Why Use Free Plagiarism Checkers for PowerPoint? Not everyone has the budget for premium plagiarism detection software. Free tools offer a cost-effective solution for students, educators, and professionals who need to ensure their work is original. Most free plagiarism checkers are available online and can be used directly from your browser, without needing any downloads or installations. This makes them a convenient option for quick checks, whether you're at home, at work, or on the go. Features to Look for in a Plagiarism Checker for PowerPoint The most important feature of a plagiarism checker is its ability to accurately detect copied content. Look for tools with high detection rates and comprehensive databases. A good plagiarism checker should be easy to use, with a simple interface that allows you to quickly upload your PowerPoint files and get results without hassle. Consider whether the plagiarism checker integrates with other platforms you use, such as Microsoft Office, Google Drive, or cloud storage services. Some plagiarism checkers offer customization options, such as choosing the database they scan against or adjusting the sensitivity of the checker. These features can be useful if you have specific needs, like checking against academic databases or avoiding self-plagiarism. How to Use Online Plagiarism Checkers for PowerPoint Effectively Start with the best possible version of your PowerPoint presentation. Ensure that all quotes, data, and images are correctly cited before running the plagiarism check. After running your presentation through the plagiarism checker, carefully review the report. Check any flagged content and make necessary revisions to ensure your work is original and properly attributed. Once you've addressed any potential issues, make a final pass through your presentation to refine your slides and ensure clarity and originality. Benefits of Using Plagiarism Checkers Using plagiarism checkers helps maintain academic integrity by ensuring your work is original and that all sources are properly cited, which is crucial for students and researchers. In the professional world, originality is key. Plagiarism checkers help ensure that your presentations reflect your own work and ideas, boosting your credibility. Plagiarism checkers save you time by quickly identifying potential issues, allowing you to focus on refining your content instead of manually checking for plagiarism. Potential Drawbacks of Free Plagiarism Checkers Free plagiarism checkers may have limitations in their databases, potentially missing some sources or failing to detect more sophisticated forms of plagiarism, like paraphrasing. Uploading your PowerPoint presentations to online tools may raise privacy concerns, especially if they contain sensitive or proprietary information. Always ensure the tool you use has a robust privacy policy. Relying too heavily on plagiarism checkers can reduce your vigilance in ensuring originality. It's essential to balance using these tools with your own checks and citations. Plagiarism Checker Tools for Different Needs For academic purposes, tools like MyAssignmentHelp's plagiarism checker offer advanced features tailored to academic writing, ensuring your research is properly cited and free of plagiarism. Professionals can benefit from plagiarism checkers that provide comprehensive scanning and detailed reports, helping to maintain a high standard of originality in corporate presentations. For casual or personal presentations, simpler tools may suffice, offering basic checks to ensure your slides are free from unintentional plagiarism. The Future of Plagiarism Checking Technology Advances in AI and machine learning are continually improving the capabilities of plagiarism checkers, making them more accurate and user-friendly. Future developments may include better integration with presentation software, real-time scanning features, and enhanced support for multimedia content in presentations. Conclusion In today's digital age, ensuring originality in your PowerPoint presentations is more important than ever. Free plagiarism checkers provide a valuable service, helping you maintain academic and professional integrity. While they have their limitations, their benefits make them an essential tool for anyone creating presentations. FAQs Free plagiarism checkers are generally reliable for basic checks, but they may not catch all instances of plagiarism or offer as detailed feedback as premium versions. While plagiarism checkers are a helpful aid, they cannot replace the need for manual citation and proper attribution. Always review and cite your sources carefully. Most reputable plagiarism checkers, like MyAssignmentHelp, have privacy policies in place to protect user data. However, it's always wise to avoid uploading sensitive or confidential presentations to online tools. Some plagiarism checkers offer limited support for multimedia content, such as images and videos. However, text-based content remains their primary focus. Yes, tools like MyAssignmentHelp's plagiarism checker offer features tailored to academic presentations, helping you ensure your slides are original and properly cited.