Name: Web Application Penetration Tester - QLS Endorsed Bundle
Price: 599 GBP
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Duco Digital Training

IT Certifications

IT Skills

IT Auditing

Ethics

Professional Ethics

IT (Information Technology)

Computing & IT

Thinking about learning more about the ethical use of AI?

The BCS Foundation Award; Understanding the Role of Ethics in the Responsible Use of AI, explores the responsibility of organisations and society towards ensuring AI is implemented for the good of others. It considers the potential harm AI may pose, and the safeguards that can be implemented to ensure it is used safely, ethically and for the good of society. You will be encouraged to explore the benefits associated with AI and the potential value it can add towards the continued evolution of humankind if managed well

You will learn learn about the impact and level of responsibility of AI in business, an understanding of the need to scale up the impact and responsibility of AI to society, understand the potential harm and safeguards and understand the role of humans in an AI world.

Understanding the Role of Ethics in the Responsible Use of AI - BCS Foundation Award

General Admission

<p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Are you ready to embark on an enlightening journey of wisdom with the Big Data bundle, and pave your way to an enriched personal and professional future? If so, then Step into a world of knowledge with our course bundle - Big Data : Data Science, Data Analytics &amp; Data Visualisation.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Delve into eight immersive CPD Accredited courses, each a standalone course:</p><ol fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Big Data Analytics with PySpark Power BI and MongoDB</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Big Data Analytics with PySpark Tableau Desktop and MongoDB</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Building Big Data Pipelines with PySpark MongoDB and Bokeh</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Develop Big Data Pipelines with R &amp; Sparklyr &amp; Tableau</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Develop Big Data Pipelines with R, Sparklyr &amp; Power BI</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Basic Data Analysis</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">SQL for Data Science, Data Analytics and Data Visualization</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Complete Python Machine Learning &amp; Data Science Fundamentals</strong></li></ol><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Traverse the vast landscapes of theory, unlocking new dimensions of understanding at every turn. Let the Big Data : Data Science, Data Analytics &amp; Data Visualisation bundle illuminate your path to wisdom.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The Big Data : Data Science, Data Analytics &amp; Data Visualisation bundle offers a comprehensive exploration into a rich tapestry of vast knowledge across eight carefully curated courses. The journey is designed to enhance your understanding and critical thinking skills. Each course within the bundle provides a deep-dive into complex theories, principles, and frameworks, allowing you to delve into the nuances of the subject matter at your own pace.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">In the framework of the Big Data : Data Science, Data Analytics &amp; Data Visualisation package, you are bestowed with complimentary PDF certificates for all the courses included in this bundle, all without any additional charge. Adorn yourself with the Big Data bundle, empowering you to traverse your career trajectory or personal growth journey with self-assurance. Register today and ignite the spark of your professional advancement!</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">So, don&#39;t wait further and join the Big Data : Data Science, Data Analytics &amp; Data Visualisation community today and let your voyage of discovery begin!</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Learning Outcomes:</strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Attain a holistic understanding in the designated areas of study with the Big Data bundle.</li><li fr-original-style="" style="box-sizing: border-box;">Establish robust bases across each course nestled within the Big Data bundle.</li><li fr-original-style="" style="box-sizing: border-box;">Decipher intricate concepts through the articulate content of the Big Data bundle.</li><li fr-original-style="" style="box-sizing: border-box;">Amplify your prowess in interpreting, scrutinising, and implementing theories.</li><li fr-original-style="" style="box-sizing: border-box;">Procure the capacity to engage with the course material on an intellectual and profound level.</li><li fr-original-style="" style="box-sizing: border-box;">Become proficient in the art of problem-solving across various disciplines.</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Stepping into the Big Data bundle is akin to entering a world overflowing with deep theoretical wisdom. Each course within this distinctive bundle is an individual journey, meticulously crafted to untangle the complex web of theories, principles, and frameworks. Learners are inspired to explore, question, and absorb, thus enhancing their understanding and honing their critical thinking skills. Each course invites a personal and profoundly enlightening interaction with knowledge.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The Big Data bundle shines in its capacity to cater to a wide range of learning needs and lifestyles. It gives learners the freedom to learn at their own pace, forging a unique path of discovery. More than just an educational journey, the Big Data bundle fosters personal growth, enabling learners to skillfully navigate the complexities of the world.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The Big Data bundle also illuminates the route to a rewarding career. The theoretical insight acquired through this bundle forms a strong foundation for various career opportunities, from academia and research to consultancy and programme management. The profound understanding fostered by the Big Data bundle allows learners to make meaningful contributions to their chosen fields. Embark on the Big Data journey and let knowledge guide you towards a brighter future.</p><h2 fr-original-style="" style="box-sizing: border-box;">
        
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                                        85 CPD hours / points
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                                            Accredited by CPD Quality Standards
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        <div fr-original-style="" style="box-sizing: border-box;">Who is this course for?</div>
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                        <div id="who_is_this_course_for" fr-original-style="" style="box-sizing: border-box;">
                            <ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Individuals keen on deepening their firm understanding in the respective fields.</li><li fr-original-style="" style="box-sizing: border-box;">Students pursuing higher education looking for comprehensive theory modules.</li><li fr-original-style="" style="box-sizing: border-box;">Professionals seeking to refresh or enhance their knowledge.</li><li fr-original-style="" style="box-sizing: border-box;">Anyone with a thirst for knowledge and a passion for continuous learning.</li></ul>
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        <div fr-original-style="" style="box-sizing: border-box;">Career path</div>
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                            <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Armed with the Big Data bundle, your professional journey can reach new heights. The comprehensive theoretical knowledge from this bundle can unlock diverse career opportunities across several fields. Whether it&#39;s academic research, consultancy, or programme management, the Big Data bundle lays a solid groundwork.</p>
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        <div fr-original-style="" style="box-sizing: border-box;">Certificates</div>
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                        <div id="optional_extras" fr-original-style="" style="box-sizing: border-box;">
                                    <h4 fr-original-style="" style="box-sizing: border-box;">CPD Certificate Of Completion</h4>
        <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">
            Digital certificate -
            Included
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            <div fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">8 Digital Certificates Are Included With This Bundle</p></div>
        <h4 fr-original-style="" style="box-sizing: border-box;">CPD Quality Standard Hardcopy Certificate (FREE UK Delivery)</h4>
        <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">
            Hard copy certificate -
            &pound;9.99
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            <div fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Hardcopy Transcript - &pound;9.99</p></div>

                        </div>

NextGen Learning

Business Analytics & Intelligence

Big Data Analytics

Data Science

Data Skills

Big Data

Continuous Professional Development (CPD)

Big Data : Data Science, Data Analytics & Data Visualisation - 8 Courses Bundle

<div id="course-pills-1">Duration<p>2 Days</p><p>12 CPD hours</p>This course is intended for<p>The target audience for the DevOps Foundation course includes Management, Operations, Developers, QA and Testing professionals such as:<br />Individuals involved in IT development IT operations or IT service management.<br />Individuals who require an understanding of DevOps principles.<br />IT professionals working within, or about to enter, an Agile Service Design Environment<br />The following IT roles: Automation Architects, Application Developers, Business Analysts, Business Managers, Business Stakeholders, Change Agents, Consultants, DevOps Consultants, DevOps Engineers, Infrastructure Architect, Integration Specialists, IT Directors, IT Managers, IT Operations, IT Team Leaders, Lean Coaches, Network Administrators, Operations Managers, Project Managers, Release Engineers, Software Developers, Software Tester/QA, System Administrators, Systems Engineers, System Integrators, Tool Providers.</p>Overview<p>The learning objectives for DevOps Foundation include an understanding of:<br />DevOps objectives and vocabulary<br />Benefits to the business and IT Principles and practices including Continuous Integration, Continuous Delivery, testing, security and the Three Ways DevOps relationship to Agile, Lean and ITSM<br />Improved workflows, communication and feedback loops<br />Automation practices including deployment pipelines and DevOps toolchains<br />Scaling DevOps for the enterprise<br />Critical success factors and key performance indicators<br />Real-life examples and results</p><p>The DevOps Foundation course provides a baseline understanding of key DevOps terminology to ensure everyone is talking the same language and highlights the benefits of DevOps to support organizational success. Learners will gain an understanding of DevOps, the cultural and professional movement that stresses communication, collaboration, integration, and automation to improve the flow of work between software developers and IT operations professionals. This course prepares you for the DevOps Foundation (DOFD) certification. </p></div><div id="course-pills-2"><h4>Exploring DevOps</h4><ul><li>Defining DevOps</li><li>Why Does DevOps Matter?</li><li></li></ul><h4>Core DevOps Principles</h4><ul><li>The Three Ways</li><li>The First Way</li><li>The Theory of Constraints</li><li>The Second Way</li><li>The Third Way</li><li>Chaos Engineering</li><li>Learning Organizations</li></ul><h4>Key DevOps Practices</h4><ul><li>Continuous Testing, Integration, Delivery, Deployment</li><li>Site Reliability &amp; Resilience Engineering</li><li>DevSecOps</li><li>ChatOps</li><li>Kanban</li></ul><h4>Business and Technology Frameworks</h4><ul><li>Agile</li><li>ITSM</li><li>Lean</li><li>Safety Culture</li><li>Learning Organizations</li><li>Continuous Funding</li></ul><h4>Culture, Behaviors &amp; Operating Models</h4><ul><li>Defining Culture</li><li>Cultural Debt</li><li>Behavioral Models</li><li>Organizational maturity models</li></ul><h4>Automation &amp; Architecting DevOps Toolchains</h4><ul><li>CI/CD</li><li>Cloud, Containers, and Microservices</li><li>AI and Machine Learning</li><li>Automation</li><li>DevOps Toolchains</li></ul><h4>Measurement, Metrics, and Reporting</h4><ul><li>The Importance of Measurement</li><li>DevOps Metrics - Speed, Quality, Stability, Culture</li><li>Change lead/cycle time</li><li>Value Driven Metrics</li></ul><h4>Sharing, Shadowing and Evolving</h4><ul><li>DevOps in the Enterprise</li><li>Roles</li><li>DevOps Leadership</li><li>Organizational Considerations</li><li>Getting Started</li><li>Challenges, Risks, and Critical Success Factors</li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans DevOps Foundation (DevOps Institute) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the DevOps Foundation (DevOps Institute) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

Nexus Human

Learning & Development

DevOps

Lean Foundation

Trainers Course

Cyber Security Foundation

FinOps

DevOps FoundationÂ©

<div id="course-pills-1">Duration<p>1 Days</p><p>6 CPD hours</p>This course is intended for<p>This introductory-level course is great for experienced technical professionals working in a wide range of industries, such as software development, data science, marketing and advertising, finance, healthcare, and more, who are looking to use the latest AI and machine learning techniques in their day to day. The hands-on labs in this course use Python, so you should have some familiarity with Python scripting basics.</p>Overview<p>Working in an interactive learning environment, led by our engaging OpenAI expert you'll:<br />Understand the capabilities and products offered by OpenAI and how to access them through the OpenAI API.<br />set up an OpenAI environment on Azure, including creating an Azure virtual machine and configuring the environment to connect to Azure resources.<br />Gain hands-on experience building a GPT-3 based chatbot on Azure and implement advanced natural language processing capabilities.<br />Use the OpenAI API to access GPT-3 and generate high-quality text<br />Learn how to use Whisper to improve the quality of text generation.<br />Understand the capabilities of DALL-E and use it to generate images for unique and engaging visuals.</p><p>Geared for technical professionals, Quick Start to Azure AI Basics for Technical Users is a fun, fast paced course designed to quickly get you up to speed with OpenAI?s powerful tools and functionality, and to provide hands-on experience in setting up an OpenAI environment on Azure. Guided by our AI expert, you?ll explore the capabilities of OpenAI's GPT-3, Whisper and DALL-E, and build a chatbot on Azure. It will provide you with the knowledge and resources to continue your journey in AI and machine learning and have a good understanding of the potential of OpenAI and Azure for your projects. First, you?ll dive into the world of OpenAI, learning about its products and the capabilities they offer. You'll also discover how Azure's offerings for AI and machine learning can complement OpenAI's tools and resources, providing you with a powerful combination for your projects. And don't worry if you're new to Azure, we'll walk you through the process of setting up an account and creating a resource group. As you progress through the course, you'll get the chance to work with OpenAI's GPT-3, one of the most advanced large language models available today. You'll learn how to use the OpenAI API to access GPT-3 and discover how to use it to generate high-quality text quickly and easily. And that's not all, you'll also learn how to build a GPT-3 based chatbot on Azure, giving you the opportunity to implement advanced natural language processing capabilities in your chatbot projects. The course will also cover OpenAI Whisper, an OpenAI tool that can improve the quality of text generation, allowing you to create more coherent and natural language content. And you will learn about OpenAI DALL-E, an OpenAI tool that can generate images, giving you the ability to create unique and engaging visuals to enhance your content and projects.</p></div><div id="course-pills-2"><h4>Introduction to OpenAI and Azure</h4><ul><li>Explore OpenAI and its products, as well as Azure's offerings for AI and Machine Learning, allowing you to understand the tools and resources available to you for your AI projects. </li><li>Explore OpenAI and its products </li><li>Explore Azure and its offerings for AI and Machine Learning </li></ul><h4>Get Hands-On: Setting up an OpenAI environment on  Azure</h4><ul><li>Walk through the process of setting up an OpenAI environment on Azure, giving you the hands-on experience needed to start building your own projects using OpenAI and Azure. </li><li>Create an Azure virtual machine and installing the OpenAI SDK </li><li>Configure the OpenAI environment and connecting to Azure resources</li></ul><h4>Explore OpenAI GPT-3 </h4><ul><li>Learn about GPT-3, one of OpenAI's most powerful language models, and how to use it to generate high quality text, giving you the ability to create natural language content quickly and easily. </li><li>Review GPT-3 and its capabilities </li><li>Use the OpenAI API to access GPT-3 </li></ul><h4>Get Hands-on: Building a GPT-3 based chatbot on Azure</h4><ul><li>Learn how to build a GPT-3 based chatbot on Azure, giving you the opportunity to learn how to implement advanced natural language processing capabilities in your chatbot projects. </li><li>Setup an Azure Function and creating a chatbot </li><li>Integrate GPT-3 with the chatbot</li></ul><h4>OpenAI Whisper</h4><ul><li>Explore Whisper, an OpenAI tool that can improve the quality of text generation, allowing you to create more coherent and natural language content. </li><li>Explore Whisper and its capabilities </li><li>Use Whisper to improve the quality of text generation</li></ul><h4>OpenAI DALL-E</h4><ul><li>Explore DALL-E, an OpenAI tool that can generate images, giving you the ability to create unique and engaging visuals to enhance your content and projects. </li><li>Explore DALL-E and its capabilities </li><li>Use the OpenAI API to access DALL-E </li></ul><h4>What?s Next: Keep Going! </h4><ul><li>Other ways OpenAI can impact your day to day </li><li>Explore great places to check for expanded tools and add-ons for Azure OpenAI </li><li>Where to go for help and support</li></ul><h4>Quick Look at Generative AI and its Business  Implications</h4><ul><li>Understanding Generative AI </li><li>Generative AI in Business </li><li>Ethical considerations of Generative AI </li></ul></div>

Generative AI

NLP Practitioner

Microsoft Azure

Azure Machine Learning

Azure Cognitive Services

AI (Artificial Intelligence)

Quick Start to Using Azure AI for Technical Users (TTAI2330)

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<h2><strong>About this Training Course</strong></h2>
<p>There are various kinds of geophysical data available. They are separated into seismic and non-seismic (multi-physics) data. Non-seismic or multi-physics data (which includes gravity, magnetics, electrical, electromagnetics, spectral etc - apart from providing complimentary information to seismic) is the main source of information for very shallow subsurface applications such as engineering, mapping pollution, archaeology, geothermal energy, and related areas. This 5 full-day blended course will focus specifically on seismic data which is the main method used in the Oil &amp; Gas industry.</p>
<p>In this blended course, participants will be equipped to understand that seismic data represents the movement of the surface, resulting from waves generated by a source, dynamite or vibrator which are reflected by changes in the subsurface rocks. The basic principles of acquisition and processing will be explained and insights into advanced methods, allowing a much more accurate interpretation of seismic data than previously considered possible, will also be provided.</p>
<p>This blended course contains an introduction to Machine Learning and its important role in all aspects of seismic acquisition, processing, and interpretation. There is no need to know in detail how the algorithms work internally but it is necessary to know how to use them correctly to achieve optimum results.</p>
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<p>By attending this course, participants will be able to acquire the following:</p>
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<li>Obtain an understanding of the strengths and limitations of geophysical methods, specifically seismic, and the costs and risks involved, and how to reduce these.</li>
<li>Be able to communicate more effectively with staff in other disciplines.</li>
<li>Understand the potential applications of seismic data and know how to formulate the requirements needed for prospect and field evaluation.</li>
<li>Gain an awareness of modern seismic technology.</li>
<li>Apply the learning in a series of practical, illustrative exercises.</li>
<li>Know what types of questions to ask to assess the necessary quality of a seismic project in its role in a sequence of E&amp;P activities</li>
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Target Audience 

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<p>The blended course is intended for <strong>non-geophysicists who have intensive interaction with geophysicists</strong>. But it may be of interest to those who want to know about the recent progress made in geophysics, leading to amazing imaging results, which could not be imagined a decade ago. The blended course will bring to the attention of the geologists, petrophysicists and reservoir/petroleum engineers an awareness of how the data they will work with is acquired and processed by the geophysicist. It will introduce the concepts that are of importance in geophysics and thus relevant for non-geophysicists to know and be able to communicate with geophysicists as well as formulate their requests.</p>
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Trainer

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<p>Your expert course leader has degree in Geology (University of Leiden), a Master's degree in Theoretical Geophysics (University of Utrecht) and a PhD in Utrecht on 'Full wave theory and the structure of the lower mantle'. This involved forward modelling of P- and S-waves diffracted around the core-mantle boundary and comparison of the frequency-dependent attenuation of the signal with those obtained from major earthquakes observed at long offsets in the 'shadow zone' of the core. These observations were then translated into rock properties of the D' transition zone.</p>
<p>After his PhD, he joined Shell Research in The Netherlands to develop methods to predict lithology and pore-fluid based on seismic, petrophysical and geological data. He subsequently worked for Shell in London to interpret seismic data from the Central North Sea Graben. As part of the Quantitative Interpretation assignment, he was also actively involved in managing, processing and interpreting Offshore Seismic Profiling experiments.</p>
<p>After his return to The Netherlands, he headed a team for the development of 3D interpretation methods using multi-attribute statistical and pattern recognition analysis on workstations. After a period of Quality Assurance of 'Contractor' software for seismic processing, he became responsible for Geophysics in the Shell Learning Centre. During that period, he was also a part-time professor in Applied Geophysics at the University of Utrecht.</p>
<p>From 2001 to 2005, he worked on the development of Potential Field Methods (Gravity, Magnetics) for detecting oil and gas. Finally, he became a champion on the use of EM methods and became involved in designing acquisition, processing and interpretation methods for Marine Controlled Source EM (CSEM) methods.</p>
<p>After his retirement from Shell, he founded his own company, specialising in courses on acquisition, processing and interpretation of geophysical data (seismic, gravity, magnetic and electromagnetic data), providing courses to International and National energy companies.</p>
<p>In the last couple of years, he became keenly interested in the use of Machine Learning in Geophysics. Apart from incorporating 'Artificial Intelligence' in his courses, he also developed a dedicated Machine Learning course for geophysics.</p>
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POST TRAINING COACHING SUPPORT (OPTIONAL)

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<p>To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster.<br />
Request for further information post training support and fees applicable</p>
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EnergyEdge - Training for a Sustainable Energy Future

Renewable Energy

Oil & Gas

Fundamentals of Seismic for Non-Geophysicists

Team of 10, pp

Normal Fee, pp

Packt

Statistics

Linear Algebra

Probability

Python

Pandas

Start your data science journey with this carefully constructed comprehensive course and get hands-on experience with Python for data science. Gain in-depth knowledge about core Python and essential mathematical concepts in linear algebra, probability, and statistics. Complete data science training with 13+ hours of content.

Data Science 101: Methodology, Python, and Essential Math

<strong>In this self-paced course, you will learn how to use TensorFlow 2 to build deep neural networks. You will learn the basics of machine learning, classification, and regression. We will also discuss the connection between artificial and biological neural networks and how that inspires our thinking in deep learning.</strong>
        <p>TensorFlow is the world's most popular library for deep learning, and it is built by Google. It is the library of choice for many companies doing AI and machine learning. So, if you want to do deep learning, you got to know TensorFlow.

In this course, you will learn how to use TensorFlow 2 to build deep neural networks. We will first start by learning the basics of machine learning, classification, and regression. Then in the next section, we will understand the connection between artificial neural networks and biological neural networks and how that inspires our thinking in the field of deep learning.

In the last two sections, you will learn about loss functions to understand mean squared error, binary cross entropy, and categorical cross entropy and gradient descent to understand stochastic gradient descent, momentum, variable and adaptive learning rates, and Adam optimization.

By the end of this course, we will have understood how to use TensorFlow for artificial neural networks in deep learning.</p>
        <h2>What You Will Learn</h2><p>Understand what machine learning is<br>Build linear models with TensorFlow 2<br>Learn how to build deep neural networks with TensorFlow 2<br>Learn how to perform image classification and regression with ANN<br>Learn loss functions such as mean-squared error and cross-entropy loss<br>Learn about stochastic gradient descent, momentum, and Adam optimization</p>
        <h2>Audience</h2><p>This course is designed for anyone interested in deep learning and machine learning, anyone who wants to implement deep neural networks in TensorFlow 2, or anyone interested in building a foundation for convolutional neural networks, recurrent neural networks, LSTMs (Long Short Term Memory), and transformers.<br><br>One must have decent Python programming skills and should be comfortable with data science libraries such as NumPy and Matplotlib.</p>
        <h2>Approach</h2><p>In this self-paced course, you will learn how to use TensorFlow 2 to build artificial neural networks. The course is well-balanced with theory that explains the ANN concepts and hands-on coding exercises for practical understanding.</p>
        <h2>Key Features</h2><p>Understand the utilization of TensorFlow 2 to construct artificial neural networks * The course covers the basics of machine learning, classification, and regression * Explore the connection between artificial neural networks and biological neural networks</p>
        
        <h2>About the Author</h2><h3>Lazy Programmer</h3><p>The Lazy Programmer, a distinguished online educator, boasts dual master's degrees in computer engineering and statistics, with a decade-long specialization in machine learning, pattern recognition, and deep learning, where he authored pioneering courses. His professional journey includes enhancing online advertising and digital media, notably increasing click-through rates and revenue. As a versatile full-stack software engineer, he excels in Python, Ruby on Rails, C++, and more. His expansive knowledge covers areas like bioinformatics and algorithmic trading, showcasing his diverse skill set. Dedicated to simplifying complex topics, he stands as a pivotal figure in online education, adeptly navigating students through the nuances of data science and AI.</p>
        <h2>Course Outline</h2><h4>1. Welcome</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>In this video, we will introduce the author and understand the course learning objective.</p></td></tr><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>In this video, we will introduce the author and understand the course learning objective.</p></td></tr><tr><td style='padding: 15px;'><p>2. Outline</p><p style='margin: 0'>In this video, we will understand the course learning approach and what is required to start with this course. Then we will also understand what is covered in this course.</p></td></tr><tr><td style='padding: 15px;'><p>2. Outline</p><p style='margin: 0'>In this video, we will understand the course learning approach and what is required to start with this course. Then we will also understand what is covered in this course.</p></td></tr></tbody></table><br><h4>2. Machine Learning and Neurons</h4><p>In this section, we will cover a crash course on machine learning and neurons. You will learn about classification and regression, and their TensorFlow implementations.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What Is Machine Learning?</p><p style='margin: 0'>In this video, we will understand what machine learning is and get a clear idea about ML (machine learning).</p></td></tr><tr><td style='padding: 15px;'><p>2. Code Preparation (Classification Theory)</p><p style='margin: 0'>In this video, we will take a crash course in linear classification for TensorFlow 2.0.</p></td></tr><tr><td style='padding: 15px;'><p>3. Classification Notebook</p><p style='margin: 0'>In this video, we will understand linear classification with the help of breast cancer dataset example.</p></td></tr><tr><td style='padding: 15px;'><p>4. Code Preparation (Regression Theory)</p><p style='margin: 0'>In this video, we will take a crash course in linear regression for TensorFlow 2.0.</p></td></tr><tr><td style='padding: 15px;'><p>5. Regression Notebook</p><p style='margin: 0'>In this video, we will understand linear regression by proving the Mosse law true.</p></td></tr><tr><td style='padding: 15px;'><p>6. The Neuron</p><p style='margin: 0'>In this video, we will understand the neuron.</p></td></tr><tr><td style='padding: 15px;'><p>7. How Does a Model 'Learn'?</p><p style='margin: 0'>In this video, we will understand how a model 'learns'.</p></td></tr><tr><td style='padding: 15px;'><p>8. Making Predictions</p><p style='margin: 0'>In this video, we will be talking about another important part of creating a model, which is making predictions.</p></td></tr><tr><td style='padding: 15px;'><p>9. Saving and Loading a Model</p><p style='margin: 0'>In this video, you will learn how to save a model and then load it later.</p></td></tr><tr><td style='padding: 15px;'><p>10. Why Keras?</p><p style='margin: 0'>In this video, we will understand why we use Keras in this course and not TensorFlow 2.</p></td></tr><tr><td style='padding: 15px;'><p>11. Suggestion Box</p><p style='margin: 0'>In this video, we will have a look at the suggestion box where we can add feedback for this course.</p></td></tr></tbody></table><br><h4>3. Feedforward Artificial Neural Networks</h4><p>In this section, we will cover feedforward artificial neural networks.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Artificial Neural Networks Section Introduction</p><p style='margin: 0'>In this video, we will get introduced to artificial neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>2. Forward Propagation</p><p style='margin: 0'>In this video, you will learn about forward propagation as it is related to neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>3. The Geometrical Picture</p><p style='margin: 0'>In this video, we will understand the geometrical picture by understanding why neural networks are so important instead of a single neural network.</p></td></tr><tr><td style='padding: 15px;'><p>4. Activation Functions</p><p style='margin: 0'>In this video, you will learn about activation functions.</p></td></tr><tr><td style='padding: 15px;'><p>5. Multiclass Classification</p><p style='margin: 0'>In this video, you will learn about multiclass classification.</p></td></tr><tr><td style='padding: 15px;'><p>6. How to Represent Images</p><p style='margin: 0'>In this video, we will discuss data and understand how to represent images.</p></td></tr><tr><td style='padding: 15px;'><p>7. Code Preparation (Artificial Neural Networks)</p><p style='margin: 0'>In this video, we will work with the MNIST dataset and ANN code preparation.</p></td></tr><tr><td style='padding: 15px;'><p>8. ANN for Image Classification</p><p style='margin: 0'>In this video, we will work on image classification with the MNIST dataset.</p></td></tr><tr><td style='padding: 15px;'><p>9. ANN for Regression</p><p style='margin: 0'>In this video, we will understand regression in ANN.</p></td></tr><tr><td style='padding: 15px;'><p>10. How to Choose Hyperparameters</p><p style='margin: 0'>In this video, we will understand how to choose hyperparameters.</p></td></tr></tbody></table><br><h4>4. In-Depth: Loss Functions</h4><p>In this optional section, we will dive deeper into loss functions.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Mean Squared Error</p><p style='margin: 0'>In this video, we will understand MSE (Mean Squared Error) from a probabilistic perspective.</p></td></tr><tr><td style='padding: 15px;'><p>2. Binary Cross Entropy</p><p style='margin: 0'>In this video, you will learn about binary cross entropy, which is the correct loss function to use for binary classification.</p></td></tr><tr><td style='padding: 15px;'><p>3. Categorical Cross Entropy</p><p style='margin: 0'>In this video, you will learn about categorical cross entropy, which is used in multiclass classification.</p></td></tr></tbody></table><br><h4>5. In-Depth: Gradient Descent</h4><p>In this optional section, we will dive deeper into gradient descent.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Gradient Descent</p><p style='margin: 0'>In this video, we will get introduced to gradient descent.</p></td></tr><tr><td style='padding: 15px;'><p>2. Stochastic Gradient Descent</p><p style='margin: 0'>In this video, you will learn about stochastic gradient descent.</p></td></tr><tr><td style='padding: 15px;'><p>3. Momentum</p><p style='margin: 0'>In this video, you will learn about momentum.</p></td></tr><tr><td style='padding: 15px;'><p>4. Variable and Adaptive Learning Rates</p><p style='margin: 0'>In this video, we will discuss variable and adaptive learning rates.</p></td></tr><tr><td style='padding: 15px;'><p>5. Adam Optimization (Part 1)</p><p style='margin: 0'>In this video, we will talk about Adam optimization, get introduced to Adam, and understand its basics.</p></td></tr><tr><td style='padding: 15px;'><p>6. Adam Optimization (Part 2)</p><p style='margin: 0'>In this video, we will understand more about Adam optimization.</p></td></tr></tbody></table>

TensorFlow

In this self-paced course, you will learn how to use TensorFlow 2 to build deep neural networks. You will learn the basics of machine learning, classification, and regression. We will also discuss the connection between artificial and biological neural networks and how that inspires our thinking in deep learning.

Deep Learning - Artificial Neural Networks with TensorFlow

<p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Are you ready to embark on an enlightening journey of wisdom with the CNC (Computer Numerical Control) Programming bundle, and pave your way to an enriched personal and professional future? If so, then Step into a world of knowledge with our course bundle - CNC (Computer Numerical Control) Programming.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Delve into eight immersive CPD Accredited courses, each a standalone course:</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 01:</strong> An Introduction to CNC Programming<br fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 02:</strong> C++ Programming Certificate Course<br fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 03: </strong>JavaScript Basics<br fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 04: </strong>Computer Science With Python<br fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 05: </strong>Machine Learning with Python Course<br fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 06: </strong>Computer Simulation of Realistic Mathematical Models<br fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 07:</strong> Building Your Own Computer Course<br fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 08:</strong> 3D Modeling for 3D Printing</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Traverse the vast landscapes of theory, unlocking new dimensions of understanding at every turn. Let the CNC (Computer Numerical Control) Programming bundle illuminate your path to wisdom.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The CNC (Computer Numerical Control) Programming bundle offers a comprehensive exploration into a rich tapestry of vast knowledge across eight carefully curated courses. The journey is designed to enhance your understanding and critical thinking skills. Each course within the bundle provides a deep-dive into complex theories, principles, and frameworks, allowing you to delve into the nuances of the subject matter at your own pace.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">In the framework of the CNC (Computer Numerical Control) Programming package, you are bestowed with complimentary PDF certificates for all the courses included in this bundle, all without any additional charge. Adorn yourself with the CNC (Computer Numerical Control) Programming bundle, empowering you to traverse your career trajectory or personal growth journey with self-assurance. Register today and ignite the spark of your professional advancement!</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">So, don&#39;t wait further and join the CNC (Computer Numerical Control) Programming community today and let your voyage of discovery begin!</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Learning Outcomes:</strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Attain a holistic understanding in the designated areas of study with the CNC (Computer Numerical Control) Programming bundle.</li><li fr-original-style="" style="box-sizing: border-box;">Establish robust bases across each course nestled within the CNC (Computer Numerical Control) Programming bundle.</li><li fr-original-style="" style="box-sizing: border-box;">Decipher intricate concepts through the articulate content of the CNC (Computer Numerical Control) Programming bundle.</li><li fr-original-style="" style="box-sizing: border-box;">Amplify your prowess in interpreting, scrutinising, and implementing theories.</li><li fr-original-style="" style="box-sizing: border-box;">Procure the capacity to engage with the course material on an intellectual and profound level.</li><li fr-original-style="" style="box-sizing: border-box;">Become proficient in the art of problem-solving across various disciplines.</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Stepping into the CNC (Computer Numerical Control) Programming bundle is akin to entering a world overflowing with deep theoretical wisdom. Each course within this distinctive bundle is an individual journey, meticulously crafted to untangle the complex web of theories, principles, and frameworks. Learners are inspired to explore, question, and absorb, thus enhancing their understanding and honing their critical thinking skills. Each course invites a personal and profoundly enlightening interaction with knowledge.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The CNC (Computer Numerical Control) Programming bundle shines in its capacity to cater to a wide range of learning needs and lifestyles. It gives learners the freedom to learn at their own pace, forging a unique path of discovery. More than just an educational journey, the CNC (Computer Numerical Control) Programming bundle fosters personal growth, enabling learners to skillfully navigate the complexities of the world.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The CNC (Computer Numerical Control) Programming bundle also illuminates the route to a rewarding career. The theoretical insight acquired through this bundle forms a strong foundation for various career opportunities, from academia and research to consultancy and programme management. The profound understanding fostered by the CNC (Computer Numerical Control) Programming bundle allows learners to make meaningful contributions to their chosen fields. Embark on the CNC (Computer Numerical Control) Programming journey and let knowledge guide you towards a brighter future.</p><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">CPD</div>
    </h2>

                        <div id="cpd" fr-original-style="" style="box-sizing: border-box;">
                            <div fr-original-style="" style="box-sizing: border-box;">
                                <div fr-original-style="" style="box-sizing: border-box;">
                                    <div fr-original-style="" style="box-sizing: border-box;">
                                        80 CPD hours / points
                                    </div>
                                        <div fr-original-style="" style="box-sizing: border-box;">
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for" fr-original-style="" style="box-sizing: border-box;">
                            <ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Individuals keen on deepening their firm understanding in the respective fields.</li><li fr-original-style="" style="box-sizing: border-box;">Students pursuing higher education looking for comprehensive theory modules.</li><li fr-original-style="" style="box-sizing: border-box;">Professionals seeking to refresh or enhance their knowledge.</li><li fr-original-style="" style="box-sizing: border-box;">Anyone with a thirst for knowledge and a passion for continuous learning.</li></ul>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Career path</div>
    </h2>

                        <div id="career_path" fr-original-style="" style="box-sizing: border-box;">
                            <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Armed with the CNC (Computer Numerical Control) Programming bundle, your professional journey can reach new heights. The comprehensive theoretical knowledge from this bundle can unlock diverse career opportunities across several fields. Whether it&#39;s academic research, consultancy, or programme management, the CNC (Computer Numerical Control) Programming bundle lays a solid groundwork.</p>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Certificates</div>
    </h2>

                        <div id="optional_extras" fr-original-style="" style="box-sizing: border-box;">
                                    <h4 fr-original-style="" style="box-sizing: border-box;">CPD Accredited Certificate</h4>
        <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">
            Digital certificate -
            Included
        </p>
        <h4 fr-original-style="" style="box-sizing: border-box;">CPD Quality Standard Hardcopy Certificate (FREE UK Delivery)</h4>
        <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">
            Hard copy certificate -
            &pound;9.99
        </p>
            <div fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Hardcopy Transcript: &pound;9.99</p></div>

                        </div>

Online Course Creation

Computing Skills

Programming Fundamentals

Computer Programming

CNC (Computer Numerical Control) Programming - 8 Courses Bundle

<p>As the world becomes increasingly data-driven, the demand for skilled data scientists continues to grow at an exponential rate. In the UK alone, the number of data science jobs has increased by over 300% in the past five years, according to recent statistics. This has created a massive opportunity for individuals with the skills and expertise to analyse data, draw insights, and make informed decisions.</p><p>The <strong>Data Scientist-Quality Licence Scheme (QLS) endorsed courses</strong> bundle is designed to help individuals tap into this growing demand by providing a comprehensive set of courses that cover the core skills and knowledge needed to become a data scientist. With<strong> 10 QLS-endorsed certificates included free of charge</strong>, learners can be confident that their learning is backed by a reputable and respected organisation.</p><p>Data science is a rapidly growing field, and individuals who possess the skills and expertise to analyse data and draw insights are in high demand. In fact, recent statistics show that the demand for data scientists in the UK has increased by over 300% in the past five years, with no signs of slowing down. By learning data science, individuals can position themselves for success in a variety of fields, from finance and healthcare to marketing and technology.</p><p>So why wait? Sign up for the Data Scientist-Quality Licence Scheme (QLS) endorsed courses bundle today and take the first step towards a lucrative and rewarding career in data science. With a wide range of courses to choose from and 10 QLS-endorsed certificates included free of charge, there's never been a better time to invest in yourself and your future.</p><p>Sign up for the Data Scientist-Quality Licence Scheme (QLS) endorsed courses bundle today and unlock your full potential.</p><p><strong>Learning Outcomes:</strong></p><ul><li>Gain proficiency in R programming for data science, including data visualisation and statistical analysis.</li><li>Develop a comprehensive understanding of statistical concepts in R, including hypothesis testing and regression analysis.</li><li>Acquire essential knowledge of data science and machine learning with R, including supervised and unsupervised learning algorithms and data analysis techniques.</li><li>Learn the fundamentals of CSS web development and responsive web design.</li><li>Develop mastery in Bootstrap 5 course and responsive web design techniques.</li><li>Gain a foundation in HTML5 web development, including website design and multimedia integration.</li><li>Acquire advanced knowledge of statistics and probability for data science and machine learning.</li></ul><p><strong>Please Note:</strong> Imperial Academy is One Education approved resale partner for Quality Licence Scheme Endorsed courses.</p><p>The Data Scientist-Quality Licence Scheme (QLS) endorsed courses bundle is designed to provide individuals with the skills and knowledge needed to succeed in the field of data science. This bundle includes a range of courses covering topics such as R programming, statistical analysis, machine learning, web development, and more.</p><p>With the R Programming for Data Science course, learners will gain proficiency in R programming, including data visualisation and statistical analysis. The Statistical Concepts in R course offers a comprehensive overview of statistical concepts in R, including hypothesis testing and regression analysis. The 2021 Data Science &amp; Machine Learning with R from A-Z course provides an in-depth understanding of data science and machine learning with R. The CSS Web Development Crash Course offers a foundation in CSS web development, while the Masterclass Bootstrap 5 Course - Responsive Web Design provides an in-depth exploration of Bootstrap 5 course and responsive web design techniques. The Statistics &amp; Probability for Data Science &amp; Machine Learning course offers an advanced exploration of statistics and probability for data science and machine learning.</p><p><strong>This bundle comprises the following courses:</strong></p><p><strong>QLS Endorsed Courses:</strong></p><ul><li><strong>Course 01:</strong> R Programming for Data Science</li><li><strong>Course 02:</strong> Statistical Concepts in R</li><li><strong>Course 03:</strong> 2021 Data Science &amp; Machine Learning with R from A-Z</li><li><strong>Course 04:</strong> CSS Web Development Crash Course</li><li><strong>Course 05:</strong> Masterclass Bootstrap 5 Course - Responsive Web Design</li><li><strong>Course 06:</strong> Basic HTML 5</li><li><strong>Course 07:</strong> Statistics &amp; Probability for Data Science &amp; Machine Learning</li><li><strong>Course 08:</strong> Excel Data Analysis</li><li><strong>Course 09:</strong> Career Coaching</li><li><strong>Course 10:</strong> The Unstoppable Self-Confidence &amp; High Self-Esteem Blueprint</li></ul><p><strong>Certificate: </strong></p><p>The Quality Licence Scheme ensures excellence and quality. These courses' learning outcomes provide the learners with high competence in their line of work as well. So, QLS-endorsed courses deliver not only deep knowledge but also add great value to the resume.</p><p>After completing the assessments, learners will be eligible for a prestigious QLS Certificate for each endorsed course in this bundle. Moreover, the certificates will be delivered to their home free of cost!</p><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <ul><li>Individuals seeking to enter the field of data science or advance their career in this field.</li><li>Programmers looking to expand their skills and knowledge base in data science and machine learning.</li><li>Students pursuing a career in data science, statistics, or related fields.</li><li>Professionals looking to enhance their data analysis skills.</li><li>Anyone with an interest in learning more about data science and machine learning.</li></ul>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Data Analyst - £20,000 to £50,000 per year</li><li>Data Scientist - £30,000 to £90,000 per year</li><li>Business Intelligence Analyst - £25,000 to £65,000 per year</li><li>Machine Learning Engineer - £40,000 to £100,000 per year</li><li>Financial Analyst - £25,000 to £65,000 per year</li><li>Statistician - £20,000 to £65,000 per year</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            Included
        </p>

                        </div>

Imperial Academy

10 QLS Endorsed Courses for Data Scientist  | 10 Endorsed Certificates Included | Life Time Access

Data Scientist - QLS Endorsed Courses

<p>Picture this: you're a highly skilled Web Application Penetration Tester, working to identify vulnerabilities and protect businesses from cyber-attacks. In today's world, where cyber-attacks are becoming more common and sophisticated, the demand for experts in this field is on the rise. In fact, according to Cybersecurity Ventures, cybercrime is expected to cost the world $6 trillion annually by 2021. This highlights the importance of having highly skilled professionals in the field of cybersecurity.</p><p>The <strong>Web Application Penetration Tester - QLS Endorsed Bundle </strong> is designed to equip learners with the skills and knowledge needed to become experts in the field of web application penetration testing. This bundle includes courses such as the Web Application Penetration Testing Course, Secure Programming of Web Applications, Python Programming Bible, and Azure Machine Learning. These courses cover a wide range of topics, including web application security, programming languages, and machine learning.</p><p>Why should people in the UK learn about web application penetration testing? Well, cybercrime is a global problem, and the UK is not immune. In fact, according to the UK government, 46% of UK businesses reported experiencing a cyber-attack in 2020. This highlights the need for highly skilled cybersecurity professionals in the UK. In addition, the UK cybersecurity industry is growing rapidly, with a market value of £8.3 billion in 2020.</p><p>By completing the Web Application Penetration Tester - QLS Endorsed Bundle, learners will gain highly sought-after skills that are in high demand in the UK and around the world. With<strong> 10 QLS Endorsed certificate hardcopy free of charge,</strong> learners will have the credentials to prove their expertise and stand out in a competitive job market.</p><p>Take the first step towards a rewarding career in cybersecurity with the Web Application Penetration Tester - QLS Endorsed Bundle.</p><p><strong>Learning Outcomes:</strong></p><ul><li>Develop knowledge and skills in web application security and penetration testing.</li><li>Learn to identify and exploit vulnerabilities in web applications.</li><li>Develop knowledge and skills in programming languages such as Python and Java.</li><li>Learn to use machine learning to enhance web application security.</li><li>Develop the ability to write secure code for web applications.</li><li>Learn to use Azure Machine Learning for data analysis and machine learning tasks.</li><li>Develop the skills needed to pursue a career in cybersecurity or web application penetration testing.</li></ul><p><strong>Please Note:</strong> Imperial Academy is One Education approved resale partner for Quality Licence Scheme Endorsed courses.</p><p>The Web Application Penetration Tester - QLS Endorsed Bundle includes a range of courses that cover different aspects of web application security and penetration testing. The Web Application Penetration Testing Course covers the principles and practices of web application penetration testing, as well as the tools and techniques used in this field. The Secure Programming of Web Applications course covers the principles of secure coding and how they can be applied in the development of web applications.</p><p>The Python Programming Bible course covers a range of topics related to Python programming, including networking, GUI, email, XML, and CGI. The Azure Machine Learning course covers the use of Azure Machine Learning for data analysis and machine learning tasks. The Introduction to Java course covers the basics of Java programming, including data types, variables, control structures, and methods. The C++ Development: The Complete Coding Guide course covers the basics of C++ programming, including data types, variables, and control structures.</p><p><strong>This bundle comprises the following courses:</strong></p><p><strong>QLS Endorsed Courses:</strong></p><ul><li><strong>Course 01:</strong> Web Application Penetration Testing Course</li><li><strong>Course 02:</strong> Secure Programming of Web Applications - Developers and TPMs</li><li><strong>Course 03:</strong> Python Programming Bible | Networking, GUI, Email, XML, CGI</li><li><strong>Course 04:</strong> Diploma in Python Programming</li><li><strong>Course 05:</strong> Azure Machine Learning</li><li><strong>Course 06:</strong> Introduction to Java</li><li><strong>Course 07:</strong> C++ Development: The Complete Coding Guide</li><li><strong>Course 08:</strong> How To Build Easy Drag &amp; Drop Landing Pages With WordPress</li><li><strong>Course 09:</strong> Career Coaching</li><li><strong>Course 10:</strong> The Unstoppable Self-Confidence &amp; High Self-Esteem Blueprint</li></ul><p><strong>Certificate: </strong></p><p>The Quality Licence Scheme ensures excellence and quality. These courses' learning outcomes provide the learners with high competence in their line of work as well. So, QLS-endorsed courses deliver not only deep knowledge but also add great value to the resume.</p><p>After completing the assessments, learners will be eligible for a prestigious QLS Certificate for each endorsed course in this bundle. Moreover, the certificates will be delivered to their home free of cost!</p><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <ul><li>Individuals interested in a career in cybersecurity or web application penetration testing.</li><li>IT professionals looking to enhance their skills in web application security and penetration testing.</li><li>Developers and programmers looking to learn about secure coding practices for web applications.</li><li>Data analysts and machine learning enthusiasts looking to apply their skills to web application security.</li><li>Anyone interested in learning about web application security and penetration testing.</li></ul>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Web Application Penetration Tester - Average Salary £45,000 per year</li><li>Cybersecurity Analyst - Average Salary £35,000 - £65,000 per year</li><li>Information Security Manager - Average Salary £60,000 - £90,000 per year</li><li>Web Application Developer - Average Salary £30,000 - £60,000 per year</li><li>Data Analyst - Average Salary £25,000 - £50,000 per year</li></ul>
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                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            Included
        </p>

                        </div>

10 QLS Endorsed Courses for Programming | 10 Endorsed Certificates Included | Life Time Access

Web Application Penetration Tester - QLS Endorsed Bundle

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

Data Engineering

Microsoft AZ-900

Microsoft AZ-303

Microsoft AZ-700

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

Machine Learning (ML)

Machine Learning for Apps Level 4

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

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

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

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

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

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

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

Python Machine Learning Crash Course for Beginners

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

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

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

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

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

Machine Learning 101 with Scikit-learn and StatsModels

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

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

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

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

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

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

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

Machine Learning for Absolute Beginners - Level 2

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

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<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>
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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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