Nanotechnology

R: Interactive Visualizations with htmlwidgets

0

Learn how to rapidly create rich, interactive data visualizations with R and htmlwidgets—packages that connect R to popular JavaScript libraries like Plotly, Leaflet, and DT.Using the R language almost exclusively, htmlwidgets allow you to create the same interactive maps, charts, and graphs you see on popular data journalism sites and in BI dashboards. You can connect R to popular JavaScript libraries—such as Plotly and Leaflet—with htmlwidget packages. The interactive visualizations you create can be used in R Markdown reports and presentations, and even integrated into rich, responsive Shiny applications. This course introduces you to the fundamental skills needed to add htmlwidgets to your R workflow. Start by learning to manage packages and structure data for visualizations with the tidyverse and the pipe operator. Then there is an important question: Which library should you choose? The course introduces five popular options: Leaflet, Plotly, Highcharter, visNetwork, and DataTables (DT). Instructor Martin Hadley shows how to use these libraries to create scattergeo, choropleth, and geolines maps; stacked bar charts, scatter charts, bubble charts, and heat maps; treemaps and time series charts; interactive networks and graphs; and responsive, interactive data tables. Plus, learn how to customize your visualizations with legends and tooltips, and extract click information for Shiny apps.

Advance Your Skills as an R Expert

0

The R language is one of the top two languages you need to learn if you want build the strongest career path possible in data science. (The other is Python.) After mastering the basics of R, take your skills in data science into highly valued areas of specialty with this learning path.Learn R in the context of the R tidyverse.Createdata visualizations and presentations.Developbusiness analytics skills at an advanced level in Excel.

Code Clinic: R

0

Practice coding with R. Explore common R programming challenges, and then compare the results with other programming languages in the Code Clinic series.Successful programmers know more than just how to code. They also know how to think about solving problems. Code Clinic is a series of courses where our instructors solve the same problems using different programming languages. Here, Mark Niemann-Ross works with R. Throughout the course, Mark introduces challenges and then provides an overview of his solutions in R. Challenges include topics such as statistical analysis and accessing peripheral devices. Visit other courses in the series to see how to solve the same challenges in languages like C++, C#, JavaScript, PHP, Python, Ruby, Go, and Swift.IntroductionWelcomeGetting the most from Code Clinic1. Problem One: Weather StatisticsIntroduction: The weather at Lake Pend OreilleOverview of linear models solutionImporting data from GitHubCalculating the coefficient of barometric pressurePlotting the pressure and fit line2. Problem Two: Where Am I?Introduction: Where am I?Overview of geolocation solutionRetrieving my latitude and longitudeMapping my location3. Problem Three: Eight QueensIntroduction: Eight queensOverview of eight queens solutionModeling the problem to exclude columnsSetting up to exclude rowsFiltering for diagonal conflictsEfficiently storing the resultsPlotting the result4. Problem Four: Musical InstrumentIntroduction: Accessing peripheralsOverview of accessing peripherals solutionUse Tcl/Tk and sonify( ) for a theremin5. Problem Five: Facial RecognitionIntroduction: Facial recognitionOverview of facial recognition solutionSending an image to Microsoft Cognitive Services Face APIRendering the image with face boxesSaving the results in JSON6. Problem Six: Real-Time Information DashboardIntroduction: Real-time information dashboardOverview of dashboard solutionUsing Shiny Server to serve data valuesUsing an R package for data and functionsUsing the Shiny client to render data

Linear Regression and Logistic Regression using R Studio

0

Linear Regression and Logistic Regression for beginners. Understand the difference between Regression & ClassificationWhat you'll learn:Learn how to solve real life problem using the Linear and Logistic Regression techniquePreliminary analysis of data using Univariate and Bivariate analysis before running regression analysisGraphically representing data in R before and after analysisHow to do basic statistical operations in RUnderstand how to interpret the result of Linear and Logistic Regression model and translate them into actionable insightIndepth knowledge of data collection and data preprocessing for Linear and Logistic Regression problemYou're looking for a complete Linear Regression and Logistic Regression course that teaches you everything you need to create a Linear or Logistic Regression model in R Studio, right?You've found the right Linear Regression course!After completing this course you will be able to:Identify the business problem which can be solved using linear and logistic regression technique of Machine Learning.Create a linear regression and logistic regression model in R Studio and analyze its result.Confidently practice, discuss and understand Machine Learning conceptsA Verifiable Certificate of Completion is presented to all students who undertake this Machine learning basics course.How this course will help you?If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you the most popular technique of machine learning, which is Linear RegressionWhy should you choose this course?This course covers all the steps that one should take while solving a business problem through linear regression.Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones:This is very good, i love the fact the all explanation given can be understood by a layman - JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. - DaisyOur PromiseTeaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message. Download Practice files, take Quizzes, and complete AssignmentsWith each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning. What is covered in this course? This course teaches you all the steps of creating a Linear Regression model, which is the most popular Machine Learning model, to solve business problems.Below are the course contents of this course on Linear Regression:Section 1 - Basics of StatisticsThis section is divided into five different lectures starting from types of data then types of statisticsthen graphical representations to describe the data and then a lecture on measures of center like meanmedian and mode and lastly measures of dispersion like range and standard deviationSection 2 - Python basicThis section gets you started with Python.This section will help you set up the python and Jupyter environment on your system and it'll teachyou how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.Section 3 - Introduction to Machine LearningIn this section we will learn - What does Machine Learning mean. What are the meanings or different terms associated with machine learning? You will see some examples so that you understand what machine learning actually is. It also contains steps involved in building a machine learning model, not just linear models, any machine learning model.Section 4 - Data PreprocessingIn this section you will learn what actions you need to take a step by step to get the data and thenprepare it for the analysis these steps are very important.We start with understanding the importance of business knowledge then we will see how to do data exploration. We learn how to do uni-variate analysis and bi-variate analysis then we cover topics like outlier treatment, missing value imputation, variable transformation and correlation.Section 5 - Regression ModelThis section starts with simple linear regression and then covers multiple linear regression.We have covered the basic theory behind each concept without getting too mathematical about it so that youunderstand where the concept is coming from and how it is important. But even if you don't understandit, it will be okay as long as you learn how to run and interpret the result as taught in the practical lectures.We also look at how to quantify models accuracy, what is the meaning of F statistic, how categorical variables in the independent variables dataset are interpreted in the results, what are other variations to the ordinary least squared method and how do we finally interpret the result to find out the answer to a business problem.By the end of this course, your confidence in creating a regression model in Python will soar. You'll have a thorough understanding of how to use regression modelling to create predictive models and solve business problems.Go ahead and click the enroll button, and I'll see you in lesson 1!CheersStart-Tech Academy------------Below is a list of popular FAQs of students who want to start their Machine learning journey-What is Machine Learning?Machine Learning is a field of computer science which gives the computer the ability to learn without being explicitly programmed. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.What is the Linear regression technique of Machine learning?Linear Regression is a simple machine learning model for regression problems, i.e., when the target variable is a real value.Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, that y can be calculated from a linear combination of the input variables (x).When there is a single input variable (x), the method is referred to as simple linear regression.When there are multiple input variables, the method is known as multiple linear regression.Why learn Linear regression technique of Machine learning?There are four reasons to learn Linear regression technique of Machine learning:1. Linear Regression is the most popular machine learning technique2. Linear Regression has fairly good prediction accuracy3. Linear Regression is simple to implement and easy to interpret4. It gives you a firm base to start learning other advanced techniques of Machine LearningHow much time does it take to learn Linear regression technique of machine learning?Linear Regression is easy but no one can determine the learning time it takes. It totally depends on you. The method we adopted to help you learn Linear regression starts from the basics and takes you to advanced level within hours. You can follow the same, but remember you can learn nothing without practicing it. Practice is the only way to remember whatever you have learnt. Therefore, we have also provided you with another data set to work on as a separate project of Linear regression.What are the steps I should follow to be able to build a Machine Learning model?You can divide your learning process into 4 parts:Statistics and Probability - Implementing Machine learning techniques require basic knowledge of Statistics and probability concepts. Second section of the course covers this part.Understanding of Machine learning - Fourth section helps you understand the terms and concepts associated with Machine learning and gives you the steps to be followed to build a machine learning modelProgramming Experience - A significant part of machine learning is programming. Python and R clearly stand out to be the leaders in the recent days. Third section will help you set up the Python environment and teach you some basic operations. In later sections there is a video on how to implement each concept taught in theory lecture in PythonUnderstanding of Linear Regression modelling - Having a good knowledge of Linear Regression gives you a solid understanding of how machine learning works. Even though Linear regression is the simplest technique of Machine learning, it is still the most popular one with fairly good prediction ability. Fifth and sixth section cover Linear regression topic end-to-end and with each theory lecture comes a corresponding practical lecture where we actually run each query with you.Why use Python for data Machine Learning?Understanding Python is one of the valuable skills needed for a career in Machine Learning.Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:In 2016, it overtook R on Kaggle, the premier platform for data science competitions.In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools.In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.Machine Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well.What is the difference between Data Mining, Machine Learning, and Deep Learning?Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions.Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.

Diversity, Inclusion, and Belonging

0

Diversity, inclusion, and belonging (DIBs) is the foundation for equitable workplaces. Learn how to activate DIBs to build a more diverse, innovative, and productive organization.

Multilevel Modeling

0

Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeIn this course, PhD candidates will get an introduction into the theory of multilevel modelling, focusing on two level multilevel models with a 'continuous' response variable. In addition, participants will learn how to run basic two-level model in R. The objective of this course is to get participants acquainted with multilevel models. These models are often used for the analysis of ‘hierarchical’ data, in which observations are nested within higher level units (e.g. repeated measures nested within individuals, or pupils nested within schools).In this type of data causes of outcomes (e.g. the performance of pupils in schools) are located both at the level of the individual (e.g., own and parental resources), and at a higher, contextual, level shared by some of the individuals (e.g. characteristics of the class and of the teacher). Because of this, the assumption of 'independent observations' is violated with hierarchical data, but multilevel modelling can easily account for that. Moreover, multilevel modelling can easily deal with missing data (in most circumstances).This course is designed and presented by Dr. Joran Jongerling on behalf of the Erasmus Graduate School of Social Sciences and the Humanities (EGSH, www.egsh.eur.nl) of the Erasmus University Rotterdam in the Netherlands. Should you have any questions about the organization or contents of the course, please send us an email at contact@egsh.eur.nl.

Groupthink: Understanding the Need for a Diverse Workplace

0

Explore how groupthink affects diversity and inclusionGroupthink is defined as ‘the practice of thinking or making decisions as a group, resulting typically in unchallenged, poor-quality decision-making’.With the creative industries being primarily dominated by young, white, middle to upper class, heterosexual males, groupthink can be a massive problem for the industry.On this two-week course from Livity, you’ll delve into the concept of groupthink, learning about its potentially negative consequences on the creative industry and how you might foster positive alternatives.Discuss whether groupthink can ever be positiveThere is a difference between groupthink and group thinking. In fact, there are many examples throughout history of groups coming together to affect positive change.You’ll explore the effects of groupthink, both negative and positive, with a focus on how it affects diversity in the workplace. Then, you’ll look at alternatives to groupthink and how an organisation can best transition to a more diverse approach to group thinking.Analyse the current state of diversity in the workplaceThe creative industry can’t expect to create inclusive work if it’s not inclusive. If research, development, and creative processes are not inclusive by design, we are not serving our customers and therefore cannot make the best work possible.This course will guide you through diversity in the workplace as it currently stands, from ethnic and gender diversity to socio-economic disparity. You’ll then discuss ways to shape an organisation’s structure to better avoid groupthink.Discover strategies to mitigate groupthinkAt the end of this course, you’ll learn how to create solutions to mitigate or foster different forms of groupthink on management, team, and individual levels, and then how to apply and encourage your methods.This course is designed for anyone wanting to contribute to and foster diversity in their processes, specifically within creative industries.You may find it especially useful if you’re looking to hire a diverse team, whether you’re the founder of a creative start-up, part of a HR team, or in a senior leadership position at a creative agency.

Build an Ethereum Blockchain App

0

What is blockchain technology, and where did it come from? Why are other people using it, and what can it do for you? This learning path introduces blockchain technologies and dives deep into Ethereum development.Explore the foundations of blockchain.Build a real Ethereum dapp.Discoverbest practices for Ethereum development.

Data Visualization in R with ggplot2

0

Discover how to create informative and visually appealing data visualizations using ggplot2, the leading visualization package for R.Discover how to create informative and visually appealing data visualizations using ggplot2, the leading visualization package for R. In this course, Mike Chapple shows how to work with ggplot2 to create basic visualizations, how to beautify those visualizations by applying different aesthetics, and how to visualize data with maps. Throughout the course, Mike also covers key concepts such as the grammar of graphics and how to apply different geometries to visualize data. To wrap up, he shares a case study that lends a practical context to the concepts covered in the course.

Diversity, Inclusion, and Belonging for Leaders and Managers

0

Discover ways to effectively lead diversity efforts in your organization. In this learning path, leaders can learn how to recognize the business need for DIBs, create a truly inclusive workplace,communicate honestly and effectively, recognize their own biases, and accept the differences of others.Identify the business case for DIBs.Learn to create an inclusive workplace.Find out how to communicate honestly and effectively.

Cultivating Diversity through Research and Insights

0

Learn to encourage diversity in creative researchCurrently, there is a severe lack of diversity and inclusion in research and development practices, which makes it difficult for the creative industry to truly represent the people who might use their products.On this two-week course from Livity, you’ll develop your understanding of what diversity looks like, specifically within creative research, and how to create processes that foster diversity in your work.Discover how non-diverse research affects creative processesResearch that accurately represents society without bias seems like the obvious option. But there are many examples of non-diverse research, across all industries, that has lead to unrepresentative results, many of which were costly and even dangerous.This course will guide you through how non-diverse research affects the research and creative processes, using a number of real-world examples to highlight the risks in both the medical and creative industries.Explore why empowerment works better than attacking biasesDuring the second week of this course, you’ll discover why empowering diversity in our development processes works better than attacking biases, exploring what empowerment is and, more importantly, what it isn’t.You’ll then delve into affirmative action and discuss whether it allows for genuine change or if it’s just a box-ticking exercise.Develop strategies and techniques for diverse researchThere are many small steps that you can take to improve the diversity in your research.Through this course, you’ll learn how to develop research with diversity baked in, delving into the negative effects of microaggressions and everyday bias. Then, you’ll finish by exploring how to develop a diverse mindset and use your learnings in your own research.This course is designed for anyone wanting to contribute to and foster diversity in their processes, specifically within creative industries.You may find it especially useful if you’re looking to hire a diverse team, whether you’re the founder of a creative start-up, part of a HR team, or in a senior leadership position at a creative agency.