Name: Quick Data Science Approach from Scratch
Price: 10.99 GBP
Availability: InStock

<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 IT Security for IT Professionals. In this course, you&#39;ll learn:</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 IT Security:</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 IT security concepts and principles</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Understanding the threat landscape and the importance of cybersecurity</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Historical perspectives on major security incidents and their impact on IT</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);">Security Policies and Procedures:</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);">Developing and implementing effective security policies</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Understanding compliance requirements and industry standards</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Incident response planning and execution</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Security awareness training for organizations</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);">Network Security:</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);">Securing network infrastructure and devices</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Intrusion detection and prevention systems</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Virtual Private Networks (VPNs) and secure communication</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Firewalls and network segmentation</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);">Operating System Security:</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);">Hardening operating systems for security</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">User access controls and permissions</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Patch management and software updates</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Securing system logs and monitoring</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);">Cryptography and Encryption:</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);">Basics of cryptography and encryption algorithms</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Implementing secure communication using encryption</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Public and private key infrastructure</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Cryptographic protocols and best practices</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);">Web Security:</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);">Securing web applications and websites</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Common web vulnerabilities and exploits</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Web application firewalls</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Secure coding practices</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);">Wireless Security:</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);">Securing wireless networks</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Authentication and encryption for Wi-Fi</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Wireless intrusion detection and prevention</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Mobile device 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);">Emerging Trends in IT Security:</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);">Cloud security and virtualization</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Internet of Things (IoT) security</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 in cybersecurity</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Blockchain and its implications for security</li></ul></li></ol><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><br fr-original-style="" style="box-sizing: border-box;"></p>

John Academy

Cryptography

Explore the world of cybersecurity with our "IT Security for IT Professionals" course. Gain expertise in fundamental areas such as network security, cryptography, web security, and emerging trends. Equip yourself with the skills needed to protect against evolving cyber threats. Enroll now for a comprehensive journey into IT security and fortify your role as a vigilant IT professional.

IT Security for IT Professionals

General Admission

<strong>Learn to effectively monitor the performance, health, and availability of Azure Services and your cloud resources</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 more skilled people in the area of cloud computing is increasing every day across multiple industries. Companies are looking for cloud experts!

Becoming a cloud expert - If you are looking to become a cloud expert, then this training program is designed to help you build your knowledge of, and experience with, cloud computing while using the Azure cloud platform. The training program is divided into levels.

Level 2  - All about monitoring.
In level 1, we learned to build an end-to-end cloud solution on the Microsoft Azure platform. Now is the time to learn how to monitor our IaaS solution effectively. In level 2, we will learn how to monitor and analyze the performance and health of our Azure resources, applications, and the Azure platform and services. One thing for sure, it is going to be an exciting journey!

All the codes and supporting files for this course are available at - https://github.com/PacktPublishing/Becoming-a-Cloud-Expert---Microsoft-Azure-IaaS---Level-2</p>
        <h2>What You Will Learn</h2><p>Understand operational and business requirements for monitoring the performance and health of a cloud solution<br>Understand the different types of telemetry data (metrics and logs) that can be collected<br>Build your own demo system in Azure (using Linux, Apache, MySQL, and PHP) &nbsp;&nbsp;<br>Set up alert rules and action groups to create an automation layer<br>Simulate traffic load on the demo system<br>Monitor the status of the Azure platform and services<br>Monitor your Azure resources (such as VMs, storage accounts, VNet, interfaces, NSGs, and web apps)<br>Analyze performance metrics in multiple layers (Azure hosting infrastructures, guest OSes, applications)<br>Analyze logs using the kusto query language in log analytics<br>Manage the life cycle of Azure alerts</p>
        <h2>Audience</h2><p>The course is designed for Cloud administrators, IT and cloud experts and DevOps engineers.</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 comprehensive overview of the Microsoft Azure platform * A unique balance between theoretical topics and practical demonstrations * The second level of Becoming a Cloud Expert training program</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'>Getting Started: Welcome!</p></td></tr><tr><td style='padding: 15px;'><p>2. Course Objectives and Structure</p><p style='margin: 0'>Getting Started: Course Objectives and Structure</p></td></tr><tr><td style='padding: 15px;'><p>3. Why?</p><p style='margin: 0'>Getting Started: Why?</p></td></tr><tr><td style='padding: 15px;'><p>4. What?</p><p style='margin: 0'>Getting Started: What?</p></td></tr><tr><td style='padding: 15px;'><p>5. How?</p><p style='margin: 0'>Getting Started: How?</p></td></tr></tbody></table><br><h4>2. Telemetry Data Collection - Types and Sources</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Telemetry Data Collection - Types and Sources: Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. Data Sources - Collection from Multiple Layers</p><p style='margin: 0'>Telemetry Data Collection - Types and Sources: Data Sources - Collection from Multiple Layers</p></td></tr><tr><td style='padding: 15px;'><p>3. Metrics and Logs</p><p style='margin: 0'>Telemetry Data Collection - Types and Sources: Metrics and Logs</p></td></tr><tr><td style='padding: 15px;'><p>4. Data Type #1 - Azure Subscription-Level Activity Log</p><p style='margin: 0'>Telemetry Data Collection - Types and Sources: Data Type #1 - Azure Subscription-Level Activity Log</p></td></tr><tr><td style='padding: 15px;'><p>5. Data Type #2 - Azure Resources Diagnostics Logs</p><p style='margin: 0'>Telemetry Data Collection - Types and Sources: Data Type #2 - Azure Resources Diagnostics Logs</p></td></tr><tr><td style='padding: 15px;'><p>6. Data Type #3 - Azure Resources Metrics</p><p style='margin: 0'>Telemetry Data Collection - Types and Sources: Data Type #3 - Azure Resources Metrics</p></td></tr><tr><td style='padding: 15px;'><p>7. Data Type #4 - Guest OS Metrics and Logs</p><p style='margin: 0'>Telemetry Data Collection - Types and Sources: Data Type #4 - Guest OS Metrics and Logs</p></td></tr><tr><td style='padding: 15px;'><p>8. Data Type #5 - Applications Metrics and Logs</p><p style='margin: 0'>Telemetry Data Collection - Types and Sources: Data Type #5 - Applications Metrics and Logs</p></td></tr></tbody></table><br><h4>3. Preparing our Demo System "Playground"</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Preparing our Demo System "Playground": Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. Step #1 - Back-End MySQL DB Server</p><p style='margin: 0'>Preparing our Demo System "Playground": Step #1 - Back-End MySQL DB Server</p></td></tr><tr><td style='padding: 15px;'><p>3. Step #2 - Front-End Apache Web Server</p><p style='margin: 0'>Preparing our Demo System "Playground": Step #2 - Front-End Apache Web Server</p></td></tr><tr><td style='padding: 15px;'><p>4. Step #3 - Log Analytics Workspace</p><p style='margin: 0'>Preparing our Demo System "Playground": Step #3 - Log Analytics Workspace</p></td></tr><tr><td style='padding: 15px;'><p>5. Step #4 - Diagnostics Settings</p><p style='margin: 0'>Preparing our Demo System "Playground": Step #4 - Diagnostics Settings</p></td></tr><tr><td style='padding: 15px;'><p>6. Step #5 - Azure Monitor for VMs</p><p style='margin: 0'>Preparing our Demo System "Playground": Step #5 - Azure Monitor for VMs</p></td></tr><tr><td style='padding: 15px;'><p>7. Step #6 - Application Insights</p><p style='margin: 0'>Preparing our Demo System "Playground": Step #6 - Application Insights</p></td></tr></tbody></table><br><h4>4. Effective Monitoring is all about Automation</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Effective Monitoring is all about Automation: Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. The Concept of Alerts and Actions</p><p style='margin: 0'>Effective Monitoring is all about Automation: The Concept of Alerts and Actions</p></td></tr><tr><td style='padding: 15px;'><p>3. How to Create an Alert Rule</p><p style='margin: 0'>Effective Monitoring is all about Automation: How to Create an Alert Rule</p></td></tr><tr><td style='padding: 15px;'><p>4. Configure Our Demo Rules and Actions</p><p style='margin: 0'>Effective Monitoring is all about Automation: Configure Our Demo Rules and Actions</p></td></tr><tr><td style='padding: 15px;'><p>5. Simulating a Load on the Web Server</p><p style='margin: 0'>Effective Monitoring is all about Automation: Simulating a Load on the Web Server</p></td></tr><tr><td style='padding: 15px;'><p>6. Managing Alerts</p><p style='margin: 0'>Effective Monitoring is all about Automation: Managing Alerts</p></td></tr></tbody></table><br><h4>5. Monitoring the Azure Platform</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Monitoring the Azure Platform: Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. Azure Global Status</p><p style='margin: 0'>Monitoring the Azure Platform: Azure Global Status</p></td></tr><tr><td style='padding: 15px;'><p>3. Personalised Service Health</p><p style='margin: 0'>Monitoring the Azure Platform: Personalised Service Health</p></td></tr></tbody></table><br><h4>6. Monitoring our Azure Solution</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>Monitoring our Azure Solution: Section Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. Review the Status, Health and Activity Logs</p><p style='margin: 0'>Monitoring our Azure Solution: Review the Status, Health and Activity Logs</p></td></tr><tr><td style='padding: 15px;'><p>3. Metrics Explorer</p><p style='margin: 0'>Monitoring our Azure Solution: Metrics Explorer</p></td></tr><tr><td style='padding: 15px;'><p>4. Log Analytics</p><p style='margin: 0'>Monitoring our Azure Solution: Log Analytics</p></td></tr><tr><td style='padding: 15px;'><p>5. Azure Monitor for VMs</p><p style='margin: 0'>Monitoring our Azure Solution: Azure Monitor for VMs</p></td></tr><tr><td style='padding: 15px;'><p>6. Azure Advisor</p><p style='margin: 0'>Monitoring our Azure Solution: Azure Advisor</p></td></tr><tr><td style='padding: 15px;'><p>7. Application Insights</p><p style='margin: 0'>Monitoring our Azure Solution: Application Insights</p></td></tr></tbody></table><br><h4>7. Course Summary</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. A Quick Recap</p><p style='margin: 0'>Course Summary: A Quick Recap</p></td></tr></tbody></table>

Packt

Cloud Computing

Linux

Learn to effectively monitor the performance, health, and availability of Azure Services and your cloud resources

Becoming a Cloud Expert - Microsoft Azure IaaS - Level 2

<p>In today's technology-driven world, a <strong>Diploma in Python Programming</strong> has become indispensable. Python, renowned for its simplicity and versatility, has emerged as a cornerstone in various industries, including Environmental Management. This course, spanning from beginner to expert levels, offers a comprehensive understanding of Python's fundamentals and advanced concepts. </p><p>With modules covering basic data types, control flow, functions, scripting, and libraries like NumPy and Pandas, participants gain proficiency in developing efficient solutions for data analysis and management tasks. <strong>The demand for Python skills is soaring</strong>, <strong>particularly in the UK</strong>, where job opportunities abound, <strong>with salaries averaging £45,000 annually</strong>. </p><p>Enrolling in this <strong>Diploma in Python Programming</strong> course not only opens doors to lucrative career prospects but also equips individuals with the tools to excel in Environmental Management through data-driven insights and automation. Enrich your skill set and stay ahead of the curve by enrolling in this <strong>Diploma in Python Programming</strong><strong>essential</strong> course today.</p><p><strong>Key Features:</strong></p><ul><li>CPD Certified</li><li>Instant e-certificate and hard copy dispatch by next working day</li><li>Fully online, interactive course with audio voiceover</li><li>Developed by qualified professionals in the field</li><li>Self-paced learning and laptop, tablet, smartphone-friendly</li><li>24/7 Learning Assistance</li><li>Discounts on bulk purchases</li></ul><strong>Course Curriculum<strong>:</strong></strong><p><strong>Python Programming: Beginner To Expert</strong></p><ul><li><strong>Module 01:</strong> Introduction to Python Programming from A-Z</li><li><strong>Module 02:</strong> Getting Familiar with Python</li><li><strong>Module 03:</strong> Basic Data Types</li><li><strong>Module 04:</strong> Python Operators</li><li><strong>Module 05:</strong> Advanced Data Types</li><li><strong>Module 06:</strong> Control Flow Part 1</li><li><strong>Module 07:</strong> Control Flow Part 2</li><li><strong>Module 08:</strong> Python Functions</li><li><strong>Module 09:</strong> User Input and Error Handling</li><li><strong>Module 10:</strong> Python Advanced Functions</li><li><strong>Module 11:</strong> Python Scripting and Libraries</li><li><strong>Module 12:</strong> NumPy</li><li><strong>Module 13:</strong> Pandas</li><li><strong>Module 14:</strong> Introduction to OOP</li><li><strong>Module 15:</strong> Advanced OOP</li><li><strong>Module 16:</strong> Starting a Career in Python</li></ul><p><strong>Learning Outcomes:</strong></p><ul><li>Master Python syntax and fundamentals proficiently.</li><li>Apply advanced data types and operators effectively.</li><li>Develop robust control flow structures for efficient programming.</li><li>Utilize Python functions and error handling techniques adeptly.</li><li>Harness the power of object-oriented programming for scalable applications.</li><li>Explore career opportunities and kickstart your journey into the Python industry.</li></ul><p><strong>Accreditation</strong></p><p>All of our courses are fully accredited, including this Diploma in Python Programming Course, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in Python Programming.</p><p><strong>Certification</strong></p><p>Once you've successfully completed your Diploma in Python Programming Course, you will immediately be sent a digital certificate. Also, you can have your printed certificate delivered by post (shipping cost £3.99). Our Diploma in Python Programming Course certification has no expiry dates, although we do recommend that you renew them every 12 months.</p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        20 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">
                            <ul><li>Aspiring programmers seeking a solid foundation in Python.</li><li>Professionals aiming to upgrade their coding skills.</li><li>Students pursuing careers in software development.</li><li>Individuals interested in data analysis and manipulation using Python.</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>Learners do not require any prior qualifications to enrol on this Diploma in Python Programming Course. You just need to have an interest in Diploma in Python Programming Course.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Python Developer - £30K to £60K/year.</li><li>Data Analyst - £25K to £45K/year.</li><li>Software Engineer - £35K to £70K/year.</li><li>Machine Learning Engineer - £40K to £80K/year.</li><li>Data Scientist - £40K to £80K/year.</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4> Digital certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
            <div><p>Once you've successfully completed your course, you will immediately be sent a FREE digital certificate.</p></div>
        <h4> Hard copy certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div><p>Also, you can have your FREE printed certificate delivered by post (shipping cost £3.99 in the UK).<br />For all international addresses outside of the United Kingdom, the delivery fee for a hardcopy certificate will be only £10.<br />Our certifications have no expiry dates, although we do recommend that you renew them every 12 months.</p></div>

                        </div>

Training Express

Diploma in Python Programming Course

<p>Imagine being able to create your own programs and applications with just a few lines of code. With our "Ultimate Python Programming Masterclass," you'll have the power to do just that. Python is a versatile and powerful programming language that is widely used in industries ranging from finance to artificial intelligence. With modules covering everything from lists and tuples to standard libraries and reference files, you'll be well on your way to mastering Python programming in no time.</p><p>But don't just take our word for it - the proof is in the pudding! Our course is designed with a hands-on approach, giving you the opportunity to apply what you've learned to real-world programming scenarios. Whether you're a seasoned programmer or a complete beginner, our "Ultimate Python Programming Masterclass" course is the perfect way to enhance your skills and take your career to the next level.</p><p>So what are you waiting for? Sign up for the "Ultimate Python Programming Masterclass" today and join the millions of programmers worldwide who are already utilising the power of Python to create innovative solutions and applications.</p><p><strong>Learning Outcomes:</strong></p><ul><li>Understand the fundamentals of Python programming and its applications in various industries.</li><li>Analyse and manipulate data using lists, tuples, strings, and dictionaries in Python.</li><li>Develop programming solutions using Python's standard libraries and reference files.</li><li>Create and implement control flow structures using conditions, loops, and statements.</li><li>Utilise advanced features of Python such as magic methods, properties, and iterators.</li></ul><p>"Ultimate Python Programming Masterclass" is a comprehensive course bundle designed to equip learners with the knowledge and skills necessary to master Python programming. This course covers a range of topics, including data manipulation, control flow structures, and advanced Python features. With a hands-on approach and real-world programming scenarios, learners will develop the ability to create powerful applications and solutions using Python.</p><p>This course bundle is ideal for anyone interested in programming or seeking to enhance their programming skills. Whether you're a student, a professional looking to switch careers, or an experienced programmer seeking to learn a new language, "Ultimate Python Programming Masterclass" is the perfect way to take your skills to the next level.</p><p><strong>â±â± Ultimate Python Programming Masterclass</strong><strong> Course Curriculum </strong></p><ul><li><strong>Section 01:</strong> Introduction to Python Programming<strong><br /></strong></li><li><strong>Section 02:</strong> Lists &amp; Tuples<strong><br /></strong></li><li><strong>Section 03: </strong>Strings<strong><br /></strong></li><li><strong>Section 04: </strong>Dictionaries<strong><br /></strong></li><li><strong>Section 05: </strong>Dictionaries Methods<strong><br /></strong></li><li><strong>Section 06: </strong>Conditions, Loops and Statements<strong><br /></strong></li><li><strong>Section 07: </strong>Abstraction-I<strong><br /></strong></li><li><strong>Section 08: </strong>Abstraction-II<strong><br /></strong></li><li><strong>Section 09: </strong>Exceptions<strong><br /></strong></li><li><strong>Section 10: </strong>Magic Methods, Properties and Iterators<strong><br /></strong></li><li><strong>Section 11: </strong>Standard Libraries<strong><br /></strong></li><li><strong>Section 12: </strong>Reference Files</li></ul><p><strong>How is the course assessed?</strong></p><p>Upon completing an online module, you will immediately be given access to a specifically crafted MCQ test. For each test, the pass mark will be set to 60%.</p><p><strong>Exam &amp; Retakes:</strong><br />It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable.</p><p><strong>Certification</strong><br />Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of __ GBP. £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery).</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">
                            <ul><li>Students interested in programming and computer science.</li><li>Professionals seeking to enhance their programming skills.</li><li>Entrepreneurs and business owners looking to create innovative solutions.</li><li>Experienced programmers seeking to learn a new language.</li><li>Individuals looking to switch careers and enter the field of programming.</li></ul>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Python Developer: £35,000 - £70,000</li><li>Data Analyst: £20,000 - £60,000</li><li>Machine Learning Engineer: £40,000 - £90,000</li><li>Software Engineer: £25,000 - £80,000</li><li>Web Developer: £20,000 - £55,000</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Digital certificate -
            £9
        </p>
            <div> <p>You can apply for a CPD Accredited PDF Certificate at the cost of £9.</p> </div>
        <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            £15
        </p>
            <div> <p>Hard copy can be sent to you via post at the expense of £15.</p> </div>

                        </div>

One Education

Ultimate Python Programming Masterclass Course

<p>The world of Artificial Intelligence (AI) is rapidly expanding, with new breakthroughs and applications emerging every day. If you're looking to stay ahead of the curve, then look no further than our comprehensive course, "Learn AI with Python." Through a combination of engaging modules and real-world case studies, our course covers everything from the basics of AI to advanced techniques for detecting patterns and natural language processing.</p><p>With a particular focus on Python programming language, learners will gain insights into Class Imbalance and Grid Search, Adaboost Regressor, Affinity Propagation Model, Gaussian Mixture Model, Logic Programming, Heuristic Search and more. Whether you're a beginner or an experienced programmer, our course will take you on a journey through the fascinating and ever-changing world of AI.</p><p>By the end of the course, learners will be equipped with the knowledge and skills to create AI models and programs that can solve real-world problems. With our expert guidance and in-depth analysis, you'll gain the knowledge and confidence to make informed decisions and navigate the complex and dynamic world of AI. Don't miss out on this opportunity to enhance your skills and advance your career - enroll in "Learn AI with Python" today and join the ranks of successful AI programmers around the world.</p><p><strong>Learning Outcomes:</strong></p><ul><li>Develop an understanding of the principles and practices of Artificial Intelligence (AI)</li><li>Learn effective strategies for detecting patterns and natural language processing</li><li>Develop analytical skills for creating AI models and programs</li><li>Understand Python programming language and its applications in AI</li><li>Be able to make informed decisions and navigate the complex and dynamic world of AI</li></ul><p>The "Learn AI with Python" course is designed to provide a comprehensive understanding of the principles and practices that underpin successful AI programming. Through engaging modules and real-world case studies, learners will gain insights into the basics of AI, advanced techniques for detecting patterns and natural language processing, and effective strategies for creating AI models and programs using Python programming language.</p><p>By the end of the course, learners will be equipped with the knowledge and skills to make informed decisions and navigate the complex and dynamic world of AI. Whether you're a beginner or an experienced programmer, this course is a must-have for anyone interested in the world of AI.</p><p><strong>â±â± Learn AI with Python</strong><strong> Course Curriculum </strong></p><ul><li><strong>Section 01: </strong>Introduction<strong><br /></strong></li><li><strong>Section 02: </strong>Class Imbalance and Grid Search<strong><br /></strong></li><li><strong>Section 03: </strong>Adaboost Regressor<strong><br /></strong></li><li><strong>Section 04: </strong>Detecting patterns with Unsupervised Learning<strong><br /></strong></li><li><strong>Section 05: </strong>Affinity Propagation Model<strong><br /></strong></li><li><strong>Section 06: </strong>Clustering Quality<strong><br /></strong></li><li><strong>Section 07: </strong>Gaussian Mixture Model<strong><br /></strong></li><li><strong>Section 08: </strong>Classifiers<strong><br /></strong></li><li><strong>Section 09: </strong>Logic Programming<strong><br /></strong></li><li><strong>Section 10: </strong>Heuristic Search<strong><br /></strong></li><li><strong>Section 11: </strong>Natural Language Processing</li></ul><p><strong>How is the course assessed?</strong></p><p>Upon completing an online module, you will immediately be given access to a specifically crafted MCQ test. For each test, the pass mark will be set to 60%.</p><p><strong>Exam &amp; Retakes:</strong><br />It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable.</p><p><strong>Certification</strong><br />Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of __ GBP. £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery).</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">
                            <ul><li>Programmers looking to enhance their AI skills</li><li>Business professionals interested in AI applications</li><li>Computer science students interested in AI</li><li>Entrepreneurs looking to incorporate AI into their products or services</li><li>Anyone interested in gaining a comprehensive understanding of AI and its applications</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>There are no formal entry requirements for the course, with enrollment open to anyone!</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>AI Programmer: £30,000 - £70,000 per year</li><li>Data Scientist: £30,000 - £80,000 per year</li><li>Machine Learning Engineer: £35,000 - £90,000 per year</li><li>AI Researcher: £40,000 - £100,000 per year</li><li>Software Developer: £25,000 - £70,000 per year</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Digital certificate -
            £9
        </p>
            <div> <p>You can apply for a CPD Accredited PDF Certificate at the cost of £9.</p> </div>
        <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            £15
        </p>
            <div> <p>Hard copy can be sent to you via post at the expense of £15.</p> </div>

                        </div>

Learn AI with Python Course

<strong>Master MongoDB, an open-source document database and leading NoSQL database that provides high performance, high availability, and automatic scaling. This course covers the MongoDB Community version for beginners and provides over 50 live-running queries, including creating new databases and tables.</strong>
        <p>MongoDB is a No SQL database, which is an open-source, cross-platform, document-oriented database written in C++. MongoDB is a popular document database with powerful capabilities, such as full-text-based search, geospatial queries, data aggregation, and many more features.

The course begins with an introduction to MongoDB and it's features and editions. You will learn to differentiate between SQL and NoSQL databases and set up and install MongoDB on Windows. You will proceed to create a new database and a new collection in MongoDB. You will learn to modify and drop databases and collections. You will learn to insert and display documents in a formatted way using the pretty() method. You will perform various operations on MongoDB using the AND, OR, NOR, and NOT operators. You will explore how to limit records using the limit() method and skip documents using the skip() method. You will create and delete indexes and perform text searches in MongoDB.

Upon completing the course, you will be able to master the concepts of MongoDB NoSQL database, create a new database, and maintain an existing database. You will be able to independently run live queries and use complex queries to manipulate data in the database.

All resources are available at: https://github.com/PacktPublishing/MongoDB-Tutorial-for-Beginners-2022-</p>
        <h2>What You Will Learn</h2><p>Install and set up MongoDB<br>Create a new database and maintain existing databases with MongoDB<br>Implement the sort() method in MongoDB and display sorted results<br>Limit records with limit() and skip documents with skip() method<br>Use find(), explain(), listCommands(), and getCollectionInfos()<br>Create and delete indexes and use search text on collections</p>
        <h2>Audience</h2><p>The course aims at delivering a high-quality learning experience to those who are database beginners, web developers wanting to learn modern database concepts, application developers that would work on databases to store information for their apps, and programmers involved in complex programming that involves data storage and handling. The only prerequisite required to benefit from this course is to have basic knowledge of computer use.</p>
        <h2>Approach</h2><p>The course is delivered step-by-step with explanations included in a tutorial-styled format to make learning MongoDB easier for beginners and to understand how to operate a new database and tables and its concepts with examples of live running queries. The course also provides frequent quizzes and helps refresh your MongoDB skills.</p>
        <h2>Key Features</h2><p>Ace NoSQL with a popular database MongoDB developed for modern applications * Learn to create and run a NoSQL database and maintain an existing database with MongoDB * Master MongoDB and understand database and table concepts with live running queries as examples</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/MongoDB-Tutorial-for-Beginners-2022-</p>
        <h2>About the Author</h2><h3>Amit Diwan</h3><p>Studyopedia was founded by Amit Diwan in 2018 after working for Tutorialspoint , IIT, IASRI, Sitepoint, DU, and C# Corner. Studyopedia sells courses on Udemy, Tutorialspoint, Geeksforgeeks, and Skillshare, providing video courses to master various technologies and programming languages, databases, frameworks, Python, data science, machine learning, Java, Android, C/C++, HTML5, Bootstrap, JavaScript, jQuery, PHP, CSS, WordPress, Drupal, Joomla, Magento, osCommerce, OpenCart, PrestaShop, and other disciplines. Studyopedia delivers high-quality video courses to millions of students and professionals enrolled through their website on multiple programming languages and technologies.</p>
        <h2>Course Outline</h2><h4>1. MongoDB Introduction</h4><p>This section briefly outlines the features and capabilities of MongoDB and how this database system can be used in modern applications.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction and Features</p><p style='margin: 0'>Learn what MongoDB is, its features, editions, and so on.</p></td></tr><tr><td style='padding: 15px;'><p>2. NoSQL Versus SQL (With Examples - MongoDB Versus MySQL)</p><p style='margin: 0'>Learn how SQL and NoSQL are different. We have considered the example of MySQL (SQL-based) and MongoDB (NoSQL based) to understand the concept. We will also see what collections and documents in MongoDB are and how they differ from tables and rows in MySQL.</p></td></tr></tbody></table><br><h4>2. MongoDB Installation and Setup</h4><p>This video demonstrates the installation and setup process of MongoDB.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Video Name</p><p style='margin: 0'>Learn how to download and install MongoDB on Windows 10. We will also see how to set the path and run mongo with mongod (daemon).</p></td></tr></tbody></table><br><h4>3. Create a Database and Collection</h4><p>In this section, you will learn how to create a database and a collection in MongoDB.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. How to create a Database in MongoDB</p><p style='margin: 0'>Learn to create a new database in MongoDB with live examples.</p></td></tr><tr><td style='padding: 15px;'><p>2. How to Create a Collection in MongoDB</p><p style='margin: 0'>Create a new collection in MongoDB with live examples. A database has a group of collections.</p></td></tr></tbody></table><br><h4>4. Drop a Collection and Database</h4><p>This section focuses on the creation of a collection and database in MongoDB.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Drop a Collection</p><p style='margin: 0'>Drop a collection in MongoDB. Dropping a collection deletes the entire collection and all its documents and fields.</p></td></tr><tr><td style='padding: 15px;'><p>2. Drop a Database</p><p style='margin: 0'>Drop a database in MongoDB. Dropping a database deletes the entire database and all its collections.</p></td></tr></tbody></table><br><h4>5. Insert and Display</h4><p>This section demonstrates how to insert documents and display documents in a MongoDB Collection.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Insert Documents</p><p style='margin: 0'>Learn to insert documents in a MongoDB collection. A collection in MongoDB has documents and fields. Insert documents using the following methods: insert(): Insert document; insertOne(): Insert a single document; insertMany(): Insert multiple documents.</p></td></tr><tr><td style='padding: 15px;'><p>2. Display Documents</p><p style='margin: 0'>Learn to display documents in MongoDB, that is, query/fetch documents from a collection.</p></td></tr><tr><td style='padding: 15px;'><p>3. Display Documents in a Formatted Way - pretty() Method</p><p style='margin: 0'>Implement the pretty() strategy in MongoDB and display formatted documents. The find() method displays documents, and pretty() displays in a formatted way.</p></td></tr><tr><td style='padding: 15px;'><p>4. Display Collections</p><p style='margin: 0'>Display all the collections in a specific MongoDB database.</p></td></tr></tbody></table><br><h4>6. MongoDB Operators</h4><p>This section illustrates the various operators available for functions in MongoDB.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. AND Operator</p><p style='margin: 0'>Implement the AND operator in MongoDB and retrieve documents satisfying all the conditions.</p></td></tr><tr><td style='padding: 15px;'><p>2. OR Operator</p><p style='margin: 0'>Learn to implement the OR operator in MongoDB to display documents satisfying at least one condition.</p></td></tr><tr><td style='padding: 15px;'><p>3. NOR Operator</p><p style='margin: 0'>Implement the NOR operator in MongoDB, the opposite of the OR operator.</p></td></tr><tr><td style='padding: 15px;'><p>4. NOT Operator</p><p style='margin: 0'>Learn to implement the NOT operator in MongoDB. It negates the conditions.</p></td></tr></tbody></table><br><h4>8. Sort Records in MongoDB</h4><p>This section explains how to sort records using the sort() method and sort in ascending and descending orders.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Sort Records</p><p style='margin: 0'>Learn to implement the sort() method in MongoDB and display sorted results. The documents can be sorted in ascending as well as descending order. For sort order, use 1 for ascending and -1 for descending order.</p></td></tr></tbody></table><br><h4>9. Text Indexing in MongoDB</h4><p>This section demonstrates how to create and delete a text index in MongoDB.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Create and Delete Text Index</p><p style='margin: 0'>Learn how to create and delete text index in MongoDB.</p></td></tr></tbody></table><br><h4>10. Text Search in MongoDB</h4><p>This section explores how to perform searches, especially text searches in MongoDB.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Text Search on a Collection in MongoDB</p><p style='margin: 0'>Learn how to perform text search with $text in MongoDB.</p></td></tr></tbody></table><br><h4>11. Section Name</h4><p>In this section, we will understand how to fetch documents from a collection in MongoDB.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. find() - Fetch Documents From a Collection</p><p style='margin: 0'>Implement the find() method in MongoDB and display documents, that is, fetch documents from a collection.</p></td></tr><tr><td style='padding: 15px;'><p>2. explain() - Provide Information on the Query Plan</p><p style='margin: 0'>Learn how to implement the explain() method in MongoDB. MongoDB explain() provides information on the query plan.</p></td></tr><tr><td style='padding: 15px;'><p>3. stats() - Display Statistics of a Database</p><p style='margin: 0'>Implement the stats() method in MongoDB. This method returns a document with statistics of the database like collections, views, objects, storageSize, indexes, indexSize, totalSize, and so on.</p></td></tr><tr><td style='padding: 15px;'><p>4. listCommands() - Display a List of All Database Commands</p><p style='margin: 0'>Learn how to implement the listCommands() method in MongoDB. This method displays a list of all database commands</p></td></tr><tr><td style='padding: 15px;'><p>5. getCollectionInfos() - Display Collection Information</p><p style='margin: 0'>Implement the getCollectionInfos() method in MongoDB. This method returns an array of documents with collection information.</p></td></tr></tbody></table><br><h4>12. MongoDB Advanced Topics</h4><p>Here, we will focus on how to aggregate, limit, and skip records.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Aggregate Records in MongoDB</p><p style='margin: 0'>Learn how to use MongoDB's aggregate() method to aggregate records.</p></td></tr><tr><td style='padding: 15px;'><p>2. Limit and Skip Records in a Single Query</p><p style='margin: 0'>Learn how we can limit and skip documents in MongoDB using the LIMIT() and SKIP() methods, respectively.</p></td></tr></tbody></table>

Database Programming

Database

NoSQL

MongoDB

Master MongoDB, an open-source document database and leading NoSQL database that provides high performance, high availability, and automatic scaling. This course covers the MongoDB Community version for beginners and provides over 50 live-running queries, including creating new databases and tables.

MongoDB Tutorial for Beginners (2022)

<div id="course-pills-1">Duration<p>3 Days</p><p>18 CPD hours</p>This course is intended for<p>Application developers who want to build cloud-native applications or redesign existing applications that will run on Google Cloud Platform</p>Overview<p>This course teaches participants the following skills:<br />Use best practices for application development.<br />Choose the appropriate data storage option for application data.<br />Implement federated identity management.<br />Develop loosely coupled application components or microservices.<br />Integrate application components and data sources.<br />Debug, trace, and monitor applications.<br />Perform repeatable deployments with containers and deployment services.<br />Choose the appropriate application runtime environment; use Google Container Engine as a runtime environment and later switch to a no-ops solution with Google App Engine flexible environment.</p><p>Learn how to design, develop, and deploy applications that seamlessly integrate components from the Google Cloud ecosystem. This course uses lectures, demos, and hands-on labs to show you how to use Google Cloud services and pre-trained machine learning APIs to build secure, scalable, and intelligent cloud-native applications.</p></div><div id="course-pills-2"><h4>Best Practices for Application Development</h4><ul><li>Code and environment management.</li><li>Design and development of secure, scalable, reliable, loosely coupled application components and microservices.</li><li>Continuous integration and delivery.</li><li>Re-architecting applications for the cloud.</li></ul><h4>Google Cloud Client Libraries, Google Cloud SDK, and Google Firebase SDK</h4><ul><li>How to set up and use Google Cloud Client Libraries, Google Cloud SDK, and Google Firebase SDK.</li><li>Lab: Set up Google Client Libraries, Cloud SDK, and Firebase SDK on a Linux instance and set up application credentials.</li></ul><h4>Overview of Data Storage Options</h4><ul><li>Overview of options to store application data.</li><li>Use cases for Google Cloud Storage, Cloud Firestore, Cloud Bigtable, Google Cloud SQL, and Cloud Spanner.</li></ul><h4>Best Practices for Using Cloud Firestore</h4><ul><li>Best practices related to using Cloud Firestore in Datastore mode for:Queries, Built-in and composite indexes, Inserting and deleting data (batch operations),Transactions,Error handling.</li><li>Bulk-loading data into Cloud Firestore by using Google Cloud Dataflow.</li><li>Lab: Store application data in Cloud Datastore.</li></ul><h4>Performing Operations on Cloud Storage</h4><ul><li>Operations that can be performed on buckets and objects.</li><li>Consistency model.</li><li>Error handling.</li></ul><h4>Best Practices for Using Cloud Storage</h4><ul><li>Naming buckets for static websites and other uses.</li><li>Naming objects (from an access distribution perspective).</li><li>Performance considerations.</li><li>Setting up and debugging a CORS configuration on a bucket.</li><li>Lab: Store files in Cloud Storage.</li></ul><h4>Handling Authentication and Authorization</h4><ul><li>Cloud Identity and Access Management (IAM) roles and service accounts.</li><li>User authentication by using Firebase Authentication.</li><li>User authentication and authorization by using Cloud Identity-Aware Proxy.</li><li>Lab: Authenticate users by using Firebase Authentication.</li></ul><h4>Using Pub/Sub to Integrate Components of Your Application</h4><ul><li>Topics, publishers, and subscribers.</li><li>Pull and push subscriptions.</li><li>Use cases for Cloud Pub/Sub.</li><li>Lab: Develop a backend service to process messages in a message queue.</li></ul><h4>Adding Intelligence to Your Application</h4><ul><li>Overview of pre-trained machine learning APIs such as Cloud Vision API and Cloud Natural Language Processing API.</li></ul><h4>Using Cloud Functions for Event-Driven Processing</h4><ul><li>Key concepts such as triggers, background functions, HTTP functions.</li><li>Use cases.</li><li>Developing and deploying functions.</li><li>Logging, error reporting, and monitoring.</li></ul><h4>Managing APIs with Cloud Endpoints</h4><ul><li>Open API deployment configuration.</li><li>Lab: Deploy an API for your application.</li></ul><h4>Deploying Applications</h4><ul><li>Creating and storing container images.</li><li>Repeatable deployments with deployment configuration and templates.</li><li>Lab: Use Deployment Manager to deploy a web application into Google App Engine flexible environment test and production environments.</li></ul><h4>Execution Environments for Your Application</h4><ul><li>Considerations for choosing an execution environment for your application or service:Google Compute Engine (GCE),Google Kubernetes Engine (GKE), App Engine flexible environment, Cloud Functions, Cloud Dataflow, Cloud Run.</li><li>Lab: Deploying your application on App Engine flexible environment.</li></ul><h4>Debugging, Monitoring, and Tuning Performance</h4><ul><li>Application Performance Management Tools.</li><li>Stackdriver Debugger.</li><li>Stackdriver Error Reporting.</li><li>Lab: Debugging an application error by using Stackdriver Debugger and Error Reporting.</li><li>Stackdriver Logging.</li><li>Key concepts related to Stackdriver Trace and Stackdriver Monitoring.</li><li>Lab: Use Stackdriver Monitoring and Stackdriver Trace to trace a request across services, observe, and optimize performance.</li></ul></div>

Nexus Human

AWS Certified Developer

Cloud Architecture

AWS Certified Cloud Practitioner

Google Suite

Google Cloud

Developing Applications with Google Cloud

Data Visualization

Python

Pandas

This course first equips you with the fundamentals of Python and then progresses to teach you how to use various libraries such as NumPy, Pandas, Seaborn, Bokeh, and so on. This course contains several mini projects so that, by the end of this course, you will be equipped with the essential tools you need to become a visualization expert.

Data Understanding and Data Visualization with Python

<p><strong>Quick Data Science Approach from Scratch</strong> is an innovatively structured course designed to introduce learners to the fascinating world of data science. The course commences with an enlightening introduction, setting the stage for a deep dive into the essence and significance of data science in the modern era. Learners are guided through a landscape of insights, where misconceptions about data science are addressed and clarified, paving the way for a clear and accurate understanding of the field.</p>
<p>In the second section, the course shifts its focus to pivotal data science concepts. Beginning with an exploration of data types and variables, learners gain a solid foundation in handling various data formats. The journey then leads to mastering descriptive analysis, a critical skill for interpreting and understanding data trends. Learners will also navigate through the intricate processes of data cleaning and feature engineering, essential skills for refining and optimizing data for analysis. The concept of 'Data Thinking Development' is introduced, fostering a mindset that is crucial for effective data science practice.</p>
<p>The final section offers an immersive experience in applying these skills to a real-world scenario. Here, learners engage in defining a problem, choosing suitable algorithms, and developing predictive models. This practical application is designed to cement the theoretical knowledge acquired and enhance problem-solving skills in data science.</p>
<p><strong>Learning Outcomes</strong></p>
<ul>
<li>Build a foundational understanding of data science and its practical relevance.</li>
<li>Develop proficiency in managing various data types and conducting descriptive analysis.</li>
<li>Learn and implement effective data cleaning and feature engineering techniques.</li>
<li>Cultivate a 'data thinking' approach for insightful data analysis.</li>
<li>Apply data science methodologies to real-life problems using algorithmic and predictive techniques.</li>
</ul>
<h3><b>Why choose this Quick Data Science Approach from Scratch<span> course</span>?</b></h3>
<ol>
<li>Unlimited access to the course for a lifetime.</li>
<li>Opportunity to earn a certificate accredited by the <b>CPD Quality Standards</b> and <b>CIQ </b>after completing this course.</li>
<li>Structured lesson planning in line with industry standards.</li>
<li>Immerse yourself in innovative and captivating course materials and activities.</li>
<li>Assessments designed to evaluate advanced cognitive abilities and skill proficiency.</li>
<li>Flexibility to complete the Course at your own pace, on your own schedule.</li>
<li>Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience.</li>
<li>Unlock career resources for CV improvement, interview readiness, and job success.</li>
</ol>
<h3><b>Who is this Quick Data Science Approach from Scratch<span> </span></b><b><span>course </span></b><b>for?</b></h3>
<ul>
<li>Novices aiming to enter the data science field.</li>
<li>Sector professionals integrating data science into their expertise.</li>
<li>Academicians and learners incorporating data science in academic pursuits.</li>
<li>Business strategists utilizing data science for enhanced decision-making.</li>
<li>Statisticians and analysts broadening their expertise into the data science domain.</li>
</ul>
<h3><b>Career path</b></h3>
<ul>
<li><strong>Entry-Level Data Scientist:</strong> £25,000 - £40,000</li>
<li><strong>Beginner Data Analyst:</strong> £22,000 - £35,000</li>
<li><strong>Emerging Business Intelligence Specialist:</strong> £28,000 - £45,000</li>
<li><strong>Data-Focused Research Scientist:</strong> £30,000 - £50,000</li>
<li><strong>Novice Machine Learning Practitioner:</strong> £32,000 - £55,000</li>
<li><strong>Data System Developer (Starter):</strong> £26,000 - £42,000</li>
</ul>
<h3><b>Prerequisites</b></h3>
<p><span>This </span><b>Quick Data Science Approach from Scratch</b><span> does not require you to have any prior qualifications or experience. You can just enrol and start learning.This </span><b>Quick Data Science Approach from Scratch</b><span> was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection.</span></p>
<h3><b>Certification</b></h3>
<p><span>After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8.</span></p><div>
				<h3>
	<span>Course Curriculum</span>
</h3>

<div>
<table width="100%">				<tbody><tr>
					<td colspan="4"> Section 01: Course Overview &amp; Introduction to Data Science</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Science Explanation</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Need of Data Science</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>8 Common mistakes by Aspiring Data Scientists/Data Science Enthusiasts</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Myths about Data Science</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 02: Data Science Concepts</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Types and Variables</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Descriptive Analysis</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Cleaning</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Feature Engineering</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Thinking Development</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Section 03: A Real Life Problem</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Problem Definition</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Algorithms</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Prediction</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Learning Methods</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Assignment</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Assignment - Quick Data Science Approach from Scratch</td>
					<td> </td>
					<td><span><i></i> 00:00:00</span></td>
				</tr>
				</tbody></table></div>

			</div>

Studyhub UK

Data Science

Data Skills

Trainers Course

Digital Skills

Quick Data Science Approach from Scratch

<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 Azure

Microsoft AZ-303

Microsoft AZ-700

Azure Machine Learning

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

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

Python Machine Learning Bootcamp

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

Machine Learning with Real World Projects

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			<h2><b>Course Overview</b></h2>		</div>
				</div>
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				<div>
					<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>
				</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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