Name: AWS Certified Solutions Architect Associate (SAA-C03)
Price: 261.99 GBP
Availability: InStock

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			<h4>Overview</h4>		</div>
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							<p><span>By enroling in Develop Big Data Pipelines with R &amp; Sparklyr &amp; Tableau, you can kickstart your vibrant career and strengthen your profound knowledge. You can learn everything you need to know about the topic.</span></p><p><span>The Develop Big Data Pipelines with R &amp; Sparklyr &amp; Tableau course includes all of the most recent information to keep you abreast of the employment market and prepare you for your future. The curriculum for this excellent Develop Big Data Pipelines with R &amp; Sparklyr &amp; Tableau course includes modules at all skill levels, from beginner to expert. You will have the productivity necessary to succeed in your organisation once you have completed our Develop Big Data Pipelines with R &amp; Sparklyr &amp; Tableau Program.</span></p><p><span>So enrol in our Develop Big Data Pipelines with R &amp; Sparklyr &amp; Tableau course right away if you're keen to envision yourself in a rewarding career.</span></p>						</div>
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			<h4>Description</h4>		</div>
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							<p><span>Enroling in this Develop Big Data Pipelines with R &amp; Sparklyr &amp; Tableau course can improve your Develop Big Data Pipelines with R &amp; Sparklyr &amp; Tableau perspective, regardless of your skill levels in the Develop Big Data Pipelines with R &amp; Sparklyr &amp; Tableau topics you want to master. If you're already a Develop Big Data Pipelines with R &amp; Sparklyr &amp; Tableau expert, this peek under the hood will provide you with suggestions for accelerating your learning, including advanced Develop Big Data Pipelines with R &amp; Sparklyr &amp; Tableau insights that will help you make the most of your time. This Develop Big Data Pipelines with R &amp; Sparklyr &amp; Tableau course will act as a guide for you if you've ever wished to excel at Develop Big Data Pipelines with R &amp; Sparklyr &amp; Tableau.</span></p>						</div>
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			<h4>Why Choose Us?</h4>		</div>
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										<span>This course is accredited by the CPD Quality Standards.</span>
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										<span>Lifetime access to the whole collection of the learning materials.</span>
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										<span>Online test with immediate results.</span>
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										<span>Enroling in the course has no additional cost.</span>
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										<span>You can study and complete the course at your own pace.</span>
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										<span>Study for the course using any internet-connected device, such as a computer, tablet, or mobile device.</span>
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			<h4>Certificate of Achievement</h4>		</div>
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							<p>Upon successful completion, you will qualify for the UK and internationally-recognised CPD certificate and you can choose to make your achievement formal by obtaining your PDF Certificate at a cost of £4.99 and Hardcopy Certificate for £9.99.</p>						</div>
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			<h4>Who Is This Course For?</h4>		</div>
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							<p><span>This Develop Big Data Pipelines with R &amp; Sparklyr &amp; Tableau course is a great place to start if you're looking to start a new career in Develop Big Data Pipelines with R &amp; Sparklyr &amp; Tableau field. This training is for anyone interested in gaining in-demand Develop Big Data Pipelines with R &amp; Sparklyr &amp; Tableau proficiency to help launch a career or their business aptitude. </span></p>						</div>
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			<h4>Requirements</h4>		</div>
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							<p>The Develop Big Data Pipelines with R &amp; Sparklyr &amp; Tableau course requires no prior degree or experience. All you require is English proficiency, numeracy literacy and a gadget with stable internet connection. Learn and train for a prosperous career in the thriving and fast-growing industry of Develop Big Data Pipelines with R &amp; Sparklyr &amp; Tableau, without any fuss.</p>						</div>
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			<h4>Career Path</h4>		</div>
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							<p><span>This Develop Big Data Pipelines with R &amp; Sparklyr &amp; Tableau training will assist you develop your Develop Big Data Pipelines with R &amp; Sparklyr &amp; Tableau ability, establish a personal brand, and present a portfolio of relevant talents. It will help you articulate a  Develop Big Data Pipelines with R &amp; Sparklyr &amp; Tableau professional story and personalise your path to a new career. Furthermore, developing this Develop Big Data Pipelines with R &amp; Sparklyr &amp; Tableau skillset can lead to numerous opportunities for high-paying jobs in a variety of fields.</span></p>						</div>
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			<h4>Order Your Certificate</h4>		</div>
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							<div><div><div><div id="1693981461.590199"><div><div><div><div><div><div><div><div><div><div><div>To order CPD Quality Standard Certificate, we kindly invite you to visit the following link:</div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>						</div>
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	<span>Course Curriculum</span>
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					<td colspan="4">Section 01: Introduction</td>
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					<td>Introduction</td>
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					<td><span><i></i> 00:12:00</span></td>
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					<td colspan="4">Section 02: Setup And Installations</td>
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					<td>Installing Apache Spark</td>
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					<td><span><i></i> 00:12:00</span></td>
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					<td>Installing Java (Optional)</td>
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					<td><span><i></i> 00:05:00</span></td>
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					<td>Testing Apache Spark Installation</td>
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					<td><span><i></i> 00:03:00</span></td>
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					<td>Installing Sparklyr</td>
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					<td><span><i></i> 00:07:00</span></td>
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					<td colspan="4">Section 03: Building The Big Data ETL Pipeline With Sparklyr</td>
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					<td>Data Extraction</td>
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					<td><span><i></i> 00:06:00</span></td>
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					<td>Data Transformation</td>
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					<td><span><i></i> 00:18:00</span></td>
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					<td>Data Exporting</td>
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					<td><span><i></i> 00:07:00</span></td>
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					<td colspan="4">Section 04: Big Data Machine Learning With Sparklyr</td>
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					<td><span><i></i> 00:18:00</span></td>
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					<td><span><i></i> 00:10:00</span></td>
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					<td>Creating the Prediction Dataset</td>
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					<td><span><i></i> 00:10:00</span></td>
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					<td><span><i></i> 00:02:00</span></td>
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					<td><span><i></i> 00:12:00</span></td>
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					<td>Creating a Bar Chart</td>
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					<td><span><i></i> 00:08:00</span></td>
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					<td>Creating a Donut Chart</td>
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					<td><span><i></i> 00:15:00</span></td>
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					<td>Creating the Magnitude Chart</td>
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					<td><span><i></i> 00:09:00</span></td>
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					<td>Creating the Dashboard</td>
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					<td><span><i></i> 00:15:00</span></td>
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NextGen Learning

Develop Big Data Pipelines with R & Sparklyr & Tableau Course

General Admission

<div id="course-pills-1">Duration<p>5 Days</p><p>30 CPD hours</p>This course is intended for<p>This course is intended for anyone responsible for conf iguring, maintaining, and troubleshooting Symantec Data Loss Prevention. Additionally, this course is intended for technical users responsible for creating and maintaining Symantec Data Loss Prevention policies and the incident response structure.</p>Overview<p>At the completion of the course, you will be able to: Enforce server, detection servers, and DLP Agents as well as reporting, workflow, incident response management, policy management and detection, response management, user and role administration, directory integration, and filtering.</p><p>This course is designed to provide you with the fundamental know ledge to configure and administer the Symantec Data Loss Prevention Enforce platform.</p></div><div id="course-pills-2"><h4>Introduction to Symantec Data Loss Prevention</h4><ul><li>Symantec Data Loss Prevention overview<br />Symantec Data Loss Prevention architecture</li></ul><h4>Navigation and Reporting</h4><ul><li>Navigating the user interface<br />Reporting and analysis<br />Report navigation, preferences, and features<br />Report filters<br />Report commands<br />Incident snapshot<br />Incident Data Access<br />Hands-on labs: Become familiar with navigation and tools in the user interface. Create, filter, summarize, customize, and distribute reports. Create users, roles, and attributes.</li></ul><h4>Incident Remediation and Workflow</h4><ul><li>Incident remediation and w orkf low<br />Managing users and attributes<br />Custom attribute lookup<br />User Risk Summary<br />Hands-on labs: Remediate incidents and configure a user?s reporting preferences</li></ul><h4>Policy Management</h4><ul><li>Policy overview<br />Creating policy groups<br />Using policy templates<br />Building policies<br />Policy development best practices<br />Hands-on labs: Use policy templates and policy builder to configure and apply new policies</li></ul><h4>Response Rule Management</h4><ul><li>Response rule overview<br />Configuring Automated Response rules<br />Configuring Smart Response rules<br />Response rule best practices<br />Hands-On Labs: Create and use Automated and Smart Response rules</li></ul><h4>Described Content Matching</h4><ul><li>DCM detection methods<br />Hands-on labs: Create policies that include DCM and then use those policies to capture incidents</li></ul><h4>Exact Data Matching and Directory Group Matching</h4><ul><li>Exact data matching (EDM)<br />Advanced EDM<br />Directory group matching (DGM)<br />Hands-on labs: Create policies that include EDM and DGM, and then use those policies to capture incident</li></ul><h4>Indexed Document Matching</h4><ul><li>Indexed document matching (IDM)<br />Hands-on labs: Create policies that include IDM rules and then use those policies to capture incidents</li></ul><h4>Vector Machine Learning</h4><ul><li>Vector Machine Learning (VML)<br />Hands-on labs: Create a VML profile, import document sets, and create a VML policy</li></ul><h4>Network Monitor</h4><ul><li>Review of Network Monitor<br />Protocols<br />Traffic filtering<br />Network Monitor best practices<br />Hands-On Labs: Apply IP and L7 filters</li></ul><h4>Network Prevent</h4><ul><li>Network Prevent overview<br />Introduction to Network Prevent (Email)<br />Introduction to Network Prevent (Web)<br />Hands-On Labs: Configure Network Prevent (E-mail) response rules, incorporate them into policies, and use the policies to capture incidents</li></ul><h4>Mobile Email Monitor and Mobile Prevent</h4><ul><li>Introduction to Mobile Email Monitor<br />Mobile Prevent overview<br />Configuration<br />VPN configuration<br />Policy and Response Rule Creation<br />Reporting and Remediation<br />Troubleshooting</li></ul><h4>Network Discover and Network Protect</h4><ul><li>Network Discover and Network Protect overview<br />Configuring Discover targets<br />Configuring Box cloud targets<br />Protecting data<br />Auto-discovery of servers and shares<br />Running and managing scans<br />Reports and remediation<br />Network Discover and Network Protect best practices<br />Hands-on labs: Create and run a filesystem target using various response rules, including quarantining</li></ul><h4>Endpoint Prevent</h4><ul><li>Endpoint Prevent overview<br />Detection capabilities at the Endpoint<br />Configuring Endpoint Prevent<br />Creating Endpoint response rules<br />Viewing Endpoint Prevent incidents<br />Endpoint Prevent best practices<br />Managing DLP Agents<br />Hands-on labs: Create Agent Groups and Endpoint response rules, monitor and block Endpoint actions, view Endpoint incidents, and use the Enforce console to manage DLP Agents</li></ul><h4>Endpoint Discover</h4><ul><li>Endpoint Discover overview<br />Creating and running Endpoint Discover targets<br />Using Endpoint Discover reports and reporting features<br />Hands-on labs: Create Endpoint Discover targets, run Endpoint Discover targets, and view Endpoint Discover incidents</li></ul><h4>Enterprise Enablement</h4><ul><li>Preparing for risk reduction<br />Risk reduction<br />DLP Maturity model</li></ul><h4>System Administration</h4><ul><li>Server administration<br />Language support<br />Incident Delete<br />Credential management<br />Troubleshooting<br />Diagnostic tools<br />Troubleshooting scenario<br />Getting support<br />Hands-on labs: Interpret event reports and traffic reports, configure alerts, and use the Log Collection and Configuration tool</li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans Symantec Data Loss Prevention 14.0 - Administration training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the Symantec Data Loss Prevention 14.0 - Administration course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

Nexus Human

CompTIA Network+

Network & Security

Cyber Security

ASIS Certified Protection Professional (CPP)

Certified in Data Protection (CDP)

CompTIA Security+

Symantec Data Loss Prevention 14.0 - Administration

Packt

PostgreSQL

Data Science

This complete training program comprising levels 1 and 2 is divided into multi-sequential sections to let you gain comprehensive knowledge and understanding of SQL and data analysis. This two-level course is designed to kickstart your SQL knowledge and perform various data analysis tasks and implement SQL in projects independently and efficiently.

Master SQL for Data Analysis

<div id="course-pills-1">Duration<p>1 Days</p><p>6 CPD hours</p>This course is intended for<p>This course is intended for:<br />Data platform engineers<br />Architects and operators who build and manage data analytics pipelines</p>Overview<p>In this course, you will learn to:<br />Compare the features and benefits of data warehouses, data lakes, and modern data architectures<br />Design and implement a batch data analytics solution<br />Identify and apply appropriate techniques, including compression, to optimize data storage<br />Select and deploy appropriate options to ingest, transform, and store data<br />Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case<br />Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights<br />Secure data at rest and in transit<br />Monitor analytics workloads to identify and remediate problems<br />Apply cost management best practices</p><p>In this course, you will learn to build batch data analytics solutions using Amazon EMR, an enterprise-grade Apache Spark and Apache Hadoop managed service. You will learn how Amazon EMR integrates with open-source projects such as Apache Hive, Hue, and HBase, and with AWS services such as AWS Glue and AWS Lake Formation. The course addresses data collection, ingestion, cataloging, storage, and processing components in the context of Spark and Hadoop. You will learn to use EMR Notebooks to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon EMR.</p></div><div id="course-pills-2"><h4>Module A: Overview of Data Analytics and the Data Pipeline</h4><ul><li>Data analytics use cases</li><li>Using the data pipeline for analytics</li></ul><h4>Module 1: Introduction to Amazon EMR</h4><ul><li>Using Amazon EMR in analytics solutions</li><li>Amazon EMR cluster architecture</li><li>Interactive Demo 1: Launching an Amazon EMR cluster</li><li>Cost management strategies</li></ul><h4>Module 2: Data Analytics Pipeline Using Amazon EMR: Ingestion and Storage</h4><ul><li>Storage optimization with Amazon EMR</li><li>Data ingestion techniques</li></ul><h4>Module 3: High-Performance Batch Data Analytics Using Apache Spark on Amazon EMR</h4><ul><li>Apache Spark on Amazon EMR use cases</li><li>Why Apache Spark on Amazon EMR</li><li>Spark concepts</li><li>Interactive Demo 2: Connect to an EMR cluster and perform Scala commands using the Spark shell</li><li>Transformation, processing, and analytics</li><li>Using notebooks with Amazon EMR</li><li>Practice Lab 1: Low-latency data analytics using Apache Spark on Amazon EMR</li></ul><h4>Module 4: Processing and Analyzing Batch Data with Amazon EMR and Apache Hive</h4><ul><li>Using Amazon EMR with Hive to process batch data</li><li>Transformation, processing, and analytics</li><li>Practice Lab 2: Batch data processing using Amazon EMR with Hive</li><li>Introduction to Apache HBase on Amazon EMR</li></ul><h4>Module 5: Serverless Data Processing</h4><ul><li>Serverless data processing, transformation, and analytics</li><li>Using AWS Glue with Amazon EMR workloads</li><li>Practice Lab 3: Orchestrate data processing in Spark using AWS Step Functions</li></ul><h4>Module 6: Security and Monitoring of Amazon EMR Clusters</h4><ul><li>Securing EMR clusters</li><li>Interactive Demo 3: Client-side encryption with EMRFS</li><li>Monitoring and troubleshooting Amazon EMR clusters</li><li>Demo: Reviewing Apache Spark cluster history</li></ul><h4>Module 7: Designing Batch Data Analytics Solutions</h4><ul><li>Batch data analytics use cases</li><li>Activity: Designing a batch data analytics workflow</li></ul><h4>Module B: Developing Modern Data Architectures on AWS</h4><ul><li>Modern data architectures</li></ul></div>

Business Analytics & Intelligence

Data Engineering

Excel Analytics

Big Data Analytics

Azure Machine Learning

AWS (Amazon Web Services)

Building Batch Data Analytics Solutions on AWS

Kubernetes

Data Visualization

Data Analysis

Elasticsearch and Elastic Stack are important tools for managing massive data. You need to know the problems it solves and how it works to design the best systems and be the most valuable engineer you can be. Explore Elasticsearch 8 and learn to manage operations on your Elastic Stack with this comprehensive course. This course covers it all, from installation to operations.

Elasticsearch 8 and the Elastic Stack: In-Depth and Hands-On

<strong>Gain proficiency in the mastery of Geographic Information Systems (GIS) via ArcGIS Desktop</strong>
        <p>This course teaches you the most important aspects of spatial data analysis using ArcGIS. It is highly beneficial for both those wanting to learn spatial data analysis and/or gain familiarity with the ArcGIS ecosystem

First, we'll start some basic GIS tasks like 'Zooming'. Then, we'll move into more complex processing tasks like 'Geo-Statistics'. We'll also deal with some theoretical concepts related to Spatial Data Analysis, and then we'll focus on implementing some of the most common GIS techniques (all the way showing you how to execute these tasks in ArcGIS Desktop).

The stuff you'll learn from this course will be extremely useful in terms of you being able to implement it on future Spatial Data projects you'll be working on. The course is a hands-on training with real data. It's a step by step course covering both the theory and application of Spatial Data Analysis. This is one of the most comprehensive courses on this topic.  

All the code and supporting files for this course are available at -
https://github.com/PacktPublishing/ArcGIS-Desktop-For-Spatial-Analysis-Go-From-Basic-To-Pro</p>
        <h2>What You Will Learn</h2><p>You will have a thorough knowledge of the ArcGIS Interface and Functionality<br>You will learn about the different toolboxes in ArcGIS and what they can do<br>You can carry out practical spatial data analysis tasks, geoprocessing and mapping using ArcGIS Desktop<br>You learn about advanced topics such as geostatistics and carry them out on actual data<br>You visualize and map spatial data</p>
        <h2>Audience</h2><p>The target audience is the people looking to get started in the field of GIS Analysis use ArcGIS Desktop in academic or professional settings and students should have access to ArcGIS Desktop. You should have prior knowledge of basic spatial data related concepts such as the different data types and coordinate systems.</p>
        <h2>Approach</h2><p>The approach is full of hands-on instructions via video-lectures, interesting and illustrative examples, clear explanations. Each video lecture will teach you a new concept that you can apply to your own data.</p>
        <h2>Key Features</h2><p>The course will teach you to implement some of the most important spatial data analysis techniques in ArcGIS Desktop * You learn to work with real spatial data * Carry our practical map making to display the results of spatial analysis * Learn about advanced topics such as geostatistics and carry them out on actual data</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/arcgis-desktop-for-spatial-analysis-go-from-basic-to-pro</p>
        <h2>About the Author</h2><h3>Minerva Singh</h3><p>Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a part-time Data Scientist. As part of her research, she must carry out extensive data analysis, including spatial data analysis. For this purpose, she prefers to use a combination of freeware tools: R, QGIS, and Python. She does most of her spatial data analysis work using R and QGIS. Apart from being free, these are very powerful tools for data visualization, processing, and analysis. She also holds an MPhil degree in Geography and Environment from Oxford University. She has honed her statistical and data analysis skills through several MOOCs, including The Analytics Edge and Statistical. In addition to spatial data analysis, she is also proficient in statistical analysis, machine learning, and data mining.</p>
        <h2>Course Outline</h2><h4>1. Welcome to Spatial Data Analysis and Visualization</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What is this Course About and Meet the Instructor</p><p style='margin: 0'>Welcome to Spatial Data Analysis and Visualization: What is this Course About and Meet the Instructor</p></td></tr><tr><td style='padding: 15px;'><p>2. Some Spatial Data Analysis Related Concepts</p><p style='margin: 0'>Welcome to Spatial Data Analysis and Visualization: Some Spatial Data Analysis Related Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. Conclusion to Section 1</p><p style='margin: 0'>Welcome to Spatial Data Analysis and Visualization: Conclusion to Section 1</p></td></tr></tbody></table><br><h4>2. Introduction to ArcGIS Desktop and its Functionality</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What is ArcGIS Desktop?</p><p style='margin: 0'>Introduction to ArcGIS Desktop and its Functionality: What is ArcGIS Desktop?</p></td></tr><tr><td style='padding: 15px;'><p>2. Accessing and Displaying Spatial Data in ArcGIS</p><p style='margin: 0'>Introduction to ArcGIS Desktop and its Functionality: Accessing and Displaying Spatial Data in ArcGIS</p></td></tr><tr><td style='padding: 15px;'><p>3. Finding and identifying Features and Values in ArcGIS Desktop</p><p style='margin: 0'>Introduction to ArcGIS Desktop and its Functionality: Finding and identifying Features and Values in ArcGIS Desktop</p></td></tr><tr><td style='padding: 15px;'><p>4. Making Selections in ArcGIS</p><p style='margin: 0'>Introduction to ArcGIS Desktop and its Functionality: Making Selections in ArcGIS</p></td></tr><tr><td style='padding: 15px;'><p>5. Zooming and Measuring</p><p style='margin: 0'>Introduction to ArcGIS Desktop and its Functionality: Zooming and Measuring</p></td></tr><tr><td style='padding: 15px;'><p>6. Calculate Shapefile Geometry in ArcGIS</p><p style='margin: 0'>Introduction to ArcGIS Desktop and its Functionality: Calculate Shapefile Geometry in ArcGIS</p></td></tr><tr><td style='padding: 15px;'><p>7. Working With Different Coordinate Systems in ArcGIS</p><p style='margin: 0'>Introduction to ArcGIS Desktop and its Functionality: Working With Different Coordinate Systems in ArcGIS</p></td></tr><tr><td style='padding: 15px;'><p>8. Conclusion to Section 2</p><p style='margin: 0'>Introduction to ArcGIS Desktop and its Functionality: Conclusion to Section 2</p></td></tr></tbody></table><br><h4>3. Working with Raster Data in ArcGIS Desktop</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Displaying Raster Data in ArcGIS</p><p style='margin: 0'>Working with Raster Data in ArcGIS Desktop: Displaying Raster Data in ArcGIS</p></td></tr><tr><td style='padding: 15px;'><p>2. Merge Rasters in ArcGIS</p><p style='margin: 0'>Working with Raster Data in ArcGIS Desktop: Merge Rasters in ArcGIS</p></td></tr><tr><td style='padding: 15px;'><p>3. Band Arithmetic in ArcGIS Desktop</p><p style='margin: 0'>Working with Raster Data in ArcGIS Desktop: Band Arithmetic in ArcGIS Desktop</p></td></tr><tr><td style='padding: 15px;'><p>4. Reclassify a Raster in ArcGIS Desktop</p><p style='margin: 0'>Working with Raster Data in ArcGIS Desktop: Reclassify a Raster in ArcGIS Desktop</p></td></tr><tr><td style='padding: 15px;'><p>5. Resample a Raster in ArcGIS</p><p style='margin: 0'>Working with Raster Data in ArcGIS Desktop: Resample a Raster in ArcGIS</p></td></tr><tr><td style='padding: 15px;'><p>6. Topographic Data Products-Brief Theoretical Introduction</p><p style='margin: 0'>Working with Raster Data in ArcGIS Desktop: Topographic Data Products-Brief Theoretical Introduction</p></td></tr><tr><td style='padding: 15px;'><p>7. Topographic Calculations in ArcGIS Desktop</p><p style='margin: 0'>Working with Raster Data in ArcGIS Desktop: Topographic Calculations in ArcGIS Desktop</p></td></tr><tr><td style='padding: 15px;'><p>8. Clip a Raster to the Extent of a Polygon in ArcGIS Desktop</p><p style='margin: 0'>Working with Raster Data in ArcGIS Desktop: Clip a Raster to the Extent of a Polygon in ArcGIS Desktop</p></td></tr><tr><td style='padding: 15px;'><p>9. Zonal Statistics</p><p style='margin: 0'>Working with Raster Data in ArcGIS Desktop: Zonal Statistics</p></td></tr><tr><td style='padding: 15px;'><p>10. Extract Raster Values to Points</p><p style='margin: 0'>Working with Raster Data in ArcGIS Desktop: Extract Raster Values to Points</p></td></tr><tr><td style='padding: 15px;'><p>11. Geo-referencing a Raster in ArcGIS Desktop</p><p style='margin: 0'>Working with Raster Data in ArcGIS Desktop: Geo-referencing a Raster in ArcGIS Desktop</p></td></tr><tr><td style='padding: 15px;'><p>12. Conclusion to Section 3</p><p style='margin: 0'>Working with Raster Data in ArcGIS Desktop: Conclusion to Section 3</p></td></tr></tbody></table><br><h4>4. Working with Vector Data in ArcGIS Desktop</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Exploratory Data Analysis with Shapefile Attributes in ArcGIS Desktop</p><p style='margin: 0'>Working with Vector Data in ArcGIS Desktop: Exploratory Data Analysis with Shapefile Attributes in ArcGIS Desktop</p></td></tr><tr><td style='padding: 15px;'><p>2. Basic Graphing of Shapefile Attributes in ArcGIS Desktop</p><p style='margin: 0'>Working with Vector Data in ArcGIS Desktop: Basic Graphing of Shapefile Attributes in ArcGIS Desktop</p></td></tr><tr><td style='padding: 15px;'><p>3. Spatial Join with Tabular Data</p><p style='margin: 0'>Working with Vector Data in ArcGIS Desktop: Spatial Join with Tabular Data</p></td></tr><tr><td style='padding: 15px;'><p>4. Spatial Joins in ArcGIS Based on Spatial Location</p><p style='margin: 0'>Working with Vector Data in ArcGIS Desktop: Spatial Joins in ArcGIS Based on Spatial Location</p></td></tr><tr><td style='padding: 15px;'><p>5. Create Choropleth Maps</p><p style='margin: 0'>Working with Vector Data in ArcGIS Desktop: Create Choropleth Maps</p></td></tr><tr><td style='padding: 15px;'><p>6. Split Shapefiles Using ArcGIS</p><p style='margin: 0'>Working with Vector Data in ArcGIS Desktop: Split Shapefiles Using ArcGIS</p></td></tr><tr><td style='padding: 15px;'><p>7. Clip Shapefile in ArcGIS</p><p style='margin: 0'>Working with Vector Data in ArcGIS Desktop: Clip Shapefile in ArcGIS</p></td></tr><tr><td style='padding: 15px;'><p>8. Intersection of Shapefiles in ArcGIS</p><p style='margin: 0'>Working with Vector Data in ArcGIS Desktop: Intersection of Shapefiles in ArcGIS</p></td></tr><tr><td style='padding: 15px;'><p>9. Add Buffer Areas to Shapefiles in ArcGIS Desktop</p><p style='margin: 0'>Working with Vector Data in ArcGIS Desktop: Add Buffer Areas to Shapefiles in ArcGIS Desktop</p></td></tr><tr><td style='padding: 15px;'><p>10. Merge and Append Shapefiles in ArcGIS Desktop</p><p style='margin: 0'>Working with Vector Data in ArcGIS Desktop: Merge and Append Shapefiles in ArcGIS Desktop</p></td></tr><tr><td style='padding: 15px;'><p>11. Dissolve Shapefiles in ArcGIS</p><p style='margin: 0'>Working with Vector Data in ArcGIS Desktop: Dissolve Shapefiles in ArcGIS</p></td></tr><tr><td style='padding: 15px;'><p>12. Conclusion to Section 4</p><p style='margin: 0'>Working with Vector Data in ArcGIS Desktop: Conclusion to Section 4</p></td></tr></tbody></table><br><h4>5. More Advanced Geoprocessing Tasks in ArcGIS: Intro to Basic Geostatistics</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Euclidean Distance Raster Generation in ArcGIS Desktop</p><p style='margin: 0'>More Advanced Geoprocessing Tasks in ArcGIS: Intro to Basic Geostatistics: Euclidean Distance Raster Generation in ArcGIS Desktop</p></td></tr><tr><td style='padding: 15px;'><p>2. Density Analysis in ArcGIS Desktop</p><p style='margin: 0'>More Advanced Geoprocessing Tasks in ArcGIS: Intro to Basic Geostatistics: Density Analysis in ArcGIS Desktop</p></td></tr><tr><td style='padding: 15px;'><p>3. Compute Line Density in ArcGIS</p><p style='margin: 0'>More Advanced Geoprocessing Tasks in ArcGIS: Intro to Basic Geostatistics: Compute Line Density in ArcGIS</p></td></tr><tr><td style='padding: 15px;'><p>4. Compute Point Density in ArcGIS</p><p style='margin: 0'>More Advanced Geoprocessing Tasks in ArcGIS: Intro to Basic Geostatistics: Compute Point Density in ArcGIS</p></td></tr><tr><td style='padding: 15px;'><p>5. Kernel Density for Linear and Point Features in ArcGIS</p><p style='margin: 0'>More Advanced Geoprocessing Tasks in ArcGIS: Intro to Basic Geostatistics: Kernel Density for Linear and Point Features in ArcGIS</p></td></tr><tr><td style='padding: 15px;'><p>6. Interpolation of Point Data-Theory</p><p style='margin: 0'>More Advanced Geoprocessing Tasks in ArcGIS: Intro to Basic Geostatistics: Interpolation of Point Data-Theory</p></td></tr><tr><td style='padding: 15px;'><p>7. Interpolation of Point Data-Application</p><p style='margin: 0'>More Advanced Geoprocessing Tasks in ArcGIS: Intro to Basic Geostatistics: Interpolation of Point Data-Application</p></td></tr><tr><td style='padding: 15px;'><p>8. Mapping Spatial Clusters in ArcGIS Desktop-Theory</p><p style='margin: 0'>More Advanced Geoprocessing Tasks in ArcGIS: Intro to Basic Geostatistics: Mapping Spatial Clusters in ArcGIS Desktop-Theory</p></td></tr><tr><td style='padding: 15px;'><p>9. Mapping Spatial Clusters in ArcGIS Desktop-Application</p><p style='margin: 0'>More Advanced Geoprocessing Tasks in ArcGIS: Intro to Basic Geostatistics: Mapping Spatial Clusters in ArcGIS Desktop-Application</p></td></tr><tr><td style='padding: 15px;'><p>10. Geographically Weighted Regression in ArcGIS-Theory</p><p style='margin: 0'>More Advanced Geoprocessing Tasks in ArcGIS: Intro to Basic Geostatistics: Geographically Weighted Regression in ArcGIS-Theory</p></td></tr><tr><td style='padding: 15px;'><p>11. Geographically Weighted Regression in ArcGIS-Application</p><p style='margin: 0'>More Advanced Geoprocessing Tasks in ArcGIS: Intro to Basic Geostatistics: Geographically Weighted Regression in ArcGIS-Application</p></td></tr><tr><td style='padding: 15px;'><p>12. Model Builder for Automating Geoprocessing</p><p style='margin: 0'>More Advanced Geoprocessing Tasks in ArcGIS: Intro to Basic Geostatistics: Model Builder for Automating Geoprocessing</p></td></tr><tr><td style='padding: 15px;'><p>13. Conclusion to Section 5</p><p style='margin: 0'>More Advanced Geoprocessing Tasks in ArcGIS: Intro to Basic Geostatistics: Conclusion to Section 5</p></td></tr></tbody></table><br><h4>6. Make Simple Maps in ArcGIS</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Examples of ArcGIS Maps</p><p style='margin: 0'>Make Simple Maps in ArcGIS: Examples of ArcGIS Maps</p></td></tr><tr><td style='padding: 15px;'><p>2. Presenting Spatial Data As a Layer File</p><p style='margin: 0'>Make Simple Maps in ArcGIS: Presenting Spatial Data As a Layer File</p></td></tr><tr><td style='padding: 15px;'><p>3. Using Base Maps provided by ArcGIS to Provide Context to Our Map</p><p style='margin: 0'>Make Simple Maps in ArcGIS: Using Base Maps provided by ArcGIS to Provide Context to Our Map</p></td></tr><tr><td style='padding: 15px;'><p>4. Putting Together a Map</p><p style='margin: 0'>Make Simple Maps in ArcGIS: Putting Together a Map</p></td></tr><tr><td style='padding: 15px;'><p>5. Conclusion to Section 6</p><p style='margin: 0'>Make Simple Maps in ArcGIS: Conclusion to Section 6</p></td></tr></tbody></table>

Programming

Gain proficiency in the mastery of Geographic Information Systems (GIS) via ArcGIS Desktop

ArcGIS Desktop for Spatial Analysis: Go from Basic to Pro

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										<span>Learn the basics of Python and the simple Python syntax</span>
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										<span>Acquire in-depth knowledge of different data types </span>
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										<span>Increase your skills in lambda, generators, classes and many more</span>
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										<span>Equip yourself with the necessary skills in Python and launch your career as a programmer</span>
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			<h2>Description </h2>		</div>
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				<p><span>Python is one of the most popular programming languages all over the globe, including in the UK. So, whether you want to be a web developer or a software engineer, without having excellent expertise in Python you can not thrive in your respective field. Are you worried to start from the beginning? Then don't worry, the Python 3 for Beginners course will educate you on basic-level Python programming.</span></p><p><span>This course will provide you with a detailed understanding of Python language. In this comprehensive training, you will learn about different data types and simple Python syntax. The course will also educate you on functions objects and variadic functions. Furthermore, you will grasp a clear understanding of object initialization, lambda and much more.</span></p><p><span>Therefore, sign up for our most user-friendly, easy-to-digest and flexible course and complete it to get the CPD- certificate of achievement. This certificate will help you grab the attention of employers.</span></p>					</div>
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				<p><span>After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for 9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for 15.99, which will reach your doorsteps by post. </span></p>					</div>
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				<p><span>When you are done with your learning, you will get some assessment questions that need to pass with a score of 60%. After submitting the assessment, you will immediately receive feedback from our experts. </span></p>					</div>
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				<p><span>After completing this course, you will get the chance to explore jobs in the following fields:</span></p>					</div>
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				<h3>Course Content</h3>

<div id="course_curriculum">
<table width="100%">				<tbody><tr>
					<td colspan="4">Module :01</td>
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					<td><i></i></td>
					<td>A Installing Python</td>
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					<td><span><i></i> 00:17:00</span></td>
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					<td><span><i></i> 00:30:00</span></td>
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					<td>Command Line</td>
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					<td><span><i></i> 00:17:00</span></td>
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					<td>Variables</td>
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					<td><span><i></i> 00:29:00</span></td>
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					<td>Simple Python Syntax</td>
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					<td><span><i></i> 00:15:00</span></td>
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					<td><span><i></i> 00:18:00</span></td>
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					<td><span><i></i> 00:17:00</span></td>
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					<td><span><i></i> 00:23:00</span></td>
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					<td colspan="4">Module :02</td>
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					<td>If Elif Else</td>
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					<td><span><i></i> 00:31:00</span></td>
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					<td>Iterable</td>
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					<td><span><i></i> 00:10:00</span></td>
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					<td><span><i></i> 00:11:00</span></td>
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					<td><span><i></i> 00:20:00</span></td>
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					<td>Execute</td>
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					<td><span><i></i> 00:05:00</span></td>
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					<td>Exceptions</td>
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					<td><span><i></i> 00:18:00</span></td>
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					<td>Data Types</td>
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					<td><span><i></i> 00:24:00</span></td>
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					<td colspan="4">Module :03</td>
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					<td>Number Types</td>
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					<td><span><i></i> 00:28:00</span></td>
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					<td>More Number Types</td>
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					<td><span><i></i> 00:13:00</span></td>
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					<td>Strings</td>
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					<td><span><i></i> 00:20:00</span></td>
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					<td>More Strings</td>
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					<td><span><i></i> 00:11:00</span></td>
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					<td><span><i></i> 00:08:00</span></td>
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					<td>Lists</td>
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					<td><span><i></i> 00:15:00</span></td>
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					<td>Dictionaries</td>
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					<td><span><i></i> 00:04:00</span></td>
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					<td>Tuples</td>
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					<td><span><i></i> 00:07:00</span></td>
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					<td>Sets</td>
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					<td><span><i></i> 00:09:00</span></td>
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					<td colspan="4">Module :04</td>
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					<td><i></i></td>
					<td>Comprehensions</td>
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					<td><span><i></i> 00:10:00</span></td>
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					<td>Definitions</td>
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					<td><span><i></i> 00:02:00</span></td>
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					<td>Functions</td>
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					<td><span><i></i> 00:06:00</span></td>
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					<td>Final Exam- Python 3 for Beginners</td>
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					<td colspan="4">Order your Certificates &amp; Transcripts	</td>
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					<td>Order your Certificates &amp; Transcripts</td>
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			</div>
			<h2>Frequently Asked Questions</h2>
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                                    Are there any prerequisites for taking the course?
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                                        There are no specific prerequisites for this course, nor
                                        are there any formal entry requirements. All you need is
                                        an internet connection, a good understanding of English
                                        and a passion for learning for this course.
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                                <span>
                                    Can I access the course at any time, or is there a set
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                                        You have the flexibility to access the course at any time
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                                    How long will I have access to the course?
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                                        For this course, you will have access to the course
                                        materials for 1 year only. This means you can review the
                                        content as often as you like within the year, even after
                                        you've completed the course. However, if you buy Lifetime
                                        Access for the course, you will be able to access the
                                        course for a lifetime.
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                                    Is there a certificate of completion provided after
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                                        Yes, upon successfully completing the course, you will
                                        receive a certificate of completion. This certificate can
                                        be a valuable addition to your professional portfolio and
                                        can be shared on your various social networks.
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                                        We want you to have a positive learning experience. If
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                                        If you encounter technical issues or content-related
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Janets

Python 3 for Beginners

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p>Candidates should be familiar with Dynamics 365 Customer Insights and have firsthand experience with one or more additional Dynamics 365 apps, Power Query, Microsoft Dataverse, Common Data Model, and Microsoft Power Platform. They should also have working knowledge of practices related to privacy, compliance, consent, security, responsible AI, and data retention policy.</p>Overview<p>After completing this course, you will be able to:<br />Clean, transform, and ingest data into Dynamics 365 Customer Insights<br />Create a unified customer profile<br />Work with Dynamics 365 Audience insights<br />Enrich data and predictions<br />Set up and manage external connections<br />Administer and monitor Customer Insights</p><p>Customer Data Platform specialists implement solutions that provide insight into customer profiles and that track engagement activities to help improve customer experiences and increase customer retention. In this course, students will learn about the Dynamics 365 Customer Insights solution, including how to unify customer data with prebuilt connectors, predict customer intent with rich segmentation, and maintain control of customer data. This specialty course starts with creating a unified profile and then working with customer data.</p></div><div id="course-pills-2"><h4>Module 1: Get started with Dynamics 365 Customer Insights</h4><ul><li>Introduction to the customer data platform</li><li>Administer Dynamics 365 Customer Insights</li><li>Explore user permissions in Dynamics 365 Customer Insights</li></ul><h4>Module 2: Ingest data into Dynamics 365 Customer Insights</h4><ul><li>Import and transform data</li><li>Connect to data sources</li><li>Work with data</li></ul><h4>Module 3: Create a unified customer profile in Dynamics 365 Customer Insights</h4><ul><li>Map data</li><li>Match data</li><li>Merge data</li><li>Find customers</li></ul><h4>Module 4: Work with Dynamics 365 Customer Insights</h4><ul><li>Explore Audience insights</li><li>Define relationships and activities</li><li>Work with measures</li><li>Work with segments</li></ul><h4>Module 5: Enrich data and predictions with Audience insights</h4><ul><li>Enrich data</li><li>Use predictions</li><li>Use machine learning models</li></ul><h4>Module 6: Manage external connections with Customer Data Platform</h4><ul><li>Export Customer Insights data</li><li>Use Customer Insights with Microsoft Power Platform</li><li>Display Customer Insights data in Dynamics 365 apps</li><li>More ways to extend Customer Insights</li></ul></div>

Microsoft 365

Microsoft Power Platform

Microsoft AZ-104

Microsoft AZ-303

Microsoft AZ-700

Microsoft Dynamics

MB-260T00: Microsoft Customer Data Platform Specialty

John Academy

Microsoft AZ-900

AWS Certified Cloud Practitioner

Microsoft Azure

AZ-900 | Microsoft Azure Fundamentals Full Course

Web Development

Python

Prepare for the AWS Certified Solutions Architect - Associate (SAA-C03) exam. Learn about the AWS Management Console, S3 buckets, instances, database services, cloud security, costs associated with AWS, Amazon Elastic Compute Cloud (EC2), Amazon Virtual Private Cloud (VPC), Amazon Simple Storage Service (S3), and Amazon Elastic Block Store (EBS).

AWS Certified Solutions Architect Associate (SAA-C03)

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p></p><p>This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.</p><p></p>Overview<p>Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.</p><p></p><p>Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow.</p><p></p>Prerequisites<p></p><li>Creating cloud resources in Microsoft Azure.</li>  <li>Using Python to explore and visualize data.</li>  <li>Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.</li>  <li>Working with containers</li>  <li>AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience.</li><p></p></div><div id="course-pills-2"><h3>1 - Design a data ingestion strategy for machine learning projects</h3> <ul> <li>Identify your data source and format</li> <li>Choose how to serve data to machine learning workflows</li> <li>Design a data ingestion solution</li> </ul> <h3>2 - Design a machine learning model training solution</h3> <ul> <li>Identify machine learning tasks</li> <li>Choose a service to train a machine learning model</li> <li>Decide between compute options</li> </ul> <h3>3 - Design a model deployment solution</h3> <ul> <li>Understand how model will be consumed</li> <li>Decide on real-time or batch deployment</li> </ul> <h3>4 - Design a machine learning operations solution</h3> <ul> <li>Explore an MLOps architecture</li> <li>Design for monitoring</li> <li>Design for retraining</li> </ul> <h3>5 - Explore Azure Machine Learning workspace resources and assets</h3> <ul> <li>Create an Azure Machine Learning workspace</li> <li>Identify Azure Machine Learning resources</li> <li>Identify Azure Machine Learning assets</li> <li>Train models in the workspace</li> </ul> <h3>6 - Explore developer tools for workspace interaction</h3> <ul> <li>Explore the studio</li> <li>Explore the Python SDK</li> <li>Explore the CLI</li> </ul> <h3>7 - Make data available in Azure Machine Learning</h3> <ul> <li>Understand URIs</li> <li>Create a datastore</li> <li>Create a data asset</li> </ul> <h3>8 - Work with compute targets in Azure Machine Learning</h3> <ul> <li>Choose the appropriate compute target</li> <li>Create and use a compute instance</li> <li>Create and use a compute cluster</li> </ul> <h3>9 - Work with environments in Azure Machine Learning</h3> <ul> <li>Understand environments</li> <li>Explore and use curated environments</li> <li>Create and use custom environments</li> </ul> <h3>10 - Find the best classification model with Automated Machine Learning</h3> <ul> <li>Preprocess data and configure featurization</li> <li>Run an Automated Machine Learning experiment</li> <li>Evaluate and compare models</li> </ul> <h3>11 - Track model training in Jupyter notebooks with MLflow</h3> <ul> <li>Configure MLflow for model tracking in notebooks</li> <li>Train and track models in notebooks</li> </ul> <h3>12 - Run a training script as a command job in Azure Machine Learning</h3> <ul> <li>Convert a notebook to a script</li> <li>Run a script as a command job</li> <li>Use parameters in a command job</li> </ul> <h3>13 - Track model training with MLflow in jobs</h3> <ul> <li>Track metrics with MLflow</li> <li>View metrics and evaluate models</li> </ul> <h3>14 - Perform hyperparameter tuning with Azure Machine Learning</h3> <ul> <li>Define a search space</li> <li>Configure a sampling method</li> <li>Configure early termination</li> <li>Use a sweep job for hyperparameter tuning</li> </ul> <h3>15 - Run pipelines in Azure Machine Learning</h3> <ul> <li>Create components</li> <li>Create a pipeline</li> <li>Run a pipeline job</li> </ul> <h3>16 - Register an MLflow model in Azure Machine Learning</h3> <ul> <li>Log models with MLflow</li> <li>Understand the MLflow model format</li> <li>Register an MLflow model</li> </ul> <h3>17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning</h3> <ul> <li>Understand Responsible AI</li> <li>Create the Responsible AI dashboard</li> <li>Evaluate the Responsible AI dashboard</li> </ul> <h3>18 - Deploy a model to a managed online endpoint</h3> <ul> <li>Explore managed online endpoints</li> <li>Deploy your MLflow model to a managed online endpoint</li> <li>Deploy a model to a managed online endpoint</li> <li>Test managed online endpoints</li> </ul> <h3>19 - Deploy a model to a batch endpoint</h3> <ul> <li>Understand and create batch endpoints</li> <li>Deploy your MLflow model to a batch endpoint</li> <li>Deploy a custom model to a batch endpoint</li> <li>Invoke and troubleshoot batch endpoints</li> </ul></div>

DP-100T01 Designing and Implementing a Data Science Solution on Azure

Welcome to the Bootcamp course. You will obtain a firm understanding of machine learning with this course. By doing so, you will be able to develop machine learning solutions for various challenges you might encounter and be prepared to start using machine learning at work or in technical interviews.

Python Machine Learning Bootcamp

Go from Beginner to Super Advance Level in Machine Learning Algorithms using Python and Mathematical Insights

Machine Learning with Real World Projects

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			<h2><b>Course Overview</b></h2>		</div>
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					<div><p><span>Machine learning as a programming technique has shaped the future of technology. In this course, you will learn how to build intelligent handwriting recognition apps from scratch using Python and Core ML.</span></p><p><span>The </span><b>Machine Learning for Apps Level 4 </b><span>course will teach you how to take advantage of machine learning to code like a pro and build incredible apps that can make predictions. Designed by industry experts, it covers best practices for managing projects, core concepts for creating your own ML model, building a convolutional neural network, and much more.</span></p><p><span>On successful completion, you will be able to build an amazing handwriting recognition app and convolutional neural network from scratch, and have an in-depth understanding of the core ML basics. This course is ideal for those with a basic understanding of iOS development.</span></p></div>
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					<div><p>This best selling <strong>Machine Learning for Apps Level 4 </strong>has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth <strong>Machine Learning for Apps Level 4 </strong>is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.</p><p>The <strong>Machine Learning for Apps Level 4 </strong>is CPD-accredited, so you can be confident you're completing a quality training course will boost your CV and enhance your career potential. The <strong>Machine Learning for Apps Level 4 </strong>is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn.</p><p>After successfully completing the<strong> Machine Learning for Apps Level 4</strong>, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the <strong>Machine Learning for Apps Level 4 </strong>will help you stand out from the crowd. You can also validate your certification on our website.</p><p>We know that you are busy and that time is precious, so we have designed the <strong>Machine Learning for Apps Level 4 </strong>to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device. </p><p>Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.</p></div>
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Course Cloud

Machine Learning (ML)

Machine Learning for Apps Level 4

<strong>The goal of this course is to use Python machine learning to create algorithms that you can use in the real world. You'll start with the basics of machine learning. You'll learn how to create, train, and optimize models and use these models in real-world applications.</strong>
        <p>Machine learning is a field of computer science through which you can create complex models that perform multiple functions using mathematical input. Python is a popular choice to create machine learning models due to a plethora of libraries easily accessible. This course takes you through this impressive combination of Python and machine learning, teaching you the basics of machine learning to create your own projects.

You'll begin learning about different types of machine learning models and how to choose the relevant ones for your project. You'll learn to optimize this model and apply performance metrics to track its performance. You'll also learn topics like regression, classification, and clustering to improve the performance of your model. You'll learn the basics of neural networks and use scikit-learn to perform calculations in your project.

By the end of this course, you'll have created a face recognition application using everything you've learned in this course.

The code bundle for this course is available at https://github.com/PacktPublishing/Python-Machine-Learning-Crash-Course-for-Beginners</p>
        <h2>What You Will Learn</h2><p>Train different types of machine learning models for your project<br>Prepare and clean data for your project<br>Optimize your machine learning model to best suit your project needs<br>Build your own machine learning model from scratch<br>Apply performance metrics to track the performance of your model<br>Use scikit-learn to perform calculations in your project</p>
        <h2>Audience</h2><p>This course is for Python developers who are new in the field of machine learning. No prior knowledge or experience of machine learning is required. A basic understanding of Python programming will be needed here.</p>
        <h2>Approach</h2><p>This is a hands-on course that takes you through all the machine learning concepts step-by-step while showing you real-world applications for each step.</p>
        <h2>Key Features</h2><p>Build a face recognition application from scratch * Prepare and train your data for your projects * Learn real-world applications of your algorithms</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Python-Machine-Learning-Crash-Course-for-Beginners</p>
        <h2>About the Author</h2><h3>AI Sciences</h3><p>AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.

AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences.

Their courses have successfully helped more than 100,000 students master AI and data science.</p>
        <h2>Course Outline</h2><h4>1. Introduction to the Course</h4><p></p><br><h4>2. Why Machine Learning</h4><p></p><br><h4>3. Process of Learning from Data</h4><p></p><br><h4>4. Machine Learning Methods</h4><p></p><br><h4>5. Data Preparation and Preprocessing</h4><p></p><br><h4>6. Machine Learning Models and Optimization</h4><p></p><br><h4>7. Building Machine Learning Model from Scratch</h4><p></p><br><h4>8. Overfitting, Underfitting and Generalization</h4><p></p><br><h4>9. Machine Learning Model Performance Metrics</h4><p></p><br><h4>10. Dimensionality Reduction</h4><p></p><br><h4>11. Deep Learning Overview</h4><p></p><br><h4>12. Hands-on Machine Learning Project Using Scikit-Learn</h4><p></p><br><h4>13. OPTIONAL Section- Mathematics Wrap-up</h4><p></p>

The goal of this course is to use Python machine learning to create algorithms that you can use in the real world. You'll start with the basics of machine learning. You'll learn how to create, train, and optimize models and use these models in real-world applications.

Python Machine Learning Crash Course for Beginners

<strong>Begin your machine learning journey by learning all about linear regression, logistic regression, and cluster analysis</strong>
        <p>Machine Learning is one of the fundamental skills you need to become a data scientist. It's the steppingstone that will help you understand deep learning and modern data analysis techniques. 

In this course, you'll explore the three fundamental machine learning topics - linear regression, logistic regression, and cluster analysis. Even neural networks geeks (like us) can't help but admit that it's these three simple methods that data science revolves around. So, in this course, we will make the otherwise complex subject matter easy to understand and apply in practice. This course supports statistics theory with practical application of these quantitative methods in Python to help you develop skills in the context of data science. 

We've developed this course with not one but two machine learning libraries: StatsModels and sklearn. You'll be eager to complete this course and get ready to become a successful data scientist! 

All the code and supporting files for this course are available at https://github.com/PacktPublishing/Machine-Learning-101-with-Scikit-learn-and-StatsModels</p>
        <h2>What You Will Learn</h2><p>Confidently work with two of the leading ML packages: statsmodels and sklearn <br>Understand how to perform a linear regression <br>Become familiar with the ins and outs of logistic regression <br>Get to grips with carrying out cluster analysis (both flat and hierarchical) <br>Apply your skills to real-life business cases <br>Get insights into the underlying ideas behind ML models</p>
        <h2>Audience</h2><p>If you want to get acquainted with fundamental machine learning methods, become a successful data scientist, or just get started with building valuable skills in machine learning and data science, this course is for you.</p>
        <h2>Approach</h2><p>The course has a perfect blend of theory and practical knowledge to help you gain a solid understand of the concepts covered.</p>
        <h2>Key Features</h2><p>Learn machine learning with StatsModels and sklearn  * Apply machine learning skills to solve real-world business cases  * Get started with linear regression, logistic regression, and cluster analysis</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/machine-learning-101-with-scikit-learn-and-statsmodels</p>
        <h2>About the Author</h2><h3>365 Careers Ltd.</h3><p>365 Careers' courses have been taken by more than 203,000 students in 204 countries. People working at world-class firms such as Apple, PayPal, and Citibank have completed 365 Careers trainings. By choosing 365 Careers, you make sure you will learn from proven experts who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time.

If you want to become a financial analyst, a finance manager, an FP&A analyst, an investment banker, a business executive, an entrepreneur, a business intelligence analyst, a data analyst, or a data scientist, 365 Careers' courses are the perfect place to start.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. Setting Up the Working Environment</h4><p></p><br><h4>3. Linear Regression with StatsModels</h4><p></p><br><h4>4. Linear Regression with Sklearn</h4><p></p><br><h4>5. Linear Regression - Practical Example</h4><p></p><br><h4>6. Logistic Regression</h4><p></p><br><h4>7. Cluster Analysis</h4><p></p><br><h4>8. Cluster Analysis: Additional Topics</h4><p></p>

Begin your machine learning journey by learning all about linear regression, logistic regression, and cluster analysis

Machine Learning 101 with Scikit-learn and StatsModels

<strong>In this course, you will learn Python fundamentals, and the concepts of the amazing pandas data science library needed to pre-process and prepare the data for machine learning algorithms.</strong>
        <p>Machine learning is one of the most exciting fields in the hi-tech industry, gaining momentum in various applications. Companies are looking for data scientists, data engineers, and machine language (ML) experts to develop products, features, and projects that will help them unleash the power of machine learning. This course will show you how to prepare data for machine learning algorithms using Python and pandas library.

The course starts by explaining the installation process of Anaconda and Jupyter. Once you are ready with the setup, you will understand Python fundamentals, such as variables, data types, conditional statements, loops, and modules. Next, you will go through the pandas library and learn how to use it for loading real-world large datasets. Towards the end, you will learn the steps and techniques to clean data and make it ready to move into machine learning algorithms.

By the end of this course, you will be well-versed with Python fundamentals and pandas library and will be ready to take on data science projects.

The code bundle for this course is available at https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners-Level-2</p>
        <h2>What You Will Learn</h2><p>Understand how to use Jupyter Lab for Jupyter notebooks<br>Learn Python fundamentals<br>Load large datasets from files using pandas<br>Uncover techniques to perform data analysis and exploration<br>Group, sort, export, select, filter, and clean data<br>Find out how to preview the data frame</p>
        <h2>Audience</h2><p>This course is designed for beginners looking to understand the theoretical side of machine learning and to enter the practical side of data science. You will understand the fundamentals of Python and learn how to pre-process data before using it in machine learning algorithms. There are no prerequisites for taking up this course; however, it is recommended to finish with the Machine Learning for Absolute Beginners - Level 1 course.</p>
        <h2>Approach</h2><p>With the help of exercises and activities, this course will show you how to use Python and pandas library for cleaning data before moving it into machine learning algorithms.</p>
        <h2>Key Features</h2><p>Learn how to use Python for data science and machine learning projects * Understand the interface of Jupyter lab tool * Discover how to get data ready for machine learning algorithms</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners-Level-2 </p>
        <h2>About the Author</h2><h3>Idan Gabrieli</h3><p>Idan Gabrieli has worked in various engineering positions in Israel's high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies.

Idan's expertise spans multiple domains, including cloud computing, machine learning, data science, and electronics. Since 2014, Idan has created and published online courses on various topics worldwide. Idan is recognized as a high-rated instructor by leading educational providers.

Idan simplifies complex technology and provides high-quality educational content with specific learning objectives that are well-structured, combining various multimedia teaching options.</p>
        <h2>Course Outline</h2><h4>1. Getting Started with Level 2!</h4><p></p><br><h4>2. Python Fundamentals for Data Science</h4><p></p><br><h4>3. Introduction to the Pandas Library</h4><p></p><br><h4>4. Loading Data into a DataFrame</h4><p></p><br><h4>5. Data Cleaning and Transformation</h4><p></p><br><h4>6. Course Summary</h4><p></p>

In this course, you will learn Python fundamentals, and the concepts of the amazing pandas data science library needed to pre-process and prepare the data for machine learning algorithms.

Machine Learning for Absolute Beginners - Level 2

<strong>Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning &amp; More!</strong>
        <p>Do you ever want to be a data scientist and build Machine Learning projects that can solve real-life problems? If yes, then this course is perfect for you.
You will train machine learning algorithms to classify flowers, predict house price, identify handwritings or digits, identify staff that is most likely to leave prematurely, detect cancer cells and much more!
Inside the course, you'll learn how to:
&bull;	Set up a Python development environment correctly
&bull;	Gain complete machine learning toolsets to tackle most real-world problems
&bull;	Understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them.
&bull;	Combine multiple models with by bagging, boosting or stacking
&bull;	Make use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data
&bull;	Develop in Jupyter (IPython) notebook, Spyder and various IDE
&bull;	Communicate visually and effectively with Matplotlib and Seaborn
&bull;	Engineer new features to improve algorithm predictions
&bull;	Make use of train/test, K-fold and Stratified K-fold cross-validation to select the correct model and predict model perform with unseen data
&bull;	Use SVM for handwriting recognition, and classification problems in general
&bull;	Use decision trees to predict staff attrition
&bull;	Apply the association rule to retail shopping datasets
&bull;	And much more!
By the end of this course, you will have a Portfolio of 12 Machine Learning projects that will help you land your dream job or enable you to solve real-life problems in your business, job or personal life with Machine Learning algorithms.</p>
        <h2>What You Will Learn</h2><p>&bull;&nbsp;&nbsp;Learn to Build Powerful Machine Learning Models to Solve Any Problem<br>&bull;&nbsp;&nbsp;Learn to Train machine learning algorithms to predict house prices, identify handwriting, detect cancer cells &amp; more</p>
        <h2>Audience</h2><p>A newbie who wants to learn machine learning algorithm with Python. Anyone who has a deep interest in the practical application of machine learning to real world problems. Anyone wishes to move beyond the basics and develop an understanding of the whole range of machine learning algorithms. Any intermediate to advanced EXCEL users who is unable to work with large datasets. Anyone interested to present their findings in a professional and convincing manner. Anyone who wishes to start or transit into a career as a data scientist. Anyone who wants to apply machine learning to their domain.</p>
        <h2>Approach</h2><p>You'll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen.</p>
        <h2>Key Features</h2><p>&bull;&nbsp;&nbsp;Solve any problem in your business or job with powerful Machine Learning models * &bull;&nbsp;&nbsp;Go from zero to hero in Python, Seaborn, Matplotlib, Scikit-Learn, SVM, and unsupervised Machine Learning etc.</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/the-complete-machine-learning-course-with-python</p>
        <h2>About the Author</h2><h3>Anthony Ng</h3><p>Anthony Ng has spent almost 10 years in the education sector covering topics such as algorithmic trading, financial data analytics, investment, and portfolio management and more. He has worked in various financial institutions and has assisted Quantopian to conduct Algorithmic Trading Workshops in Singapore since 2016. He has also presented in QuantCon Singapore 2016 and 2017. He is passionate about finance, data science and Python and enjoys researching, teaching and sharing knowledge. He holds a Master of Science in Financial Engineering from NUS Singapore and MBA and Bcom from Otago University.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What Does the Course Cover?</p><p style='margin: 0'>Introduction: What Does the Course Cover?</p></td></tr></tbody></table><br><h4>2. Getting Started with Anaconda</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. [Windows OS] Downloading & Installing Anaconda</p><p style='margin: 0'>Getting Started with Anaconda: [Windows OS] Downloading &amp; Installing Anaconda</p></td></tr><tr><td style='padding: 15px;'><p>2. [Windows OS] Managing Environment</p><p style='margin: 0'>Getting Started with Anaconda: [Windows OS] Managing Environment</p></td></tr><tr><td style='padding: 15px;'><p>3. Navigating the Spyder & Jupyter Notebook Interface</p><p style='margin: 0'>Getting Started with Anaconda: Navigating the Spyder &amp; Jupyter Notebook Interface</p></td></tr><tr><td style='padding: 15px;'><p>4. Downloading the IRIS Datasets</p><p style='margin: 0'>Getting Started with Anaconda: Downloading the IRIS Datasets</p></td></tr><tr><td style='padding: 15px;'><p>5. Data Exploration and Analysis</p><p style='margin: 0'>Getting Started with Anaconda: Data Exploration and Analysis</p></td></tr><tr><td style='padding: 15px;'><p>6. Presenting Your Data</p><p style='margin: 0'>Getting Started with Anaconda: Presenting Your Data</p></td></tr></tbody></table><br><h4>3. Regression</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Regression: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Categories of Machine Learning</p><p style='margin: 0'>Regression: Categories of Machine Learning</p></td></tr><tr><td style='padding: 15px;'><p>3. Working with Scikit-Learn</p><p style='margin: 0'>Regression: Working with Scikit-Learn</p></td></tr><tr><td style='padding: 15px;'><p>4. Boston Housing Data - EDA</p><p style='margin: 0'>Regression: Boston Housing Data - EDA</p></td></tr><tr><td style='padding: 15px;'><p>5. Correlation Analysis and Feature Selection</p><p style='margin: 0'>Regression: Correlation Analysis and Feature Selection</p></td></tr><tr><td style='padding: 15px;'><p>6. Simple Linear Regression Modelling with Boston Housing Data</p><p style='margin: 0'>Regression: Simple Linear Regression Modelling with Boston Housing Data</p></td></tr><tr><td style='padding: 15px;'><p>7. Robust Regression</p><p style='margin: 0'>Regression: Robust Regression</p></td></tr><tr><td style='padding: 15px;'><p>8. Evaluate Model Performance</p><p style='margin: 0'>Regression: Evaluate Model Performance</p></td></tr><tr><td style='padding: 15px;'><p>9. Multiple Regression with statsmodel</p><p style='margin: 0'>Regression: Multiple Regression with statsmodel</p></td></tr><tr><td style='padding: 15px;'><p>10. Multiple Regression and Feature Importance</p><p style='margin: 0'>Regression: Multiple Regression and Feature Importance</p></td></tr><tr><td style='padding: 15px;'><p>11. Ordinary Least Square Regression and Gradient Descent</p><p style='margin: 0'>Regression: Ordinary Least Square Regression and Gradient Descent</p></td></tr><tr><td style='padding: 15px;'><p>12. Regularised Method for Regression</p><p style='margin: 0'>Regression: Regularised Method for Regression</p></td></tr><tr><td style='padding: 15px;'><p>13. Polynomial Regression</p><p style='margin: 0'>Regression: Polynomial Regression</p></td></tr><tr><td style='padding: 15px;'><p>14. Dealing with Non-linear relationships</p><p style='margin: 0'>Regression: Dealing with Non-linear relationships</p></td></tr><tr><td style='padding: 15px;'><p>15. Feature Importance Revisited</p><p style='margin: 0'>Regression: Feature Importance Revisited</p></td></tr><tr><td style='padding: 15px;'><p>16. Data Pre-Processing 1</p><p style='margin: 0'>Regression: Data Pre-Processing 1</p></td></tr><tr><td style='padding: 15px;'><p>17. Data Pre-Processing 2</p><p style='margin: 0'>Regression: Data Pre-Processing 2</p></td></tr><tr><td style='padding: 15px;'><p>18. Variance Bias Trade Off - Validation Curve</p><p style='margin: 0'>Regression: Variance Bias Trade Off - Validation Curve</p></td></tr><tr><td style='padding: 15px;'><p>19. Variance Bias Trade Off - Learning Curve</p><p style='margin: 0'>Regression: Variance Bias Trade Off - Learning Curve</p></td></tr><tr><td style='padding: 15px;'><p>20. Cross Validation</p><p style='margin: 0'>Regression: Cross Validation</p></td></tr></tbody></table><br><h4>4. Classification</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Classification: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Logistic Regression 1</p><p style='margin: 0'>Classification: Logistic Regression 1</p></td></tr><tr><td style='padding: 15px;'><p>3. Logistic Regression 2</p><p style='margin: 0'>Classification: Logistic Regression 2</p></td></tr><tr><td style='padding: 15px;'><p>4. MNIST Project 1 - Introduction</p><p style='margin: 0'>Classification: MNIST Project 1 - Introduction</p></td></tr><tr><td style='padding: 15px;'><p>5. MNIST Project 2 - SGDClassifiers</p><p style='margin: 0'>Classification: MNIST Project 2 - SGDClassifier</p></td></tr><tr><td style='padding: 15px;'><p>6. MNIST Project 3 - Performance Measures</p><p style='margin: 0'>Classification: MNIST Project 3 - Performance Measures</p></td></tr><tr><td style='padding: 15px;'><p>7. MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score</p><p style='margin: 0'>Classification: MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score</p></td></tr><tr><td style='padding: 15px;'><p>8. MNIST Project 5 - Precision and Recall Tradeoff</p><p style='margin: 0'>Classification: MNIST Project 5 - Precision and Recall Tradeoff</p></td></tr><tr><td style='padding: 15px;'><p>9. MNIST Project 6 - The ROC Curve</p><p style='margin: 0'>Classification: MNIST Project 6 - The ROC Curve</p></td></tr></tbody></table><br><h4>5. Support Vector Machine (SVM)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Support Vector Machine (SVM): Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Support Vector Machine (SVM) Concepts</p><p style='margin: 0'>Support Vector Machine (SVM): Support Vector Machine (SVM) Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. Linear SVM Classification</p><p style='margin: 0'>Support Vector Machine (SVM): Linear SVM Classification</p></td></tr><tr><td style='padding: 15px;'><p>4. Polynomial Kernel</p><p style='margin: 0'>Support Vector Machine (SVM): Polynomial Kernel</p></td></tr><tr><td style='padding: 15px;'><p>5. Gaussian Radial Basis Function</p><p style='margin: 0'>Support Vector Machine (SVM): Gaussian Radial Basis Function</p></td></tr><tr><td style='padding: 15px;'><p>6. Support Vector Regression</p><p style='margin: 0'>Support Vector Machine (SVM): Support Vector Regression</p></td></tr><tr><td style='padding: 15px;'><p>7. Advantages and Disadvantages of SVM</p><p style='margin: 0'>Support Vector Machine (SVM): Advantages and Disadvantages of SVM</p></td></tr></tbody></table><br><h4>6. Tree</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Tree: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Decision Tree</p><p style='margin: 0'>Tree: What is Decision Tree</p></td></tr><tr><td style='padding: 15px;'><p>3. Training a Decision Tree</p><p style='margin: 0'>Tree: Training a Decision Tree</p></td></tr><tr><td style='padding: 15px;'><p>4. Visualising a Decision Trees</p><p style='margin: 0'>Tree: Visualising a Decision Trees</p></td></tr><tr><td style='padding: 15px;'><p>5. Decision Tree Learning Algorithm</p><p style='margin: 0'>Tree: Decision Tree Learning Algorithm</p></td></tr><tr><td style='padding: 15px;'><p>6. Decision Tree Regression</p><p style='margin: 0'>Tree: Decision Tree Regression</p></td></tr><tr><td style='padding: 15px;'><p>7. Overfitting and Grid Search</p><p style='margin: 0'>Tree: Overfitting and Grid Search</p></td></tr><tr><td style='padding: 15px;'><p>8. Where to From Here</p><p style='margin: 0'>Tree: Where to From Here</p></td></tr><tr><td style='padding: 15px;'><p>9. Project HR - Loading and preprocesing data</p><p style='margin: 0'>Tree: Project HR - Loading and preprocesing data</p></td></tr><tr><td style='padding: 15px;'><p>10. Project HR - Modelling</p><p style='margin: 0'>Tree: Project HR - Modelling</p></td></tr></tbody></table><br><h4>7. Ensemble Machine Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Ensemble Machine Learning: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Ensemble Learning Methods Introduction</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble Learning Methods Introduction</p></td></tr><tr><td style='padding: 15px;'><p>3. Bagging Part 1</p><p style='margin: 0'>Ensemble Machine Learning: Bagging Part 1</p></td></tr><tr><td style='padding: 15px;'><p>4. Bagging Part 2</p><p style='margin: 0'>Ensemble Machine Learning: Bagging Part 2</p></td></tr><tr><td style='padding: 15px;'><p>5. Random Forests</p><p style='margin: 0'>Ensemble Machine Learning: Random Forests</p></td></tr><tr><td style='padding: 15px;'><p>6. Extra-Trees</p><p style='margin: 0'>Ensemble Machine Learning: Extra-Trees</p></td></tr><tr><td style='padding: 15px;'><p>7. AdaBoost</p><p style='margin: 0'>Ensemble Machine Learning: Decision Tree Regression</p></td></tr><tr><td style='padding: 15px;'><p>8. Gradient Boosting Machine</p><p style='margin: 0'>Ensemble Machine Learning: Gradient Boosting Machine</p></td></tr><tr><td style='padding: 15px;'><p>9. XGBoost</p><p style='margin: 0'>Ensemble Machine Learning: XGBoost</p></td></tr><tr><td style='padding: 15px;'><p>10. Project HR - Human Resources Analytics</p><p style='margin: 0'>Ensemble Machine Learning: Project HR - Human Resources Analytics</p></td></tr><tr><td style='padding: 15px;'><p>11. Ensemble of ensembles Part 1</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble of ensembles Part 1</p></td></tr><tr><td style='padding: 15px;'><p>12. Ensemble of ensembles Part 2</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble of ensembles Part 2</p></td></tr></tbody></table><br><h4>8. k-Nearest Neighbours (kNN)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. kNN Introduction</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. kNN Concepts</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. kNN and Iris Dataset Demo</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN and Iris Dataset Demo</p></td></tr><tr><td style='padding: 15px;'><p>4. Distance Metric</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Distance Metric</p></td></tr><tr><td style='padding: 15px;'><p>5. Project Cancer Detection Part 1</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Project Cancer Detection Part 1</p></td></tr><tr><td style='padding: 15px;'><p>6. Project Cancer Detection Part 2</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Project Cancer Detection Part 2</p></td></tr></tbody></table><br><h4>9. Dimensionality Reduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Dimensionality Reduction: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Dimensionality Reduction Concept</p><p style='margin: 0'>Dimensionality Reduction: Dimensionality Reduction Concept</p></td></tr><tr><td style='padding: 15px;'><p>3. PCA Introduction</p><p style='margin: 0'>Dimensionality Reduction: PCA Introduction</p></td></tr><tr><td style='padding: 15px;'><p>4. Dimensionality Reduction Demo</p><p style='margin: 0'>Dimensionality Reduction: Dimensionality Reduction Demo</p></td></tr><tr><td style='padding: 15px;'><p>5. Project Wine 1: Dimensionality Reduction with PCA</p><p style='margin: 0'>Dimensionality Reduction: Project Wine 1: Dimensionality Reduction with PCA</p></td></tr><tr><td style='padding: 15px;'><p>6. Project Wine 2: Choosing the Number of Components</p><p style='margin: 0'>Dimensionality Reduction: Project Wine 2: Choosing the Number of Components</p></td></tr><tr><td style='padding: 15px;'><p>7. Kernel PCA</p><p style='margin: 0'>Dimensionality Reduction: Kernel PCA</p></td></tr><tr><td style='padding: 15px;'><p>8. Kernel PCA Demo</p><p style='margin: 0'>Dimensionality Reduction: Kernel PCA Demo</p></td></tr><tr><td style='padding: 15px;'><p>9. LDA & Comparison between LDA and PCA</p><p style='margin: 0'>Dimensionality Reduction: LDA &amp; Comparison between LDA and PCA</p></td></tr></tbody></table><br><h4>10. Unsupervised Learning: Clustering</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Unsupervised Learning: Clustering: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Clustering Concepts</p><p style='margin: 0'>Unsupervised Learning: Clustering: Clustering Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. MLextend</p><p style='margin: 0'>Unsupervised Learning: Clustering: MLextend</p></td></tr><tr><td style='padding: 15px;'><p>4. Ward's Agglomerative Hierarchical Clustering</p><p style='margin: 0'>Unsupervised Learning: Clustering: Ward's Agglomerative Hierarchical Clustering</p></td></tr><tr><td style='padding: 15px;'><p>5. Truncating Dendrogram</p><p style='margin: 0'>Unsupervised Learning: Clustering: Truncating Dendrogram</p></td></tr><tr><td style='padding: 15px;'><p>6. k-Means Clustering</p><p style='margin: 0'>Unsupervised Learning: Clustering: k-Means Clustering</p></td></tr><tr><td style='padding: 15px;'><p>7. Elbow Method</p><p style='margin: 0'>Unsupervised Learning: Clustering: Elbow Method</p></td></tr><tr><td style='padding: 15px;'><p>8. Silhouette Analysis</p><p style='margin: 0'>Unsupervised Learning: Clustering: Silhouette Analysis</p></td></tr><tr><td style='padding: 15px;'><p>9. Mean Shift</p><p style='margin: 0'>Unsupervised Learning: Clustering: Mean Shift</p></td></tr></tbody></table>

TensorFlow

Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning &amp; More!

The Complete Machine Learning Course with Python

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					<div><p>The comprehensive Machine Learning - Use in Flutter has been designed by industry experts to provide learners with everything they need to enhance their skills and knowledge in their chosen area of study. Enrol on the Machine Learning - Use in Flutter today, and learn from the very best the industry has to offer!</p></div>
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					<div><p>This best selling Machine Learning - Use in Flutter has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth Machine Learning - Use in Flutter is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.</p><p>The Machine Learning - Use in Flutter is CPD-accredited, so you can be confident you're completing a quality training course which will boost your CV and enhance your career potential. The Machine Learning - Use in Flutter is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn.</p><p>After successfully completing the Machine Learning - Use in Flutter, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the Machine Learning - Use in Flutter will help you stand out from the crowd. You can also validate your certification on our website.</p><p>We know that you are busy and that time is precious, so we have designed the Machine Learning - Use in Flutter to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device. </p><p>Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.</p></div>
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Learning & Development

Learning To Learn

Machine Learning - Use in Flutter

<strong>This course will take you through the fundamental concepts of machine learning (ML) and artificial intelligence (AI). By the end of this course, you will be ready to dive into the advanced concepts of ML.</strong>
        <p>Instead of using hard-coded rules for performing something, artificial intelligence (AI) and machine learning (ML) prepare the machines to learn things from data to decipher the complex patterns automatically and then use it for building multiple use cases. If you too want to make applications and solve complex problems using AI and ML, then this course will help you to get started with the basics.

The course starts by highlighting the learning objectives and explains the rise of AI in today's world. Next, you will be introduced to the concepts of ML. You will learn to understand ML terminologies and learn how to train a model. Towards the end, you will become familiar with the classification of ML systems, including the topics of supervised and unsupervised learning, regression, clustering, and reinforcement.

By the end of this course, you will have a fundamental knowledge of AI and ML.

All the resource for this course are available at https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners---Level-1</p>
        <h2>What You Will Learn</h2><p>Find out why machine learning (ML) is called a black box<br>Get to grips with the features and labels of ML<br>Uncover techniques for training ML models<br>Find out the differences between supervised and unsupervised learning<br>Understand regression, clustering, and dimension reduction</p>
        <h2>Audience</h2><p>If you are a developer, product or project manager, engineer, or a data science enthusiast, who is looking to learn the fundamentals of artificial intelligence (AI) and machine learning (ML), this course is for you. There is no prerequisite to start this course as it is specially designed for absolute beginners.</p>
        <h2>Approach</h2><p>With the help of simple narration, interesting examples, and practical tips, this course helps you to get started with artificial intelligence (AI) and machine learning (ML).</p>
        <h2>Key Features</h2><p>Learn the basics of artificial intelligence (AI), machine learning (ML), and deep learning * Find out how ML helps in training a model * Discover how ML systems are classified</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners---Level-1</p>
        <h2>About the Author</h2><h3>Idan Gabrieli</h3><p>Idan Gabrieli has worked in various engineering positions in Israel's high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies.

Idan's expertise spans multiple domains, including cloud computing, machine learning, data science, and electronics. Since 2014, Idan has created and published online courses on various topics worldwide. Idan is recognized as a high-rated instructor by leading educational providers.

Idan simplifies complex technology and provides high-quality educational content with specific learning objectives that are well-structured, combining various multimedia teaching options.</p>
        <h2>Course Outline</h2><h4>1. Getting Started with Level 1!</h4><p></p><br><h4>2. The Rise of Artificial Intelligence</h4><p></p><br><h4>3. Introduction to Machine Learning</h4><p></p><br><h4>4. Classification of ML Systems</h4><p></p><br><h4>5. Course Summary</h4><p></p>

Deep Learning

This course will take you through the fundamental concepts of machine learning (ML) and artificial intelligence (AI). By the end of this course, you will be ready to dive into the advanced concepts of ML.

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