This course will cover the steps used in weighting sample surveys, including methods for adjusting for nonresponse and using data external to the survey for calibration. Among the techniques discussed are adjustments using estimated response propensities, poststratification, raking, and general regression estimation. Alternative techniques for imputing values for missing items will be discussed. For both weighting and imputation, the capabilities of different statistical software packages will be covered, including R®, Stata®, and SAS®.

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 is designed to quite literally ‘make a science’ out of something at the heart of society: social networks. Humans are natural network scientists, as we compute new network configurations all the time, almost unaware, when thinking about friends and family (which are particular forms of social networks), about colleagues and organizational relations (other, overlapping network structures), and about how to navigate delicate or opportunistic network configurations to save guard or advance in our social standing (with society being one big social network itself). While such network structures always existed, computational social science has helped to reveal and to study them more systematically. In the first part of the course we focus on network structure. This looks as static snapshots of networks, which can be intricate and reveal important aspects of social systems. In our hands-on lab, you will also visualize and analyze a network with a software yourself, which will help to appreciate the complexity social networks can take on. During the second part of the course, we will look at how networks evolve in time. We ask how we can predict what kind of network will form and if and how we could influence network dynamics.

By the end of this course, learners will understand what computer vision is, as well as its mission of making computers see and interpret the world as humans do, by learning core concepts of the field and receiving an introduction to human vision capabilities. They are equipped to identify some key application areas of computer vision and understand the digital imaging process. The course covers crucial elements that enable computer vision: digital signal processing, neuroscience and artificial intelligence. Topics include color, light and image formation; early, mid- and high-level vision; and mathematics essential for computer vision. Learners will be able to apply mathematical techniques to complete computer vision tasks. This course is ideal for anyone curious about or interested in exploring the concepts of computer vision. It is also useful for those who desire a refresher course in mathematical concepts of computer vision. Learners should have basic programming skills and experience (understanding of for loops, if/else statements), specifically in MATLAB (Mathworks provides the basics here: https://www.mathworks.com/learn/tutorials/matlab-onramp.html). Learners should also be familiar with the following: basic linear algebra (matrix vector operations and notation), 3D co-ordinate systems and transformations, basic calculus (derivatives and integration) and basic probability (random variables). Material includes online lectures, videos, demos, hands-on exercises, project work, readings and discussions. Learners gain experience writing computer vision programs through online labs using MATLAB* and supporting toolboxes. * A free license to install MATLAB for the duration of the course is available from MathWorks.

This course is an introduction to sequence models and their applications, including an overview of sequence model architectures and how to handle inputs of variable length. • Predict future values of a time-series • Classify free form text • Address time-series and text problems with recurrent neural networks • Choose between RNNs/LSTMs and simpler models • Train and reuse word embeddings in text problems You will get hands-on practice building and optimizing your own text classification and sequence models on a variety of public datasets in the labs we’ll work on together. Prerequisites: Basic SQL, familiarity with Python and TensorFlow

This is a self-paced lab that takes place in the Google Cloud console. In this lab you use Machine Learning (ML) to analyze the public NCAA dataset and predict NCAA tournament brackets.

Welcome to this project-based course Introduction to EDA in R. In this project, you will learn how to perform extensive exploratory data analysis on both quantitative and qualitative variables using basic R functions. By the end of this 2-hour long project, you will understand how to create different basic plots in R. Also, you will learn how to create plots for categorical variables and numeric or quantitative variables. By extension, you will learn how to plot three variables and save your plot as an image in R. Note, you do not need to be a data scientist to be successful in this guided project, just a familiarity with basic statistics and using R suffice for this project. If you are not familiar with R and want to learn the basics, start with my previous guided projects titled “Getting Started with R” and “Calculating Descriptive Statistics in R”

By the end of this project, you will learn to use R lpSolveAPI. You will learn to: # Formulate Line Balancing Problem & Determine Objective Function # Apply Constraints On Tasks Assignment To Stations # Apply The Sum Of Durations Constraints On Tasks # Apply Task Precedence Relationship Constraints # Run Optimiser, Obtain & Analyse Solution

In this 2-hour long project-based course, you will learn how to train an Object Detection Model using Facebook's Detectron2. Detectron2 is a research platform and a production library for deep learning, built by Facebook AI Research (FAIR). We will be building an Object Detection Language Identification Model to identify English and Hindi texts written which can be extended to different use cases. We will look at the entire cycle of Model Development and Evaluation in Detectron2. We will first look at how to load a dataset, visualize it and prepare it as an input to the Deep Learning Model. We will then look at how we can build a Faster R-CNN model in Detectron2 and customize it. We will then configure the parameters & hyperparameters of the model. We will then move on to training the Model and subsequently to model inference and evaluation. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

In this project you will learn about several visualization techniques and their importance for Statistical Analysis. The project demonstrates different plotting techniques, for example, histograms, scatter plots, box and whiskers plot, violin plot, bar plot, addition of regression line to scatter plot, and creating matrix of multiple plots. It also discusses the suitability of each plots according to the data type of the variables and illustrates multiple ways to achieve the desired plots efficiently. The project refers to 'Palmer Penguins' data set for the illustrative purpose.