This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will learn how to start a managed Spark/Hadoop cluster using Dataproc, submit a sample Spark job, and shut down your cluster using the Google Cloud Console.

This is a self-paced lab that takes place in the Google Cloud console. Vertex AI brings together the Google Cloud services for building ML under one, unified UI and API. In Vertex AI, you can now easily train and compare models using AutoML or custom code training and all your models are stored in one central model repository. These models can now be deployed to the same endpoints on Vertex AI. AutoML Vision helps anyone with limited Machine Learning (ML) expertise train high quality image classification models. In this hands-on lab, you will learn how to produce a custom ML model that automatically recognizes damaged car parts. Once you’ve produced your ML model, it’ll be immediately available for use. You can use the UI or the REST API to start generating predictions directly from the Google Cloud Console.

In this 1.03-hour long project-based course, you will learn to add LABELS to variables, use FORMATS to enhance outputs, regroup values using FORMATS, discover more on FORMAT RANGES and store your FORMATS in a FORMAT LIBRARY. 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.

Interested in increasing your knowledge of the Big Data landscape? This course is for those new to data science and interested in understanding why the Big Data Era has come to be. It is for those who want to become conversant with the terminology and the core concepts behind big data problems, applications, and systems. It is for those who want to start thinking about how Big Data might be useful in their business or career. It provides an introduction to one of the most common frameworks, Hadoop, that has made big data analysis easier and more accessible -- increasing the potential for data to transform our world! At the end of this course, you will be able to: * Describe the Big Data landscape including examples of real world big data problems including the three key sources of Big Data: people, organizations, and sensors. * Explain the V’s of Big Data (volume, velocity, variety, veracity, valence, and value) and why each impacts data collection, monitoring, storage, analysis and reporting. * Get value out of Big Data by using a 5-step process to structure your analysis. * Identify what are and what are not big data problems and be able to recast big data problems as data science questions. * Provide an explanation of the architectural components and programming models used for scalable big data analysis. * Summarize the features and value of core Hadoop stack components including the YARN resource and job management system, the HDFS file system and the MapReduce programming model. * Install and run a program using Hadoop! This course is for those new to data science. No prior programming experience is needed, although the ability to install applications and utilize a virtual machine is necessary to complete the hands-on assignments. Hardware Requirements: (A) Quad Core Processor (VT-x or AMD-V support recommended), 64-bit; (B) 8 GB RAM; (C) 20 GB disk free. How to find your hardware information: (Windows): Open System by clicking the Start button, right-clicking Computer, and then clicking Properties; (Mac): Open Overview by clicking on the Apple menu and clicking “About This Mac.” Most computers with 8 GB RAM purchased in the last 3 years will meet the minimum requirements.You will need a high speed internet connection because you will be downloading files up to 4 Gb in size. Software Requirements: This course relies on several open-source software tools, including Apache Hadoop. All required software can be downloaded and installed free of charge. Software requirements include: Windows 7+, Mac OS X 10.10+, Ubuntu 14.04+ or CentOS 6+ VirtualBox 5+.

Training & Visualizing a Decision Tree ,predicting and checking sensitivity

Welcome to Predictive Modeling, Model Fitting, and Regression Analysis. In this course, we will explore different approaches in predictive modeling, and discuss how a model can be either supervised or unsupervised. We will review how a model can be fitted, trained and scored to apply to both historical and future data in an effort to address business objectives. Finally, this course includes a hands-on activity to develop a linear regression model.

Hello everyone and welcome to this new hands-on project on ML classification with AWS AutoGluon. In this project, we will train several machine learning classifiers to detect and classify disease using a super powerful library known as AutoGluon. AutoGluon is the library behind Amazon Web Services (AWS) autopilot and it allows for quick prototyping of several powerful models using a few lines of code.

If I Googled you, what would I find? As we move around the online world we leave tracks and traces of our activity all the time: social media accounts, tagged images, professional presences, scraps of text, but also many artefacts we don't always realise we are leaving behind, or that others leave about us. In this course you will hear from a range of experts and you will have an opportunity to explore and reflect on your own online tracks and traces, to understand why your digital footprint is important. We will introduce you to some of the tools and approaches to effectively manage your online presence (or digital footprint). The course will focus on the different dimensions of a digital footprint, including developing an effective online presence, managing your privacy, creating opportunities for networking, balancing and managing professional and personal presences (eprofessionalism). By the end of this course (MOOC) you should be equipped to ensure that your digital footprint works for you, whether you want to be more private online, or are looking to create a more effective and impactful presence. You can also join the conversation on Twitter using the hashtag #DFMOOC and follow us @DFMOOC We hope you enjoy the course!

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will explore leveraging Cloud Error Reporting to understand the error reports of an application and application issues.

In this 1-hour long project-based course, you will create an end-to-end Topic model using PyCaret a low-code Python open-source Machine Learning library. You will learn how to automate the major steps for preprocessing, building, evaluating and deploying Machine Learning Models for Topic . Here are the main steps you will go through: frame the problem, get and prepare the data, discover and visualize the data, create the transformation pipeline, build, evaluate, interpret and deploy the model. This guided project is for seasoned Data Scientists who want to build a accelerate the efficiency in building POC and experiments by using a low-code library. It is also for Citizen data Scientists (professionals working with data) by using the low-code library PyCaret to add machine learning models to the analytics toolkit In order to be successful in this project, you should be familiar with Python and the basic concepts on 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.