Name: Data Analyst (Data Analytics) Course
Price: 12 GBP
Availability: InStock

<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 the following participants:<br />Individuals planning to deploy applications and create application environments on Google Cloud Platform<br />Developers, systems operations professionals, and solution architects getting started with Google Cloud Platform<br />Executives and business decision makers evaluating the potential of Google Cloud Platform to address their business needs.</p>Overview<p>This course teaches participants the following skills:<br />Identify Google Cloud counterparts for Azure IaaS, Azure PaaS, Azure SQL, Azure Blob Storage, Azure Application Insights, and Azure Data Lake<br />Configure accounts, billing, projects, networks, subnets, firewalls, VMs, disks, auto scaling, load balancing,storage, databases, IAM, and more<br />Manage and monitor applications<br />Explain feature and pricing model differences</p><p>This 1-day instructor led course introduces Azure professionals to the core capabilities of Google Cloud in the four technology pillars: networking, compute, storage, and database. It is designed for Azure system administrators, Solution Architects and SysOps Administrators familiar with Azure features and setup; and want to gain experience configuring Google Cloud products immediately. With presentations, demos, and hands-on labs, participants get details of similarities, differences, and initial how-tos quickly.</p></div><div id="course-pills-2"><h4>Introducing Google Cloud</h4><ul><li>Explain the advantages of Google Cloud.</li><li>Define the components of Google's network infrastructure, including: Points of presence, data centers, regions, and zones.</li><li>Understand the difference between Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS).</li><li></li></ul><h4>Getting Started with Google Cloud</h4><ul><li>Identify the purpose of projects on Google Cloud.</li><li>Understand how Azure's resource hierarchy differs from Google Cloud's</li><li>Understand the purpose of and use cases for Identity and Access Management.</li><li>Understand how Azure AD differs from Google Cloud IAM.</li><li>List the methods of interacting with Google Cloud.</li><li>Launch a solution using Cloud Marketplace.</li></ul><h4>Virtual Machines in the Cloud</h4><ul><li>Identify the purpose and use cases for Google Compute Engine</li><li>Understand the basics of networking in Google Cloud.</li><li>Understand how Azure VPC differs from Google VPC.</li><li>Understand the similarities and differences between Azure VM and Google Compute Engine.</li><li>Understand how typical approaches to load-balancing in Google Cloud differ from those in Azure.</li><li>Deploy applications using Google Compute Engine</li></ul><h4>Storage in the Cloud</h4><ul><li>Understand the purpose of and use cases for: Cloud Storage,</li><li>Cloud SQL, Cloud Bigtable and Cloud Datastore.</li><li>Understand how Azure Blob compares to Cloud Storage.</li><li>Compare Google Cloud?s managed database services with Azure SQL.</li><li>Learn how to choose among the various storage options on Google Cloud.</li><li>Load data from Cloud Storage into BigQuery</li></ul><h4>Containers in the Cloud</h4><ul><li>Define the concept of a container and identify uses for containers.</li><li>Identify the purpose of and use cases for Google Container Engine and Kubernetes.</li><li>Understand how Azure Kubernetes Service differs from from Google Kubernetes Engine.</li><li>Provision a Kubernetes cluster using Kubernetes Engine.</li><li>Deploy and manage Docker containers using kubectl</li></ul><h4>Applications in the Cloud</h4><ul><li>Understand the purpose of and use cases for Google App Engine.</li><li>Contrast the App Engine Standard environment with the App Engine Flexible environment.</li><li>Understand how App Engine differs from Azure App Service.</li><li>Understand the purpose of and use cases for Google Cloud Endpoints.</li><li></li></ul><h4>Developing, Deploying and Monitoring in the Cloud</h4><ul><li>Understand options for software developers to host their source code.</li><li>Understand the purpose of template-based creation and management of resources.</li><li>Understand how Google Cloud Deployment Manager differs from Azure Resource Manager.</li><li>Understand the purpose of integrated monitoring, alerting, and debugging</li><li>Understand how Google Monitoring differs from Azure Application Insights and Azure Log Analytics.</li><li>Create a Deployment Manager deployment.</li><li>Update a Deployment Manager deployment.</li><li>View the load on a VM instance using Google Monitoring.</li></ul><h4>Big Data and Machine Learning in the Cloud</h4><ul><li>Understand the purpose of and use cases for the products and services in the Google Cloud big data and machine learning platforms.</li><li>Understand how Google Cloud BigQuery differs from Azure Data Lake.</li><li>Understand how Google Cloud Pub/Sub differs from Azure Event Hubs and Service Bus.</li><li>Understand how Google Cloud?s machine-learning APIs differ from Azure's.</li><li>Load data into BigQuery from Cloud Storage.</li><li>Perform queries using BigQuery to gain insight into data</li></ul><h4>Summary and Review</h4><ul><li>Review the products that make up Google Cloud and remember how to choose among them</li><li>Understand next steps for training and certification</li><li>Understand, at a high level, the process of migrating from Azure to Google Cloud.</li></ul></div>

Nexus Human

AWS Certified Cloud Practitioner

Google Suite

Google Cloud

Microsoft Azure

Google Workspace

Google Digital Marketing

Google Cloud Fundamentals for Azure Professionals

<div id="course-pills-1">Duration<p>5 Days</p><p>30 CPD hours</p>This course is intended for<p>The primary audience for this course is as follows:<br />IT Solution Architects<br />Network Security Architects<br />Networking Admin and Operations<br />Networking Field Engineers</p><p>In this workshop you will learn why we are implementing Assurance, and what does looks like and key features. We will also discuss the Cisco DNA center appliance and workflow and tasks associated with an implementation.</p></div><div id="course-pills-2"><h4>Module 1: Introduction to Cisco DNA Center Assurance</h4><ul><li>Introduction to DNA Center</li><li>System Architecture</li><li>Key Features and Use Cases</li><li>Introduction to Cisco ISE and DNAC-ISE integration</li></ul><h4>Module 2: Design</h4><ul><li>Network design options</li><li>Sites</li><li>Creating Enterprise and Sites Hierarchy</li><li>Configuring General Network Settings</li><li>Loading maps into the GUI</li><li>IP Address Management</li><li>Software Image Management ? Lab on Day 5</li><li>Network Device Profiles</li><li>AAA</li><li>SNMP</li><li>Syslog</li><li>IP address pools</li><li>Creating Enterprise and Guest SSIDs ? Lab on Day 2 </li><li>Creating the wireless RF Profile</li><li>Cresting the Guest Portal for the Guest SSIDs</li><li>Network profiles ? Lab on Day 2</li><li>Authentication templates</li></ul><h4>Module 3: Discovery and Provision</h4><ul><li>Device Discovery and Protocols</li><li>Devices Onboarding</li><li>Assigning Devices to a site</li><li>Provisioning device with profiles</li><li>Plug and Play</li><li>Templates</li><li>Templates for Day-0</li><li>Templates for Day-N operations</li></ul><h4>Module 4: Monitoring Device, Client, and Application Health</h4><ul><li>DNAC Telemetry<br />Monitoring Network Device Health and Performance<br />Monitoring Wired and Wireless Client Health and Performance<br />Monitoring Application Health and Performance</li></ul><h4>Module 5: Application Visibility and DNAC Integration with Umbrella</h4><ul><li>Monitoring Application Visibility in DNA Center<br />Umbrella Introduction<br />DNA Center Umbrella Integration and Use Cases</li></ul><h4>Module 6: Troubleshooting Issues, Observing Insights and Trends</h4><ul><li>Detect Issues, Insights, and Trends in the Network<br />DNA Center Integration with other tools for Monitoring and Management</li></ul><h4>Module 7: Troubleshooting using Cisco DNA Center Assurance Tools</h4><ul><li>DNAC Assurance Troubleshooting Tools<br />Using Sensor Tests<br />Using Intelligent Capture <br />Spectrum Analysis</li></ul><h4>Module 8: DNAC and Thousand Eyes</h4><ul><li>Introduction to ThousandEyes<br />ThousandEyes Use Cases<br />DNA Center and ThousandEyes Integration</li></ul><h4>Module 9: AI Analytics and Machine Learning in DNA Center</h4><ul><li>Overview of AI Analytics and ML (Machine Learning)<br />DNA Center AI and ML Use Cases</li></ul><h4>Module 10: DNA Center Maintenance</h4><ul><li>DNA Center Reports<br />DNA Center Backup and Restore</li></ul></div>

Cisco Certified Network Associate (CCNA)

AWS Certification

CompTIA Network+

IT Certifications

Cisco Certified Internetwork Expert (CCIE)

AWS Certified Solutions Architect

Cisco Implementing and Administering DNA Center for Assurance v2.0 (IADNACA)

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					<div><p>In this captivating course, you will embark on an exciting adventure through the Python programming language. Our journey begins with Section 1, 'Introduction &amp; Getting Started,' where you'll uncover the magic of Python and set the stage for your coding expedition. As you progress through the course, you'll explore essential concepts such as variables, data types, and conditional statements, all while acquiring the skills to craft your own Python programs.</p><p>With engaging exercises like building a program to say hello and creating a simple calculator, you'll gain hands-on experience, solidifying your newfound knowledge. Sections 10 and 11 delve into the powerful world of loops, covering both for and while loops, providing you with the tools to automate tasks and solve real-world problems. To put your skills to the test, Section 12 guides you in building a Username Password App, demonstrating the practical application of Python in everyday life.</p></div>
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			<h2> <b>Why should I take this course?</b></h2>		</div>
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                                <h2>Course Curriculum</h2>
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					<td>Unit 1: Introduction</td><td><span>🕐 00:03:00</span></td>
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					<td>Unit 2: Instructor's Introduction</td><td><span>🕐 00:03:00</span></td>
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					<td>Unit 1: Downloading and Installing Python</td><td><span>🕐 00:12:00</span></td>
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					<td>Unit 1: Variables and Basic Data Types in Python</td><td><span>🕐 00:12:00</span></td>
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					<td>Unit 1: Exercise - Build a Program to Say Hello</td><td><span>🕐 00:05:00</span></td>
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					<td colspan="4"><strong>Section 8: Exercise - Build a Simple Calculator</strong></td>
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					<td>Unit 1: Exercise - Build a Simple Calculator App</td><td><span>🕐 00:08:00</span></td>
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					<td colspan="4"><strong>Section 10: Loops - For Loop</strong></td>
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					<td colspan="4"><strong>Section 11: Loops - While Loop</strong></td>
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					<td>Unit 1: Loops - While Loop</td><td><span>🕐 00:08:00</span></td>
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					<td colspan="4"><strong>Section 12: Exercise - Building a Username Password App</strong></td>
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					<td>Unit 1: Exercise - Building a Username and Password App</td><td><span>🕐 00:05:00</span></td>
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Skill Up

iscover the fascinating world of Python programming as you take your first steps into the realm of coding. This course is designed to ignite your passion for programming, providing a solid foundation for beginners. Get ready to unleash your creative potential in the world of technology.

Python Crash Course - Level 1

General Admission

<p>Are you looking to enhance your Python- Beginner to Advance skills? If yes, then you have come to the right place. Our comprehensive course on Python- Beginner to Advance will assist you in producing the best possible outcome by mastering the Python- Beginner to Advance skills.</p><p>The Python- Beginner to Advance course is for those who want to be successful. In the Python- Beginner to Advance course, you will learn the essential knowledge needed to become well versed in Python- Beginner to Advance.</p><p>Our course starts with the basics of Python- Beginner to Advance and gradually progresses towards advanced topics. Therefore, each lesson of this Python- Beginner to Advance course is intuitive and easy to understand.</p><p><strong>Why would you choose the Python- Beginner to Advance course from Compliance Central:</strong></p><ul><li>Lifetime access to Python- Beginner to Advance course materials</li><li>Full tutor support is available from Monday to Friday with the Python- Beginner to Advance course</li><li>Learn Python- Beginner to Advance skills at your own pace from the comfort of your home</li><li>Gain a complete understanding of Python- Beginner to Advance course</li><li>Accessible, informative Python- Beginner to Advance learning modules designed by experts</li><li>Get 24/7 help or advice from our email and live chat teams with the Python- Beginner to Advance course</li><li>Study Python- Beginner to Advance in your own time through your computer, tablet or mobile device</li><li>A 100% learning satisfaction guarantee with your Python- Beginner to Advance course</li></ul><p><strong>Curriculum Breakdown of the Python- Beginner to Advance Course</strong></p><ul><li>Introduction</li><li>Curriculum Overview</li><li>What's New</li><li>command line basics</li><li>python installation</li><li>Pycham-ce ide installation</li><li>Setting up environment</li><li>Running python code</li><li>git and github overview</li><li>Python Data Types</li><li>Python Arithmetic Operators Numbers</li><li>Variable Assignments</li><li>Strings Introduction</li><li>Indexing and Slicing with Strings</li><li>String Properties and Methods</li></ul><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        10 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>The Python- Beginner to Advance course helps aspiring professionals who want to obtain the knowledge and familiarise themselves with the skillsets to pursue a career in Python- Beginner to Advance. It is also great for professionals who are already working in Python- Beginner to Advance and want to get promoted at work.</p>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>To enrol in this Python- Beginner to Advance course, all you need is a basic understanding of the English Language and an internet connection.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>The Python- Beginner to Advance course will enhance your knowledge and improve your confidence in exploring opportunities in various sectors.</p><ul><li>Python Developer: £35,000 to £70,000 per year</li><li>Data Analyst: £25,000 to £55,000 per year</li><li>Machine Learning Engineer: £45,000 to £85,000 per year</li><li>Software Engineer: £40,000 to £75,000 per year</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited PDF Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
            <div><p><strong>CPD Accredited PDF Certificate</strong></p></div>
        <h4>CPD Accredited Hard Copy Certificate</h4>
        <p>
            Hard copy certificate -
            £10.79
        </p>
            <div><p><strong>CPD Accredited Hard Copy Certificate</strong></p><p><strong>Delivery Charge:</strong></p><p>Inside the UK: Free</p><p>Outside of the UK: £9.99 each</p></div>

                        </div>

Compliance Central

Python- Beginner to Advance

<div id="course-pills-1">Duration<p>2 Days</p><p>12 CPD hours</p>This course is intended for<p>This course is designed for people who want to learn the Python programming language in preparation for using Python to develop software for a wide range of applications, such as data science, machine learning, artificial intelligence, and web development.</p>Overview<p>In this course, you will develop simple command-line programs in Python. You will:<br />Set up Python and develop a simple application.<br />Declare and perform operations on simple data types, including strings, numbers, and dates.<br />Declare and perform operations on data structures, including lists, ranges, tuples, dictionaries, and sets.<br />Write conditional statements and loops.<br />Define and use functions, classes, and modules.<br />Manage files and directories through code.<br />Deal with exceptions.</p><p>Though Python has been in use for nearly thirty years, it has become one of the most popular languages for software development, particularly within the fields of data science, machine learning, artificial intelligence, and web development?all areas in which Python is widely used. Whether you're relatively new to programming, or have experience in other programming languages, this course will provide you with a comprehensive first exposure to the Python programming language that can provide you with a quick start in Python, or as the foundation for further learning.<br /><br />You will learn elements of the Python 3 language and development strategies by creating a complete program that performs a wide range of operations on a variety of data types, structures, and objects, implements program logic through conditional statements and loops, structures code for reusability through functions, classes, and modules, reads and writes files, and handles error conditions.</p></div><div id="course-pills-2"><h4>Lesson 1: Setting Up Python and Developing a Simple Application</h4><ul><li>Topic A: Set Up the Development Environment</li><li>Topic B: Write Python Statements</li><li>Topic C: Create a Python Application</li><li>Topic D: Prevent Errors</li></ul><h4>Lesson 2: Processing Simple Data Types</h4><ul><li>Topic A: Process Strings and Integers</li><li>Topic B: Process Decimals, Floats, and Mixed Number Types</li></ul><h4>Lesson 3: Processing Data Structures</h4><ul><li>Topic A: Process Ordered Data Structures</li><li>Topic B: Process Unordered Data Structures</li></ul><h4>Lesson 4: Writing Conditional Statements and Loops in Python</h4><ul><li>Topic A: Write a Conditional Statement</li><li>Topic B: Write a Loop</li></ul><h4>Lesson 5: Structuring Code for Reuse</h4><ul><li>Topic A: Define and Call a Function</li><li>Topic B: Define and Instantiate a Class</li><li>Topic C: Import and Use a Module</li></ul><h4>Lesson 6: Writing Code to Process Files and Directories</h4><ul><li>Topic A: Write to a Text File</li><li>Topic B: Read from a Text File</li><li>Topic C: Get the Contents of a Directory</li><li>Topic D: Manage Files and Directories</li></ul><h4>Lesson 7: Dealing with Exceptions</h4><ul><li>Topic A: Handle Exceptions</li><li>Topic B: Raise Exceptions</li></ul></div>

Online Course Creation

Programming Fundamentals

Computer Programming

Continuous Professional Development (CPD)

Introduction to Programming with Python (v1.01)

<p>In today's rapidly evolving digital era, the fusion of finance and technology has paved the way for unprecedented opportunities. Enter the world of FinTech, Cryptocurrency, and the power of Data Analysis. With this '<strong>Data Analytics (Data Analysis), FinTech and Cryptocurrency</strong>' bundle, you're taking the first step into a realm where Data Analysis isn't just a tool-it's the core of decision-making. Dive deep into the nuances of modern finance, learn the intricacies of Cryptocurrency, and harness the might of Data Analysis to make informed strategies. </p><p>In the UK, professionals in these fields can enjoy impressive salary ranges, with earnings starting from £35,000 per year and reaching up to £80,000 per year, making it an enticing career choice. This bundle includes<strong> three courses </strong>that will equip you with the essential knowledge and skills to excel in this domain. This comprehensive <strong>Data Analysis </strong>bundle provides a valuable opportunity to explore the world of finance, technology, and data. By enrolling in these <strong>Data Analysis </strong>bundles, you will gain a deep understanding of the innovations shaping the financial industry, such as blockchain and artificial intelligence, and how they intersect with technology.</p><p>Each Data Analytics (Data Analysis) course in FinTech and Cryptocurrency bundle holds a prestigious CPD accreditation, symbolising exceptional quality. The materials, brimming with knowledge, are regularly updated, ensuring their relevance. This <strong>Data Analysis </strong>bundle promises not just education but an evolving learning experience. Engage with this extraordinary collection, and prepare to enrich your personal and professional development.</p><p><strong>Immerse yourself in these diverse, enthralling subjects, each designed to fuel your curiosity and enhance your knowledge. Dive in now!</strong></p><p><strong>The courses in this Data Analysis bundle include:</strong></p><ul><li><strong>Course 1: </strong>FinTech</li><li><strong>Course 2: </strong>Cryptocurrency</li><li><strong>Course 3: </strong>Data Analytics</li></ul><p><strong>Learning Outcomes:</strong></p><p>By completing this <strong>Data Analysis </strong>bundle, you will achieve the following learning outcomes:</p><ul><li>Understand the principles and applications of FinTech in the financial industry.</li><li>Leverage Data Analysis for informed decision-making in finance and digital currencies.</li><li>Use Data Analysis to forecast market trends in FinTech and Cryptocurrency.</li><li>Apply statistical analysis techniques to interpret data effectively.</li><li>Elevate financial proficiency by integrating insights from Data Analysis.</li><li>Develop a strategic mindset for leveraging data analytics in FinTech and Cryptocurrency.</li></ul><p>The first course, <strong>FinTech</strong>, delves into the fascinating intersection of finance and technology. Gain a deep understanding of the technological innovations that are revolutionising the financial industry, including blockchain, artificial intelligence, and mobile banking. Explore the impact of digital currencies, peer-to-peer lending, and robo-advisors on traditional financial systems.</p><p>The second course, <strong>Cryptocurrency</strong>, uncovers the secrets of this decentralised digital currency phenomenon. Discover the fundamentals of cryptocurrencies, such as Bitcoin and Ethereum, and explore the underlying blockchain technology. Dive into topics like mining, digital wallets, smart contracts, and the future of cryptocurrencies. Develop a solid foundation to navigate the complex world of digital assets.</p><p>The third course, <strong>Data Analytics</strong>, equips you with the essential skills to extract insights from vast amounts of data. Learn the techniques and tools used to collect, clean, and analyze data, allowing you to make informed decisions and predictions. Dive into statistical analysis, data visualisation, and machine learning algorithms. Harness the power of data to drive business growth and enhance decision-making processes.</p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        15 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>This Data Analytics (<strong>Data Analysis)</strong> in FinTech and Cryptocurrency course is suitable for:</p><ul><li>Professionals aspiring to work in the FinTech or Cryptocurrency sectors.</li><li>Financial analysts seeking to enhance their data analytics skills.</li><li>Entrepreneurs who are interested in leveraging technology to innovate in the financial industry.</li><li>Graduates looking to enter the finance or technology sectors with a competitive edge.</li><li>Business professionals aiming to stay ahead of industry trends.</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>You can delightfully enrol in this Data Analytics (<strong>Data Analysis)</strong> in FinTech and Cryptocurrency course without any formal requirements.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>You can pursue various exciting career paths in FinTech and Cryptocurrency, including:</p><ul><li>Financial Data Analyst: £35,000 - £50,000 per year.</li><li>Blockchain Developer: £45,000 - £75,000 per year.</li><li>Cryptocurrency Investment Analyst: £50,000 - £80,000 per year.</li><li>FinTech Consultant: £40,000 - £65,000 per year.</li><li>Data Scientist (Financial Sector): £55,000 - £90,000 per year.</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate Of Completion</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>Certificate Of Completion</h4>
        <p>
            Hard copy certificate -
            £9.99
        </p>

                        </div>

NextGen Learning

Finance Cert & Exam Prep

Certified Cryptocurrency Investigator Course (CCI)

Big Data Analytics

Data Science

Cryptocurrency & Blockchain

Digital Skills

Data Analytics (Data Analysis), FinTech and Cryptocurrency

<strong>Learn to build a complete Internet of Things solution with Arduino, a SQL Database, and a web server</strong>
        <p>The best way to find out whether this course is really for you is by asking yourself a few basic questions:

&bull; Do you like building things yourself?
&bull; Are you looking for ways to easily combine software and hardware components?
&bull; Did you ever wonder how electronic devices really work?
&bull; Do you have a creative idea and are just looking for ways to easily prototype it?

If your answer to most of the questions above is "Yes," then you're in the right place. The Arduino is probably one of the most amazing developments and prototyping platforms today with endless possibilities for Do-It-Yourself hobbyists looking for ways to express their creativity and technical capabilities.

This course is LEVEL 3 and is part of a larger comprehensive training program divided into levels that are all about the Arduino and its surrounding ecosystem. Each course covers a specific group of subjects to lets you develop and grow your skills step-by-step while enjoying the journey. Join us and learn to build a complete data-logger solution including a web server and a database to store and analyze the collected data.

All the code and supporting files for this course are available at -
https://github.com/PacktPublishing/Crazy-about-Arduino-Your-End-to-End-Workshop---Level-3</p>
        <h2>What You Will Learn</h2><p>Build an end-to-end Internet of Things (IoT) data logger<br>Collect data using multiple sensors: DHT11, LDR, and ultrasonic<br>Set up a personal web and database server (MySQL)<br>Connect the Arduino to the Internet using the Ethernet module<br>Design and use a web API to send sensor data from the Arduino to the webserver<br>Analyze data logger data</p>
        <h2>Audience</h2><p>This course is for technology innovators, electronics hobbyists, and beginners with a basic knowledge of Electronics and Arduino who are interested in building a complete Internet of Things solution with Arduino.</p>
        <h2>Approach</h2><p>This complete course, packed with step-by-step instructions, working examples, and helpful advice, is clearly divided into small chunks to help you understand each part individually and learn at your own pace.</p>
        <h2>Key Features</h2><p>A complete and well-organized course  * Build an interesting IoT project with sensors, an Apache web server, and a MySQL database  * Design and program a smart sketch code to run the overall project </p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/crazy-about-arduino-your-end-to-end-workshop---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</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Course Introduction</p><p style='margin: 0'>Introduction to Level 3.</p></td></tr></tbody></table><br><h4>2. Step 1 - Setting the Sensors - Let's Measure Something</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>An overview of the section and topics covered.</p></td></tr><tr><td style='padding: 15px;'><p>2. What is a Sensor?</p><p style='margin: 0'>Starting with the basic definition of sensor.</p></td></tr><tr><td style='padding: 15px;'><p>3. Sensors are Everywhere</p><p style='margin: 0'>Now what kind of things can be detected by sensors? well there many types of sensors that can detect different physical properties.</p></td></tr><tr><td style='padding: 15px;'><p>4. Measuring Temperature and Humidity</p><p style='margin: 0'>Measuring temperature and humidity with the DHT11 sensor.</p></td></tr><tr><td style='padding: 15px;'><p>5. Measuring Light Changes</p><p style='margin: 0'>Measuring light changes with the LDR sensor.</p></td></tr><tr><td style='padding: 15px;'><p>6. Measuring Distance</p><p style='margin: 0'>Measuring distance with the Ultrasonic sensor.</p></td></tr><tr><td style='padding: 15px;'><p>7. Wiring All Sensors to the Arduino</p><p style='margin: 0'>How to connect each sensor to the Arduino Uno board.</p></td></tr><tr><td style='padding: 15px;'><p>8. Hardware Setup - All Sensors</p><p style='margin: 0'>Demonstrating the hardware setup and the circuit design using Fritzing.</p></td></tr><tr><td style='padding: 15px;'><p>9. Project Demo</p><p style='margin: 0'>Demonstrating how the project is working so far with the three sensors.</p></td></tr><tr><td style='padding: 15px;'><p>10. Code Review</p><p style='margin: 0'>Detailed analysis of the Arduino sketch.</p></td></tr></tbody></table><br><h4>3. Step 2 - Ethernet Shield - Get Connected!</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>An overview of the section and topics covered.</p></td></tr><tr><td style='padding: 15px;'><p>2. IP Address</p><p style='margin: 0'>IP and MAC Addresses.</p></td></tr><tr><td style='padding: 15px;'><p>3. MAC Address</p><p style='margin: 0'>IP and MAC Addresses.</p></td></tr><tr><td style='padding: 15px;'><p>4. Client-Server Communication</p><p style='margin: 0'>A large part of network communication is based on client-server communication model, where a server is designed to be a centralized system that serves many clients.</p></td></tr><tr><td style='padding: 15px;'><p>5. TCP/UDP Ports</p><p style='margin: 0'>Ports numbers are used to separated data streams to the relevant applications.</p></td></tr><tr><td style='padding: 15px;'><p>6. Network Socket</p><p style='margin: 0'>Network socket is basically some endpoint of a logical connection and this is managed dynamically by the operation system</p></td></tr><tr><td style='padding: 15px;'><p>7. TCP Session</p><p style='margin: 0'>Connection oriented that established with the remote socket.</p></td></tr><tr><td style='padding: 15px;'><p>8. HTTP and HTML</p><p style='margin: 0'>HTTP functions as a request-response protocol in the client-server computing model.where HTML is a markup language for creating web pages and web applications.</p></td></tr><tr><td style='padding: 15px;'><p>9. The Ethernet Shield W5100</p><p style='margin: 0'>This is the type of Ethernet module we will use in our project. It is called Ethernet Shield W5100. This shield enables our small Arduino to be connected to the internet and by doing so opens endless amounts of possibility by allowing us to connect our projects to the internet.</p></td></tr><tr><td style='padding: 15px;'><p>10. The Ethernet Library</p><p style='margin: 0'>For the Ethernet shield there is a dedicated library that is used to setup the MAC and IP address, initiate connection as client to a remote server and accept connections as server from remote clients.</p></td></tr><tr><td style='padding: 15px;'><p>11. Hardware Setup - Ethernet Shield W5100</p><p style='margin: 0'>Demonstrating how the project is working with the Ethernet shield.</p></td></tr><tr><td style='padding: 15px;'><p>12. Project Demo - connecting to remote server</p><p style='margin: 0'>Demonstrating how the project is working with the Ethernet shield.</p></td></tr><tr><td style='padding: 15px;'><p>13. Code Review</p><p style='margin: 0'>Detailed analysis of the Arduino sketch.</p></td></tr></tbody></table><br><h4>4. Step 3 - WampServer - Hosting your own Web Server!</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>An overview of the section and topics covered.</p></td></tr><tr><td style='padding: 15px;'><p>2. Apache, PHP and MySQL</p><p style='margin: 0'>WampServer is a free web development environment for windows that enables us to setup and run a web server on our computer to host web sites. It is basically a package of three main building blocks: Apache2, PHP and MySQL.</p></td></tr><tr><td style='padding: 15px;'><p>3. Download & Install</p><p style='margin: 0'>Download and install the WampServer application.</p></td></tr><tr><td style='padding: 15px;'><p>4. Getting started with WampServer</p><p style='margin: 0'>How to setup, test and use the WampServer application.</p></td></tr><tr><td style='padding: 15px;'><p>5. Basic Server Configuration</p><p style='margin: 0'>Performing the basic server configuration.</p></td></tr></tbody></table><br><h4>5. Step 4 - MySQL Database - Storing Data Somewhere</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>An overview of the section and topics covered.</p></td></tr><tr><td style='padding: 15px;'><p>2. What is a Database?</p><p style='margin: 0'>A database is basically a logical container for storing data in a very structured way.</p></td></tr><tr><td style='padding: 15px;'><p>3. MySQL Tools</p><p style='margin: 0'>To be able to manage and access the database there two main options such as MySQL simple command line window and phpMyAdmin web client graphical.</p></td></tr><tr><td style='padding: 15px;'><p>4. Using phpMyAdmin Console</p><p style='margin: 0'>Introduction to the phpMyAdmin tool.</p></td></tr><tr><td style='padding: 15px;'><p>5. SQL Statements</p><p style='margin: 0'>Overview of SQL statements, zooming on SELECT and INSERT.</p></td></tr><tr><td style='padding: 15px;'><p>6. Testing our Project Database</p><p style='margin: 0'>Performing few simple database testings.</p></td></tr></tbody></table><br><h4>6. Step 5 - PHP - Running Scripts at the Server</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>An overview of the section and topics covered.</p></td></tr><tr><td style='padding: 15px;'><p>2. What is PHP?</p><p style='margin: 0'>Introduction to the PHP server-side scripting language.</p></td></tr><tr><td style='padding: 15px;'><p>3. Basic PHP Syntax</p><p style='margin: 0'>Basic review to the relevant PHP syntax we will need to create our scripts.</p></td></tr><tr><td style='padding: 15px;'><p>4. PHP Extension to MySQL - Introduction</p><p style='margin: 0'>We are going to use the new PHP extension to MySQL, called PHP MySQLi that stands for PHP MySQL improved.</p></td></tr><tr><td style='padding: 15px;'><p>5. mysqli - Establish Database Connection</p><p style='margin: 0'>Before any PHP script can perform an action on the MySQL database, it must first hold a connection to the database.</p></td></tr><tr><td style='padding: 15px;'><p>6. Insert Data</p><p style='margin: 0'>Building the needed scripts for inserting data into the database.</p></td></tr><tr><td style='padding: 15px;'><p>7. Insert Data - PHP Scripts Testing</p><p style='margin: 0'>Performing scripts testing.</p></td></tr><tr><td style='padding: 15px;'><p>8. Query Data</p><p style='margin: 0'>Building the needed script for querying data from the database.</p></td></tr><tr><td style='padding: 15px;'><p>9. Query Data - PHP Scripts Testing</p><p style='margin: 0'>Performing scripts testing.</p></td></tr><tr><td style='padding: 15px;'><p>10. MySQL Error Handling</p><p style='margin: 0'>Errors are almost unavoidable as in some point of time you will encounter a situation that your script is not really working and will require some level of debugging to understand the problem root cause</p></td></tr><tr><td style='padding: 15px;'><p>11. MySQL Errors Simulation</p><p style='margin: 0'>Simulating some errors related to the MySQL database.</p></td></tr></tbody></table><br><h4>7. Step 6 - Final Project - Web-based Datalogger</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>An overview of the section and topics covered.</p></td></tr><tr><td style='padding: 15px;'><p>2. Connecting the Arduino to the Web Server</p><p style='margin: 0'>Now we have our own web server up and running, configured and it time to connect the Arduino to our web server.</p></td></tr><tr><td style='padding: 15px;'><p>3. Final Project - Checklist and Demo</p><p style='margin: 0'>There are few simple steps or checklist that are needed to be able to bring up the system.</p></td></tr></tbody></table><br><h4>8. Course Summary</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What did we covered?</p><p style='margin: 0'>Quick course summary, what have we learn in Level 3....</p></td></tr><tr><td style='padding: 15px;'><p>2. What Next? Level 4!</p><p style='margin: 0'>Thanks!!! and I hope to see you in the next level!</p></td></tr></tbody></table>

Packt

Java

Arduino

Learn to build a complete Internet of Things solution with Arduino, a SQL Database, and a web server

Crazy about Arduino: Your End-to-End Workshop - Level 3

<p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><b data-stringify-type="bold" fr-original-style=" color: rgb(209, 210, 211);" style="color: rgb(209, 210, 211); box-sizing: border-box;" id="isPasted"><span fr-original-style="color: rgb(0, 0, 0);" style="color: rgb(0, 0, 0); box-sizing: border-box;">Welcome to Electronic Document Management: Secure and Accessible File Storage. In this course, you&#39;ll learn:<br fr-original-style="" style="box-sizing: border-box;"></span></b></p><ol style="color: rgb(209, 213, 219); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(209, 213, 219);"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong style="color: var(--tw-prose-bold); font-weight: 700; box-sizing: border-box;" fr-original-style=" color: var(--tw-prose-bold);">Introduction to Electronic Document Management (EDM):</strong></p><ul style="color: initial; box-sizing: border-box;" fr-original-style="color: initial;"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Definition and significance of EDM in the digital age</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Evolution of document management systems</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Key components and features of EDM solutions</li></ul></li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong style="color: var(--tw-prose-bold); font-weight: 700; box-sizing: border-box;" fr-original-style=" color: var(--tw-prose-bold);">Standards, Regulations, and Compliance in EDM:</strong></p><ul style="color: initial; box-sizing: border-box;" fr-original-style="color: initial;"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Overview of global and industry-specific standards</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Regulatory frameworks governing electronic document management</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Strategies for ensuring compliance and mitigating risks</li></ul></li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong style="color: var(--tw-prose-bold); font-weight: 700; box-sizing: border-box;" fr-original-style=" color: var(--tw-prose-bold);">Implementing and Framework of EDM:</strong></p><ul style="color: initial; box-sizing: border-box;" fr-original-style="color: initial;"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Practical considerations for implementing EDM systems</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Frameworks for effective electronic document organization</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Integration of EDM with existing business processes</li></ul></li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong style="color: var(--tw-prose-bold); font-weight: 700; box-sizing: border-box;" fr-original-style=" color: var(--tw-prose-bold);">Electronic Document Lifecycle Management:</strong></p><ul style="color: initial; box-sizing: border-box;" fr-original-style="color: initial;"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Understanding the document lifecycle in a digital environment</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Version control, revision history, and document tracking</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Automation of document workflows for enhanced efficiency</li></ul></li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong style="color: var(--tw-prose-bold); font-weight: 700; box-sizing: border-box;" fr-original-style=" color: var(--tw-prose-bold);">Operations and Processes in EDM:</strong></p><ul style="color: initial; box-sizing: border-box;" fr-original-style="color: initial;"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Day-to-day operations in an electronic document management system</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Collaboration and sharing features</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Search and retrieval techniques for efficient information access</li></ul></li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong style="color: var(--tw-prose-bold); font-weight: 700; box-sizing: border-box;" fr-original-style=" color: var(--tw-prose-bold);">Document Control, Security, and Auditing:</strong></p><ul style="color: initial; box-sizing: border-box;" fr-original-style="color: initial;"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Access controls and permissions in EDM</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Encryption, data integrity, and secure document storage</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Auditing and monitoring tools to ensure compliance and security</li></ul></li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong style="color: var(--tw-prose-bold); font-weight: 700; box-sizing: border-box;" fr-original-style=" color: var(--tw-prose-bold);">Advanced Topics in EDM:</strong></p><ul style="color: initial; box-sizing: border-box;" fr-original-style="color: initial;"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Integration with other enterprise systems</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Advanced metadata management and tagging</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Artificial Intelligence and Machine Learning applications in EDM</li></ul></li></ol>

John Academy

Information Management

Compliance

Unlock the secrets of efficient Electronic Document Management (EDM) with our comprehensive course on Secure and Accessible File Storage. Delve into industry standards, regulations, and compliance, master the implementation and framework of EDM systems, and explore advanced topics such as AI integration. Elevate your skills in document lifecycle management, operations, and security auditing. Join us to gain the expertise needed for seamless digital transformation and effective information management in today's dynamic business environment.

Electronic Document Management: Secure and Accessible File Storage

<strong>Plan, deploy and monitor cloud solutions in Microsoft Azure's Infrastructure as a Service</strong>
        <p>Cloud computing is one of the biggest technology revolutions in the IT industry, spreading at the speed of light all over the world. A growing number of business companies and organizations are looking for ways to migrate their applications into the cloud or build new cloud-native applications from scratch on top of cloud infrastructure. The demand for skilled people in the area of cloud computing is increasing every day across industries. Companies are looking for cloud experts!

This course is part of a training program called Becoming a Cloud Expert. It sets a strong foundation for the viewers to learn to migrate applications onto the cloud while using the Infrastructure as a Service model inside Microsoft Azure.

Join us and start your journey as a cloud expert while learning to create a variety of virtual resources in Microsoft Azure. 
All the code and supporting files for this course are available at - https://github.com/PacktPublishing/Becoming-a-Cloud-Expert---Microsoft-Azure-IaaS---Level-1</p>
        <h2>What You Will Learn</h2><p>Understand the building blocks of Azure s Infrastructure as a Service<br>Create virtual networks and subnets, and allocate private and public IP addresses<br>Plan and deploy Windows and Linux virtual machines<br>Configure traffic filtering using security rules<br>Manage and attach virtual disks to VMs<br>Create and manage storage accounts<br>Configure users and access role assignments<br>Operate and monitor ongoing VM tasks</p>
        <h2>Audience</h2><p>This course is for Cloud administrators, DevOps engineers, IT and cloud experts.<br></p>
        <h2>Approach</h2><p>This complete course, packed with step-by-step instructions, working examples, and helpful advice, is clearly divided into small chunks to help you understand each topic individually and learn at your own pace.</p>
        <h2>Key Features</h2><p>A comprehensive overview of the Microsoft Azure platform * A unique balance between theoretical topics and practical demonstrations * Practice with a dedicated project book</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/becoming-a-cloud-expert---microsoft-azure-iaas---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</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Welcome!</p><p style='margin: 0'>Welcome and thanks for joining! this training program "Becoming a Cloud Expert".</p></td></tr><tr><td style='padding: 15px;'><p>2. Course Objectives and Structure</p><p style='margin: 0'>Before we start lets' quickly review the course main learning objectives and high-level structure.</p></td></tr></tbody></table><br><h4>2. Introduction to Microsoft Azure</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Here, we look into the topics covered in this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Cloud Computing Definition</p><p style='margin: 0'>Cloud Computing is the transformation of computer hardware, software and networks into a utility service just like electric, water or gas services. Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources, that can be rapidly provisioned and released with minimal management effort or service provider interaction.</p></td></tr><tr><td style='padding: 15px;'><p>3. Microsoft Azure Cloud</p><p style='margin: 0'>Microsoft Azure provides the services to build, manage and deploy applications on a global scale while using multiple data centre's connected with a super-fast network. The all hardware and software orchestration of the cloud infrastructure is done using a specialized cloud operating system, called "Microsoft Azure" developed by Microsoft.</p></td></tr><tr><td style='padding: 15px;'><p>4. Global Footprint</p><p style='margin: 0'>The cloud services are supposed to be available on a global scale and this is one of the most impressive things about a public cloud provider, like Microsoft Azure, Amazon AWS, or Google cloud. Those players are building and running a huge global infrastructure and every year they announce on additional regions.</p></td></tr><tr><td style='padding: 15px;'><p>5. Demo - Azure Portal Overview</p><p style='margin: 0'>Let's review the Azure web portal at a high level.</p></td></tr><tr><td style='padding: 15px;'><p>6. Azure Resource Manager (ARM)</p><p style='margin: 0'>As part of the management layer of Azure, there is a central component that is called Azure Resource Manager, or in short ARM. The azure resource manager is used to allocate and deploy resources, organize resources into groups, control access to resources and much more.</p></td></tr><tr><td style='padding: 15px;'><p>7. Demo - Azure ARM</p><p style='margin: 0'>Let's review the Azure ARM inside Microsoft Azure - resources, resource groups, resource providers, accounts and subscriptions and deployment templates.</p></td></tr><tr><td style='padding: 15px;'><p>8. Azure Role-Based Access Control (RBAC)</p><p style='margin: 0'>Access management for resources is a critical function for any group of users or an organization that is using the Azure cloud. As administrators, the Role-based access control (RBAC) will be used to manage who has access to Azure resources and what they can do with those resources.</p></td></tr><tr><td style='padding: 15px;'><p>9. Demo - Azure RBAC</p><p style='margin: 0'>Let's create new users and assign them access to specific entities.</p></td></tr></tbody></table><br><h4>3. Azure IaaS - Networking</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Here, we investigate the topics covered in this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. What is a Virtual Network?</p><p style='margin: 0'>A virtual network is basically a private isolated connectivity layer being used to connect between a group of cloud resources.</p></td></tr><tr><td style='padding: 15px;'><p>3. Virtual Network Setting</p><p style='margin: 0'>In azure a virtual network is a managed entity that users are creating with a group of setting that is configured during the creation process or added later when the virtual network entity is already created.</p></td></tr><tr><td style='padding: 15px;'><p>4. Demo - Creating a Virtual Network (VNet)</p><p style='margin: 0'>Let's review the all process end-to-end for creating virtual network and subnets.</p></td></tr><tr><td style='padding: 15px;'><p>5. IP Address Types</p><p style='margin: 0'>As soon as we have a new virtual network, we can create VMs as resources inside that virtual network or inside a specific subnet. Each resource must be assigned with at least one private IP address from the virtual network address range we allocated to the virtual network. In addition, if we want a resource inside the virtual network to access the outside internet than we will need to use also a public IP address.</p></td></tr><tr><td style='padding: 15px;'><p>6. VM, NICs and IP Configuration</p><p style='margin: 0'>We have an IP address, that can be public or private created dynamically or with a static configuration. In Azure, we have a virtual machine, network interfaces and IP configuration profiles. Let's see how those building blocks are connected and what kind of combination we can create.</p></td></tr><tr><td style='padding: 15px;'><p>7. Demo - Network Interfaces and IP Configuration</p><p style='margin: 0'>Let's review how to create network interfaces, attach them to VMs and create or adjust IP configuration.</p></td></tr><tr><td style='padding: 15px;'><p>8. Network Security Group (NSG)</p><p style='margin: 0'>All public cloud providers provide an ability to filter traffic and I am planning to present how it is being done in Microsoft Azure. In Microsoft Azure, we can filter network traffic to and from resources in a virtual network with an entity that is called a network security group.</p></td></tr><tr><td style='padding: 15px;'><p>9. Application Security Group (ASG)</p><p style='margin: 0'>Another useful option in Azure related to traffic filtering is using application security groups. The focus is moving to the function of the applications. We can group VMs based on their function.</p></td></tr><tr><td style='padding: 15px;'><p>10. Demo - Configuring NSG and ASG</p><p style='margin: 0'>Let's review how to configure NSG and ASG.</p></td></tr></tbody></table><br><h4>4. Azure IaaS - Storage</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Here, we investigate the topics covered in this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. The Power of a Cloud Storage</p><p style='margin: 0'>Cloud storage is basically the option to store data on the Internet through a cloud computing provider who manages and operates data storage as a service. We talked about it in the introduction course about cloud computing, but this is the power of the cloud, it is a public infrastructure enabling companies to build applications that can scale on a global level without the limitation of a single private data center.</p></td></tr><tr><td style='padding: 15px;'><p>3. Types of Cloud Storage Services</p><p style='margin: 0'>These are three main storage types Block Storage, File Storage, and Object Storage.</p></td></tr><tr><td style='padding: 15px;'><p>4. Azure Storage Services</p><p style='margin: 0'>Storage services provided by public cloud providers are basically a mix of multiple options to fulfill the storage needs of a variety of applications.</p></td></tr><tr><td style='padding: 15px;'><p>5. Storage Accounts</p><p style='margin: 0'>Any type of storage service, like blobs, files, disks, tables, and queues that we would like to use must be allocated inside a logical entity called a storage account. A storage account entity can be created and managed by us or in some cases it will be created and managed by Azure.</p></td></tr><tr><td style='padding: 15px;'><p>6. Storage Replication Options</p><p style='margin: 0'>One of the most valuable features in a public cloud is high data durability. We know that data can be damaged because of many things like hardware failures, network or power outages, or maybe some massive site disasters. Systems availability is a critical factor and data is a critical component in almost all systems. Therefore, Microsoft Azure and other cloud providers are replicating our data and enable us to configure the best replication option for our application.</p></td></tr><tr><td style='padding: 15px;'><p>7. Demo - Creating and Managing a Storage Account</p><p style='margin: 0'>Let's review how to create and manage a storage account.</p></td></tr><tr><td style='padding: 15px;'><p>8. VMs Disks</p><p style='margin: 0'>In addition to allocating computing power and setting networking connectivity, we also would like the capability to allocate storage capacity as virtual disks. These are three types of azure VM disks: - Operating System Disk (OS Disk), Temporary Disk and Data Disk/s.</p></td></tr><tr><td style='padding: 15px;'><p>9. Demo - Create, Attach and Detach Data Disks</p><p style='margin: 0'>Let's review how to create, attach and detach data disks for VMs.</p></td></tr><tr><td style='padding: 15px;'><p>10. Encryption Data at Rest</p><p style='margin: 0'>Security is a critical part of any solution, including the storage. Our next topic is encryption data at rest, meaning data that is stored in Microsoft Azure storage services. These are Azure Key Vault, SSE - Storage Service Encryption, Azure Disk Encryption (ADE).</p></td></tr></tbody></table><br><h4>5. Azure IaaS - Compute</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Here, we investigate the topics covered in this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Virtualization</p><p style='margin: 0'>At the core technology value of virtualization, we can say that virtualization is a great way to divide and optimize physical IT resources into logical entities or also called virtual resources. It is an abstraction management layer of physical objects into logical objects. Now going back to the cloud, a public cloud environment is all about virtualization but of course in a much larger scale than a single private data center.</p></td></tr><tr><td style='padding: 15px;'><p>3. Virtual Machines</p><p style='margin: 0'>VM as a resource can be allocated on-demand while using the flexibility comes with a cloud environment. We can define the required VM from a variety of VM types and sizes to be used for different application workloads.</p></td></tr><tr><td style='padding: 15px;'><p>4. VM Types and Sizes</p><p style='margin: 0'>Every application or better call it workload will have a different resources consumption profile. Some applications require more CPUs power and less memory capacity and in other cases, it will be the other way around, more memory and less CPU power. So Microsoft Azure provides us with a variety of VM types and sizes that will be more optimized to the resources consumption profile of our application.</p></td></tr><tr><td style='padding: 15px;'><p>5. Demo - Creating a VMs</p><p style='margin: 0'>Let's review how to create a new VM while following the all process end-to-end.</p></td></tr><tr><td style='padding: 15px;'><p>6. Demo - VMs Settings</p><p style='margin: 0'>VMs Setting are as follows: - Attaching Network Interfaces, attaching Data Disks, scaling a VM Size, security recommendations, adding Extensions and configure access control.</p></td></tr><tr><td style='padding: 15px;'><p>7. Demo - VMs Operations</p><p style='margin: 0'>VMs Operations are as follows: - shutdown/Run a VM, configure Auto-shutdown, scheduled Backups, using Inventory management for installed software components and running scripts on a VM.</p></td></tr><tr><td style='padding: 15px;'><p>8. Demo - VMs Monitoring</p><p style='margin: 0'>VMs Monitoring include - analysing activity logs, review metrics or KPIs, configure alerts, configure the diagnostics setting and review the Azure Advisor recommendations</p></td></tr></tbody></table><br><h4>6. Mission is Possible! - Your Project Assignment</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Mission Briefing</p><p style='margin: 0'>Do you choose to accept your mission?</p></td></tr></tbody></table><br><h4>7. Course Summary</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Let's Summarize</p><p style='margin: 0'>Let's recap the main topics we covered in this course.</p></td></tr><tr><td style='padding: 15px;'><p>2. What Next?</p><p style='margin: 0'>Thank you for joining and learning this course, let's speak about your next step moving forward as a Cloud Expert.</p></td></tr></tbody></table>

DevOps

Cloud Computing

Plan, deploy and monitor cloud solutions in Microsoft Azure's Infrastructure as a Service

Becoming a Cloud Expert - Microsoft Azure IaaS - Level 1

<p><strong>[Updated 2024]</strong></p><p><strong>To provide significant commercial value, you create and construct data models that make data simple to grasp. Take the training you need to gain the abilities and information required to make a successful career as a data analyst.</strong></p><p>Through the use of visualisation and reporting technologies, a data analyst enables businesses to maximise the value of their data assets. Additionally, data analysts are in charge of profiling, cleansing, and transforming data. Additionally, they are responsible for establishing and constructing efficient data models, enabling and integrating advanced analytics capabilities into reports for analysis.</p><p>From this data analyst course, you will learn to transform raw data into important and insightful insights after working with the relevant stakeholders to establish appropriate and essential data and reporting requirements.</p><p>The training materials of this Data Analyst (Data Analytics) course are available online for you to learn at your own pace and fast-track your career with ease.</p><p><strong>Key Features </strong></p><ul><li>Accredited by CPD</li><li>Instant e-certificate</li><li>Fully online, interactive Data Analyst (Data Analytics) course with audio voiceover</li><li>Self-paced learning and laptop, tablet, smartphone-friendly</li><li>24/7 Learning Assistance</li><li>Discounts on bulk purchases</li></ul><p><strong>Curriculum of Data Analyst (Data Analytics) Course</strong></p><ul><li><strong>Module 01</strong>: Introduction</li><li><strong>Module 02</strong>: Agenda and Principles of Process Management</li><li><strong>Module 03</strong>: The Voice of the Process</li><li><strong>Module 04</strong>: Working as One Team for Improvement</li><li><strong>Module 05</strong>: Exercise: The Voice of the Customer</li><li><strong>Module 06</strong>: Tools for Data Analysis</li><li><strong>Module 07</strong>: The Pareto Chart</li><li><strong>Module 08</strong>: The Histogram</li><li><strong>Module 09:</strong> The Run Chart</li><li><strong>Module 10</strong>: Exercise: Presenting Performance Data</li><li><strong>Module 11</strong>: Understanding Variation</li><li><strong>Module 12</strong>: The Control Chart</li><li><strong>Module 13</strong>: Control Chart Example</li><li><strong>Module 14</strong>: Control Chart Special Cases</li><li><strong>Module 15</strong>: Interpreting the Control Chart</li><li><strong>Module 16</strong>: Control Chart Exercise</li><li><strong>Module 17</strong>: Strategies to Deal with Variation</li><li><strong>Module 18:</strong> Using Data to Drive Improvement</li><li><strong>Module 19</strong>: A Structure for Performance Measurement</li><li><strong>Module 20</strong>: Data Analysis Exercise</li><li><strong>Module 21:</strong> Course Project</li><li><strong>Module 22</strong>: Test your Understanding</li></ul><p><strong>Accreditation</strong></p><p>All of our courses are fully accredited, including this Data Analyst (Data Analytics) course, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field.</p><p><strong>Certification</strong></p><p>Once you've successfully completed your Data Analyst (Data Analytics) course, you will immediately be sent a digital certificate. Also, you can have your printed certificate delivered by post (shipping cost £3.99).</p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        10 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>This course is ideal for all employees or anyone who genuinely wishes to learn more about Data Analyst (Data Analytics) basics, including:</p><ul><li>Data scientist</li><li>Machine learning engineer</li><li>Data analytics consultant.</li><li>Data analytics manager</li><li>Financial analyst</li><li>Marketing analyst</li><li>Systems analyst</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>No prior degree or experience is required to enrol in this course.</p>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4> Hard copy certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div><p>You will get the hard copy certificates for Free! The delivery charge of the hard copy certificate inside the UK is £3.99 each.</p></div>
        <h4>Digital certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>

                        </div>

Training Express

Data Analyst (Data Analytics) Course

<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>

Data Engineering

Microsoft AZ-900

Microsoft AZ-303

Microsoft AZ-700

Azure Machine Learning

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

Python

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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