This is a hands-on, guided project on Automatic Machine Learning with H2O AutoML and Python. By the end of this project, you will be able to describe what AutoML is and apply automatic machine learning to a business analytics problem with the H2O AutoML interface in Python. H2O's AutoML automates the process of training and tuning a large selection of models, allowing the user to focus on other aspects of the data science and machine learning pipeline such as data pre-processing, feature engineering and model deployment. To successfully complete the project, we recommend that you have prior experience in Python programming, basic machine learning theory, and have trained ML models with a library such as scikit-learn. We will not be exploring how any particular model works nor dive into the math behind them. Instead, we assume you have this foundational knowledge and want to learn to use H2O's AutoML interface for automatic machine learning. 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.

Welcome! In this course learners will develop expertise in basic magnetic resonance imaging (MRI) physics and principles and gain knowledge of many different data acquisition strategies in MRI. In particular, learners will get to know what is magnetic resonance phenomenon, how magnetic resonance signals are generated, how an image can be formulated using MRI, how soft tissue contrast can change with imaging parameters. Also introduced will be MR imaging sequences of spin echo, gradient echo, fast spin echo, echo planar imaging, inversion recovery, etc.

This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. The hands-on section of this course focuses on using best practices for unsupervised learning. By the end of this course you should be able to: Explain the kinds of problems suitable for Unsupervised Learning approaches Explain the curse of dimensionality, and how it makes clustering difficult with many features Describe and use common clustering and dimensionality-reduction algorithms Try clustering points where appropriate, compare the performance of per-cluster models Understand metrics relevant for characterizing clusters Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Unsupervised Machine Learning techniques in a business setting.   What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.

This course is the final course in a three part algebra sequence, In this course, students extend their knowledge of more advanced functions, and apply and model them using both algebraic and geometric techniques. This course enables students to make logical deductions and arrive at reasonable conclusions. Such skills are crucial in today's world. Knowing how to analyze quantitative information for the purpose of making decisions, judgments, and predictions is essential for understanding many important social and political issues. Quantitative Skills and Reasoning provides students the skills needed for evaluating such quantitatively-based arguments. This class is important as the mathematical ideas it treats and the mathematical language and symbolic manipulation it uses to express those ideas are essential for students who will progress to calculus, statistics, or data science.

In this capstone project we’ll combine all of the skills from all four specialization courses to do something really fun: analyze social networks! The opportunities for learning are practically endless in a social network. Who are the “influential” members of the network? What are the sub-communities in the network? Who is connected to whom, and by how many links? These are just some of the questions you can explore in this project. We will provide you with a real-world data set and some infrastructure for getting started, as well as some warm up tasks and basic project requirements, but then it’ll be up to you where you want to take the project. If you’re running short on ideas, we’ll have several suggested directions that can help get your creativity and imagination going. Finally, to integrate the skills you acquired in course 4 (and to show off your project!) you will be asked to create a video showcase of your final product.

In this 1-hour long project-based course, you will learn how to set up a Google Colab notebook, source data from the internet, load data into Python, merge two datasets, clean data, perform exploratory data analysis, carry out ANOVA and create boxplots. Throughout the course you will work on an Education dataset from World Bank. This will allow you to perform statistical analysis on your own datasets in Python. This project does not require any previous Python or coding experience, but it would be useful for learners to understand the statistical methods covered. The course includes data sourcing and cleaning which are invaluable real world skills, and focuses on visualizing your results which is needed as a large part of any analysis is the storytelling.

Organisations all around the world are using data to predict behaviours and extract valuable real-world insights to inform decisions. Managing and analysing big data has become an essential part of modern finance, retail, marketing, social science, development and research, medicine and government. This MOOC, designed by an academic team from Goldsmiths, University of London, will quickly introduce you to the core concepts of Data Science to prepare you for intermediate and advanced Data Science courses. It focuses on the basic mathematics, statistics and programming skills that are necessary for typical data analysis tasks. You will consider these fundamental concepts on an example data clustering task, and you will use this example to learn basic programming skills that are necessary for mastering Data Science techniques. During the course, you will be asked to do a series of mathematical and programming exercises and a small data clustering project for a given dataset.

In this 1-hour long project-based course, you will learn the basic building blocks in the R language and how to Develop an R program that constructs reverse, complement, and reverse-complement nucleic acid sequences (DNA, RNA). Also, you will make your code read a file that has a long DNA sequence and deal with one of the complete genomes for the novel coronavirus.

This is a Google Cloud Self-Paced Lab. In this lab, you will learn how to utilize custom fields in Looker Explores queries. Looker provides the ability for non-developer users to create and utilize ad hoc fields for richer data analysis. This is done by creating custom measures, custom dimensions, table calculations and using custom groupings to narrow down data to match specific conditions. Using ad hoc fields gives non-developers the ability to create new fields, as opposed to regular fields, which require that you have development permissions and understand LookML allowing them to have more flexibility in finding data they are looking for independently.

In this project-based course, you will learn the fundamentals of sentiment analysis, and build a logistic regression model to classify movie reviews as either positive or negative. We will use the popular IMDB data set. Our goal is to use a simple logistic regression estimator from scikit-learn for document classification. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and scikit-learn pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - 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.