Name: Advanced Chatbots with Deep Learning and Python
Price: 82.99 GBP
Availability: InStock

<div id="course-pills-1">Duration<p>2 Days</p><p>12 CPD hours</p>This course is intended for<p>This course is intended for software testers, architects, engineers, or other related roles, who wish to apply AI to software testing practices within their enterprise.<br />While there are no specific pre-requisites for this course, it would be helpful is the attendee has familiarity with basic scripting (Python preferred) and be comfortable with working from the command line (for courses that add the optional hands-on labs). Attendees without basic scripting skills can follow along with the hands-on labs or demos.</p>Overview<p>This course introduces AI and related technologies from a practical applied software testing perspective. Through engaging lecture and demonstrations presented by our expert facilitator, students will explore:<br />Exploring AI<br />Introduction to Machine Learning<br />Introduction to Deep Learning<br />Introduction to Data Science<br />Artificial Intelligence (AI) in Software Testing<br />Implementing AI in Test Automation<br />Innovative AI Test Automation Tools for the Future</p><p>Implementing AI in Software Testing / AI in Test Automation is an introductory-level course for attendees new to AI, Machine Learning or Deep Learning who wish to automate software testing tasks leveraging AI. The course explores the essentials of AI, ML and DL and how the integrate into IT business operations and initiatives. Then the course moves to specifics about the skills, techniques and tools used to apply AI to common software testing requirements.</p></div><div id="course-pills-2"><h4>Exploring AI</h4><ul><li>AI-Initiatives</li><li>The Priority: Excellence</li><li>AI- Intelligence Types</li><li>The Machine Learning Types</li><li>The Quality Learning Initiative</li><li>The Inception in Academics</li><li>AI - Importance &amp; Applications</li><li>The Re-visit</li><li>Learning Re-visited via AI</li><li>Teaching in the world of AI</li><li>Exploring AI for Self-Development</li><li>AI In Academics Beyond Academics</li></ul><h4>Introduction to Machine Learning</h4><ul><li>What is Machine Learning?</li><li>Why Machine Learning?</li><li>Examples - Algorithms behind Machine Learning</li></ul><h4>Introduction to Deep Learning</h4><ul><li>What is Deep Learning?</li><li>Why Deep Learning?</li><li>Example - Deep Learning Vs Machine Learning</li></ul><h4>Introduction to Data Science</h4><ul><li>What is Data Science?</li><li>Why Data Science?</li><li>Examples - Use Cases of Data Science</li></ul><h4>Artificial Intelligence (AI) in Software Testing</h4><ul><li>What is AI in Software Testing?</li><li>The Role of AI Testing</li><li>Why do we Need AI in Software Testing?</li><li>Pros and Cons of AI in Software Testing</li><li>Applications of AI in Software Testing</li><li>Is it time for Testers or QA Teams to worry about AI?</li><li>Automated Testing with Artificial Intelligence</li></ul><h4>Implementing AI in Test Automation</h4><ul><li>Training the AI Bots</li><li>Challenges with AI-powered Applications</li><li>Examples - Real World use cases using Artificial Intelligence</li><li>Demo - Facial Emotion Detection Using Artificial Intelligence</li><li>Demo - Text Analysis API Using Artificial Intelligence</li><li>Demo - EYE SPY Mobile App Using Artificial Intelligence</li></ul><h4>Innovative AI Test Automation Tools for the Future</h4><ul><li>Tools used for Implementing AI in Automation Testing</li><li>What is NEXT?</li><li>AI Test Automation Demo using Testim</li></ul></div>

Nexus Human

Action Learning Facilitator

API Testing

Software Testing & QA

Automation Testing

Microsoft AI-900

AI (Artificial Intelligence)

Implementing AI in Software Testing | AI in Test Automation (TTAI2140)

<p>Feeling Stuck in Your Career? The <strong>Mechanical Engineering with Mechatronics Engineering for Mechanic - 30 in 1 Courses</strong> Bundle is Your Skill-Building Solution. This exceptional collection of <strong>30 premium courses</strong> is designed to encourage growth and improve your career opportunities. Suited to meet different interests and goals, the Mechanical Engineering bundle provides an engaging learning experience, helping you learn skills across various disciplines.</p><p>With the <strong>Mechanical Engineering with Mechatronics Engineering for Mechanic</strong>Bundle, you'll have a personalised journey that aligns with your career goals and interests. This comprehensive package helps you confidently tackle new challenges, whether entering a new field or enhancing your existing knowledge. The Mechanical Engineering bundle is your gateway to expanding your career options, increasing job demand, and enhancing your skill set.</p><p>By enrolling in this bundle, you'll receive complimentary PDF certificates for all courses, adding value to your resume at no extra cost. Develop key skills and achieve important progress in your career and personal development. Start your journey today and experience the transformative impact of the Mechanical Engineering bundle on your job life and career growth!</p><p><strong>This Mechanical Engineering with Mechatronics Engineering for Mechanic</strong><strong>Bundle Comprises the Following CPD Accredited Courses:</strong></p><ul><li><strong>Course 01: </strong>Mechanical Engineering</li><li><strong>Course 02: </strong>Engineering Mechanics Course for Beginners</li><li><strong>Course 03: </strong>Crack Your Mechanical Engineer Interview</li><li><strong>Course 04: </strong>Automotive Engineering: Onboard Diagnostics</li><li><strong>Course 05: </strong>Automotive Design</li><li><strong>Course 06: </strong>Hybrid Vehicle Expert Training</li><li><strong>Course 07: </strong>Engine Lubrication Systems Online Course</li><li><strong>Course 08: </strong>Rotating Machines</li><li><strong>Course 09: </strong>Electric Circuits for Electrical Engineering</li><li><strong>Course 10: </strong>Electrical Machines for Electrical Engineering</li><li><strong>Course 11: </strong>Electronic &amp; Electrical Devices Maintenance &amp; Troubleshooting</li><li><strong>Course 12: </strong>Digital Electric Circuits &amp; Intelligent Electrical Devices</li><li><strong>Course 13: </strong>MATLAB Simulink for Electrical Power Engineering</li><li><strong>Course 14: </strong>Power Electronics for Electrical Engineering</li><li><strong>Course 15: </strong>Energy Engineer Course</li><li><strong>Course 16: </strong>Energy Saving in Electric Motors</li><li><strong>Course 17: </strong>Electric Vehicle Battery Management System</li><li><strong>Course 18: </strong>A complete course on Turbocharging</li><li><strong>Course 19: </strong>Heating Ventilation and AirConditioning (HVAC) Technician</li><li><strong>Course 20: </strong>Python Intermediate Training</li><li><strong>Course 21: </strong>Spatial Data Visualisation and Machine Learning in Python</li><li><strong>Course 22: </strong>Data Center Training Essentials: Mechanical &amp; Cooling</li><li><strong>Course 23: </strong>Career Development Plan Fundamentals</li><li><strong>Course 24: </strong>CV Writing and Job Searching</li><li><strong>Course 25: </strong>Learn to Level Up Your Leadership</li><li><strong>Course 26: </strong>Networking Skills for Personal Success</li><li><strong>Course 27: </strong>Ace Your Presentations: Public Speaking Masterclass</li><li><strong>Course 28:</strong> Learn to Make a Fresh Start in Your Life</li><li><strong>Course 29: </strong>Motivation - Motivating Yourself &amp; Others</li><li><strong>Course 30: </strong>Excel: Top 50 Microsoft Excel Formulas in 50 Minutes!</li></ul><p><strong>What will make you stand out?</strong></p><p>Upon completion of this online <strong>Mechanical Engineering with Mechatronics Engineering for Mechanic</strong>Bundle, you will gain the following:</p><ul><li>CPD QS Accredited Proficiency with this Mechanical Engineering bundle</li><li>After successfully completing the Mechanical Engineering bundle, you will receive a FREE PDF Certificate from REED as evidence of your newly acquired abilities.</li><li>Lifetime access to the whole collection of learning materials of this Mechanical Engineering bundle</li><li>The online test with immediate results</li><li>You can study and complete the Mechanical Engineering bundle at your own pace.</li><li>Study for the Mechanical Engineering bundle using any internet-connected device, such as a computer, tablet, or mobile device.</li></ul><p>The <strong>Mechanical Engineering with Mechatronics Engineering for Mechanic</strong>bundle is a premier learning resource, with each course module holding respected CPD accreditation, symbolising exceptional quality. The content is packed with knowledge and is regularly updated to ensure it remains relevant. This bundle offers not just education but a constantly improving learning experience designed to enrich both your personal and professional development.</p><p>Advance the future of learning with the <strong>Mechanical Engineering bundle</strong>, a comprehensive complete collection of 30 courses. Each course in the Mechanical Engineering bundle has been handpicked by our experts to provide a broad range of learning opportunities. Together, these modules form an important and well-rounded learning experience.</p><p>Our mission is to deliver high-quality, accessible education for everyone. Whether you are starting your career, switching industries, or enhancing your professional skills, the Mechanical Engineering bundle offers the flexibility and convenience to learn at your own pace. Make the Mechanical Engineering package your trusted partner in your lifelong learning journey.</p><h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p>The <strong>Mechanical Engineering with Mechatronics Engineering for Mechanic</strong>bundle is perfect for:</p><ul><li>Expand your knowledge and skillset for a fulfilling career with the Mechanical Engineering bundle.</li><li>Become a more valuable professional by earning CPD certification and mastering in-demand skills with the Mechanical Engineering bundle.</li><li>Discover your passion or explore new career options with the diverse learning opportunities in the Mechanical Engineering bundle.</li><li>Learn on your schedule, in the comfort of your home - the Mechanical Engineering bundle offers ultimate flexibility for busy individuals.</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>You are warmly invited to register for this bundle. Please be aware that no formal entry requirements or qualifications are necessary. This <strong>Mechanical Engineering with Mechatronics Engineering for Mechanic </strong>bundle curriculum has been crafted to be open to everyone, regardless of previous experience or educational attainment.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>Gain a wide range of skills across various fields, improve your problem-solving capabilities, and keep current with industry trends. Perfect for those aiming for career advancement, exploring a new professional direction, or pursuing personal growth. Begin your journey with the <strong>Mechanical Engineering with Mechatronics Engineering for Mechanic</strong>bundle.</p>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Certificates</h4>
        <p>
            Digital certificate -
            Included
        </p>

                        </div>

Compliance Central

Mechanical Engineering with Mechatronics Engineering for Mechanic - 30 in 1  Courses

General Admission

<p>Unlock your potential and achieve your goals with our comprehensive online Foundations of Artificial Intelligence  courses! Dive into a world of knowledge and skill enhancement right from the comfort of your own home. Whether you're looking to advance your career, explore a new hobby, or simply expand your horizons, our Foundations of Artificial Intelligence courses are designed to empower you every step of the way.</p><p><strong>Key Features</strong></p><p>The Foundations of Artificial Intelligence course includes the following offers: </p><ul><li>CPD Quality Standards Accredited Foundations of Artificial Intelligence Course</li><li>Lifetime Access</li><li>No Hidden Fees</li><li>24/7 Tutor Support </li><li>Certificate Available with reasonable price</li></ul><p>Discover a wealth of learning opportunities with our diverse range of online courses including Foundations of Artificial Intelligence. From professional development to personal enrichment, we offer courses in a variety of subjects including business, technology, arts, languages, and more. This Foundations of Artificial Intelligence course is carefully curated by industry experts and delivered through engaging multimedia content, interactive quizzes, and hands-on projects to ensure an immersive learning experience.</p><p><strong>Curriculum</strong></p><ul><li><strong>Module 01: Introduction To Artificial Intelligence</strong></li><li><strong>Module 02: Mathematics For AI</strong></li><li><strong>Module 3: Knowledge Representation In AI - Part 1</strong></li><li><strong>Module 4: Knowledge Representation In AI - Part 2</strong></li><li><strong>Module 5: Machine Learning - Part 1</strong></li><li><strong>Module 6: Machine Learning - Part 2</strong></li><li><strong>Module 7: Deep Learning</strong></li><li><strong>Module 8: Natural Language Processing</strong></li><li><strong>Module 9: Computer Vision</strong></li><li><strong>Module 10: Robotics</strong></li><li><strong>Module 11: Building AI Applications</strong></li></ul><p><strong>Why This Online Course is Better for You:</strong></p><p>Unlike traditional classroom settings, our online Foundations of Artificial Intelligence course offers flexibility and convenience tailored to fit your busy lifestyle. With 24/7 access to Foundations of Artificial Intelligence course materials and the ability to learn at your own pace, you can study whenever and wherever it's most convenient for you. Plus, our online platform provides a supportive community of fellow learners and dedicated instructors to guide you every step of the way.</p><h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p>Our Foundations of Artificial Intelligence course is designed for anyone eager to expand their knowledge and skills, regardless of their background or experience level. Whether you're a working professional looking to upskill, a student seeking supplemental education, or an enthusiast pursuing a passion project, our courses are accessible to learners of all ages and abilities.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>Invest in your future and unlock new career opportunities with our online Foundations of Artificial Intelligence course. Gain valuable skills and credentials that are in high demand in today's competitive job market. Whether you're looking to switch careers, climb the corporate ladder, or start your own business, This course provide the expertise and certification you need to succeed.</p>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4> Digital certificate</h4>
        <p>
            Digital certificate -
            £9
        </p>
        <h4> Hard copy certificate</h4>
        <p>
            Hard copy certificate -
            £15
        </p>
            <div><p>A Hardcopy Certificate (international): £15 + £10 (postal charge) = £25</p></div>

                        </div>

One Education

Foundations of Artificial Intelligence Course

<p><strong>R Programming for Data Science </strong></p><p>Tired of browsing and searching for a <strong>R Programming </strong>course you are looking for? Can't find the complete package that fulfils all your needs? Then don't worry as you have just found the solution. Take a minute and look through this extensive bundle that has everything you need to succeed.</p><p>After surveying thousands of learners just like you and considering their valuable feedback, this all-in<strong>-</strong>one R Programming bundle has been designed by industry experts. We prioritised what learners were looking for in a complete package and developed this in-demand R Programming course that will enhance your skills and prepare you for the competitive job market.</p><p>Also, ourexperts are available for answering your queries on R Programmingand help you along your learning journey. Advanced audio-visual learning modules of these R Programmingcourses are broken down into little chunks so that you can learn at your own pace without being overwhelmed by too much material at once.</p><p>Furthermore, to help you showcase your expertise in R Programming, we have prepared a special gift of 1 hardcopy certificate and 1 PDF certificate for the title course completely free of cost. These certificates will enhance your credibility and encourage possible employers to pick you over the rest.</p><p><strong>This R Programming Bundle Consists of the following Premium courses: </strong></p><ul><li><strong> Course 01:</strong> R Programming for Data Science<strong><br /></strong></li><li><strong> Course 02: </strong>Data Analytics with Tableau<strong><br /></strong></li><li><strong> Course 03: </strong>Introduction to Data Analysis<strong><br /></strong></li><li><strong> Course 04: </strong>Data Science with Python<strong><br /></strong></li><li><strong> Course 05: </strong>Data Science &amp; Machine Learning with Python<strong><br /></strong></li><li><strong> Course 06: </strong>Data Structures Complete Course<strong><br /></strong></li><li><strong> Course 07: </strong>Complete Google Analytics Course<strong><br /></strong></li><li><strong> Course 08: </strong>Data Analysis in Excel Level 3 Course<strong><br /></strong></li><li><strong> Course 09: </strong>Basic Google Data Studio<strong><br /></strong></li><li><strong> Course 10: </strong>Data Center Training Essentials: General Introduction<strong><br /></strong></li><li><strong> Course 11: </strong>SQL Programming Masterclass<strong><br /></strong></li><li><strong> Course 12: </strong>Data Analysis in Excel Level 3 Course<strong><br /></strong></li><li><strong> Course 13: </strong>Learn to Use Python for Spatial Analysis in ArcGIS<strong><br /></strong></li><li><strong> Course 14: </strong>GDPR Data Protection Level 5</li></ul><p>Enrol now in R Programming to advance your career, and use the premium study materials from Apex Learning.</p><p>The R Programming for Data Science bundle incorporates basic to advanced level skills to shed some light on your way and boost your career. Hence, you can strengthen your R Programmingexpertise and essential knowledge, which will assist you in reaching your goal.</p><p><strong>Certificate:</strong></p><ul><li><strong>PDF Certificate: Free (Previously it was £6*11 = £66)</strong></li><li><strong>Hard Copy Certificate: Free (For The Title Course: Previously it was £10)</strong></li></ul><h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p>Anyone from any background can enrol in this R Programming for Data Science bundle.</p>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>Our R Programming course is fully compatible with PCs, Macs, laptops, tablets and Smartphone devices.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>Having this R Programming expertise will increase the value of your CV and open you up to multiple job sectors.</p>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Digital certificate -
            Included
        </p>
            <div> <p>You will get the PDF Certificate for the title course <strong>(R Programming for Data Science)</strong> absolutely Free!</p> </div>
        <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div> <p>You will get the Hard Copy certificate for the title course <strong>(R Programming for Data Science</strong><strong>)</strong> absolutely Free! Other Hard Copy certificates are available for £10 each. </p><p><strong>Please Note:</strong> The delivery charge inside the UK is £3.99, and the international students must pay a £9.99 shipping cost.</p> </div>

                        </div>

Apex Learning

R Programming for Data Science

Packt

Data Science

This course for absolute beginners provides you with the opportunity to systematically learn core statistical and probability concepts, descriptive statistics, hypothesis testing, regression analysis, analysis of variance (ANOVA), and advanced regression/ML methods such as logistics regressions, polynomial regressions, decision trees, and more.

Statistics and Mathematics for Data Science and Data Analytics

<p>Are you interested in becoming a well-rounded IT professional? Our "IT: Programming, Web Development, Networking &amp; Security Complete Training" bundle is the perfect choice for you!</p><p>This bundle package consists of 30 premium courses, carefully curated to provide you with a comprehensive understanding of various IT domains. From web development to networking and security, this bundle has got you covered.</p><p>Get experience with essential IT skills, including Python, SQL, JavaScript, C#, and more. Our courses are designed to equip you with the knowledge and skills required to succeed in your IT career.</p><p>You will also learn about data protection, ethical hacking, and cybersecurity through our specialized courses. Our bundle includes a course on GDPR, a crucial compliance regulation, and ethical hacking with Kali Linux.</p><p>In addition to technical skills, we also offer career-focused courses, including career development plan fundamentals, CV writing, job searching, and interview skills. You will also learn how to create a professional LinkedIn profile to boost your job search.</p><p>Invest in your career and enroll in our "IT: Programming, Web Development, Networking &amp; Security Complete Training" bundle. With our expert-led courses and career-focused modules, you will gain a competitive edge in the IT industry. Start your journey today!</p><p><strong>This </strong><strong>All About IT </strong><strong>Bundle consists of the following premium courses: </strong></p><ul><li>Course 01: IT Support Technician</li><li>Course 02: Network Security and Risk Management</li><li>Course 03: CompTIA Network</li><li>Course 04: Information Management</li><li>Course 05: Web Application Penetration Testing Course</li><li>Course 06: CompTIA Cloud+ (CV0-002)</li><li>Course 07: PLC Programming Diploma</li><li>Course 08: Coding with Scratch</li><li>Course 09: Create Your First Responsive Website</li><li>Course 10: Python Course</li><li>Course 11: Diploma in Front-End Web Development (HTML, CSS, &amp; Bootstrap)</li><li>Course 12: SQL For Data Analytics &amp; Database Development</li><li>Course 13: C# (C-Sharp)</li><li>Course 14: Computer Operating System and Troubleshooting</li><li>Course 15: MS Word Essentials - The Complete Word Course - Level 3</li><li>Course 16: Building Your Own Computer Course</li><li>Course 17: ASP.Net MVC and Entity Framework Course</li><li>Course 18: It: Ethical Hacking, IT Security and IT Cyber Attacking</li><li>Course 19: Learn Ethical Hacking with Kali Linux</li><li>Course 20: SQL Database Administrator</li><li>Course 21: JavaScript</li><li>Course 22: Web Design (HTML, CSS, Bootstrap) Complete Course</li><li>Course 23: Data Science and Visualisation with Machine Learning</li><li>Course 24: Cisco ASA Firewall Training Course</li><li>Course 25: Data Protection (GDPR) Practitioner</li></ul><p><strong>Moreover, this bundles include 5 career-focused courses:</strong></p><ul><li>Course 01: Career Development Plan Fundamentals</li><li>Course 02: CV Writing and Job Searching</li><li>Course 03: Interview Skills: Ace the Interview</li><li>Course 04: Video Job Interview for Job Seekers</li><li>Course 05: Create a Professional LinkedIn Profile</li></ul><p>Our cutting-edge learning package offers top-notch digital aid and first-rate tutor support. You will acquire the crucial hard and soft skills needed for career advancement because this bundle has been thoroughly examined and is career-friendly. So don't overthink! Enrol today.</p><p><strong>Learning Outcomes</strong></p><p>This unique All About IT mega bundle will help you to-</p><ul><li>Quench your thirst for knowledge</li><li>Be up-to-date about the latest advancements</li><li>Achieve your dream career goal in this sector</li><li>Know the applicable rules and regulations needed for a professional in this area</li><li>Acquire some valuable knowledge related to All About IT to uplift your morale</li></ul><p>The bundle incorporates basic to advanced level skills to shed some light on your way and boost your career. Hence, you can strengthen your expertise and essential knowledge, which will assist you in reaching your goal. Moreover, you can learn from any place in your own time without travelling for classes.</p><p><strong>Certificate:</strong></p><ul><li><strong>PDF Certificate: Free for all </strong><strong>30</strong><strong> courses</strong></li><li><strong>Hard Copy Certificate: Free (For The Title Course: Previously it was £10)</strong></li></ul><h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p>The All About IT bundle is designed to assist anyone with a curious mind, anyone looking to boost their CVs or individuals looking to upgrade their career to the next level can also benefit from the learning materials.</p>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>The courses in this bundle has been designed to be fully compatible with tablets and smartphones.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>This All About IT bundle will give you an edge over other competitors and will open the doors for you to a plethora of career opportunities.</p>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div> <p>You will get the Hard Copy certificate for the title course (<strong>IT Support Technician</strong>) absolutely Free! Other Hard Copy certificates are available for £10 each. </p><p><strong>Please Note:</strong> The delivery charge inside the UK is £3.99, and the international students must pay a £9.99 shipping cost.</p> </div>

                        </div>

IT: Programming, Web Development, Networking & Security Complete Training

Loughborough

<div>
		

		<div>
			
			<p>Data Ethics for Business Professionals is designed for individuals who are seeking to demonstrate an understanding of the ethical uses of data in business settings.</p>
			<div><i></i> 6 hours or 3 x 90 min sessions</div>
			<div><i></i>  £45.00 incl. VAT</div>
			
			
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	</div>

	

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    <div>

		
		 
		

		<div></div>

		<div>
			<p>Want to run this event in-house?  Enquire about running this event in-house </p>
			
		</div>

		<div>
			<p>Interested in attending? Have a suggestion about running this event near you?<br />Register your interest now</p>
			
		</div>
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        <div>
			
		</div>
				

	
		<h3>Description</h3>
		<p></p><p><span>This course empowers professionals to identify sources of ethical risk and move beyond transparency to understand all ethical principles while balancing business considerations of ethically driven technology.</span></p>
<p></p>
	
		<h3>Modules/Lessons</h3>
		<p></p><h4>Overview</h4>
<p>The power of extracting value from data utilizing Artificial Intelligence, Data Science and Machine Learning exposes the learning differences between humans and machines. Humans can apply ethical principles throughout the decision-making process to avoid discrimination, societal harm, and marginalization to maintain and even enhance acceptable norms. Machines make decisions autonomously. So how do we apply ethical principles to data driven technology? This course provides business professionals and consumers of technology core concepts of ethical principles, how they can be applied to emerging data driven technologies and the impact to an organization which ignores ethical use of technology.</p>
<h4>Course Length:</h4>
<p>6 hours instructional time Content is designed to be presented as a full day class, or in three 90-minute sessions by lesson.</p>
<h4>Course Content:</h4>
<p>Each lesson includes activities, indicated in orange and may include resources for learners to integrate ethical practices into their own organization, indicated in blue.</p>
<h4>Introduction to Data Ethics</h4>
<p>Define Ethics <br />Define Data <br />Define Data Ethics <br />Principles of Data Ethics <br />The Case for Data Ethics<br />Identifying Ethical Issues</p>
<h4>Ethical Principles</h4>
<p>Ethical Frameworks <br />Applying Ethical Frameworks <br />Privacy, Fairness, and Safety <br />Applying Privacy, Fairness, and Safety Principles <br />Algorithms and Human-Centered Values <br />Discussing True and False Positives and Negatives <br />Discussing Accuracy and Precision <br />Discussing Correlation and Causation <br />Transparency and Explainability: The Black Box Problem <br />Discussing Black Box Parallels <br />Inclusive Growth, Sustainable Development, and Well-Being <br />Examining a Tech for Good Organization <br />Improving Ethical Data Practices</p>
<h4>Sources of Ethical Risk</h4>
<p>Bias and Discrimination <br />Case Study: Allegheny Family Screening Tool <br />Data Surveillance <br />Safety and Security <br />Case Study: PredPol</p>
<h4>Business Considerations</h4>
<p>Data Legislation <br />Manage the Effects of Data <br />Case Study <br />Embed Organizational Values in the Data Value Chain <br />Building a Data Ethics Culture/Code of Ethics</p>
<h4>Exam</h4>
<p><span>A Data Ethics Business Professional is an individual who has demonstrated foundational knowledge of the ethical use of data in emerging technologies in a business. A credential holder is able to identify, explain, and document how organizations identify ethical principles and considerations, understand sources of risk, and explain how businesses can create sustainable, ethical decision-making processes.</span></p>
<p><span>Exam Details: 40 Mins, Multiple Choice Online Assessment. </span></p>
<p></p>
	
		<h3>Target Student</h3>
		<p></p><p>This course is designed for business leaders and decision makers, including C-level executives, project and product managers, HR leaders, Marketing and Sales leaders, and technical sales consultants, who have a vested interest in the representation of ethical values in technology solutions. Other individuals who want to know more about data ethics are also candidates for this course.</p>
<p></p>
	
		<h3>Prerequisites</h3>
		<p></p><p>To ensure your success in this course, you should have a working knowledge of general business concepts and practices. You should also have a basic understanding of Artificial Intelligence and or Data Science.</p>
<p></p>

Mpi Learning - Professional Learning And Development Provider

Data Ethics for Business Professionals is designed for individuals who are seeking to demonstrate an understanding of the ethical uses of data in business settings.

Data Ethics for Business Professionals

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			<h4><b>Do you want to acquire the knowledge that will Help you Understand &amp; Cope with your Asperger's Child? </b></h4>
<p><span>Asperger's syndrome is a developmental disorder that affects a child's ability to socialise and communicate effectively with others. Children with Asperger's syndrome typically present social awkwardness and an all-absorbing interest in specific topics. </span><span>This course is designed for people who are caregivers for autistic individuals (Parents and Educators). This course aims to help you comprehend the world of</span><span> Asperger's syndrome</span><span>, spot the signs and symptoms of the syndrome spectrum so that you can help someone living with such a condition to live a richer life.</span></p>
<h3><b>Course Highlights</b></h3>
<h1>Asperger Syndrome Awareness</h1>
<p><span> is an award winning and the best selling</span><span> course that has been given the CPD Certification &amp; IAO accreditation. It is the most suitable course anyone looking to work in this or relevant sector.</span></p>
<p><span>It is considered one of the perfect courses in the UK that can help students/learners to get familiar with the topic and gain necessary skills to perform well in this field. We have packed Asperger Syndrome Awareness into </span><span><b>6</b> </span><span>modules for teaching you everything you need to become successful in this profession.</span></p>
<p><span>To provide you ease of access, this course is designed for both part-time and full-time students. You can become accredited in just </span><b>3 hours, 17 minutes</b><span> and it is also possible to study at your own pace.</span></p>
<p><span>We have experienced tutors who will help you throughout the comprehensive syllabus of this course and answer all your queries through email.</span></p>
<p><span>For further clarification, you will be able to recognize your qualification by checking the validity from our dedicated website.</span></p>
<h3><b>Why You Should Choose</b><b> Asperger Syndrome Awareness</b></h3>
<ul>
<li><span>Lifetime access to the course</span></li>
<li><span>No hidden fees or exam charges</span></li>
<li><span>CPD Accredited certification on successful completion</span></li>
<li><span>Full Tutor support on weekdays (Monday - Friday)</span></li>
<li><span>Efficient exam system, assessment and instant results</span></li>
<li><span>Download Printable PDF certificate immediately after completion</span></li>
<li><span>Obtain the original print copy of your certificate, dispatch the next working day for as little as £9.</span></li>
<li><span>Improve your chance of gaining professional skills and better earning potential.</span></li>
</ul>
<h3><b>Who is this Course for?</b></h3>
<p><b>Asperger Syndrome Awareness </b><span>is CPD certified and IAO accredited. This makes it perfect for anyone trying to learn potential professional skills.</span></p>
<p><span>As there is no experience and qualification required for this course, it is available for all students from any academic backgrounds.</span></p>
<h3><b>Requirements</b></h3>
<p><span>Our</span><b> Asperger Syndrome Awareness</b><span> is fully compatible with any kind of device. Whether you are using Windows computer, Mac, smartphones or tablets, you will get the same experience while learning. Besides that, you will be able to access the course with any kind of internet connection from anywhere at any time without any kind of limitation.</span></p>
<h3><b>Career Path</b></h3>
<p><span>You will be ready to enter the relevant job market after completing this course. You will be able to gain necessary knowledge and skills required to succeed in this sector. All our Diplomas' are CPD and IAO accredited so you will be able to stand out in the crowd by adding our qualifications to your CV and Resume. </span></p><table>				<tbody><tr>
					<td colspan="4">Foundations of Artificial Intelligence</td>
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					<td>Module 01: Introduction to Artificial Intelligence</td>
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					<td>Module 06: Machine Learning - Part 2</td>
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					<td><span><i></i> 00:15:00</span></td>
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					<td>Module 07: Deep Learning</td>
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					<td>Module 10: Robotics</td>
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					<td>Module 11: Building AI Applications</td>
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iStudy UK

Online Course Marketing

Online Course Creation

Autism Spectrum Counselling & Training (ASPECT)

Trainers Course

Assessor Training

Foundations of Artificial Intelligence

<p><strong> 24-Hour Knowledge Knockdown! Prices Reduced Like Never Before </strong></p><p>C++ is a versatile language essential for high-performance applications and game development. In the UK, the demand for skilled C++ programmers is projected to grow by<strong> 12% by 2027. </strong>This <strong>Advanced C++ Programming</strong> bundle equips you to tap into that demand, with a carefully curated selection of courses designed to take you from absolute beginner to confident C++ developer.</p><p>With this Advanced C++ Programming course you will get <strong>20 CPD Accredited PDF Certificates, Hard Copy Certificate</strong> of C++ Programming for Absolute Beginners and our exclusive <strong>student ID card</strong> absolutely free.</p><p><strong>Courses Are Included In This Advanced C++ Programming Bundle: </strong></p><ul><li><strong>Course 01: </strong>C++ Programming for Absolute Beginners</li><li><strong>Course 02: </strong>C++ Development: The Complete Coding Guide</li><li><strong>Course 03: </strong>Game Development using Cocos2d-x v3 C++</li><li><strong>Course 04: </strong>Tic-Tac-Toe Clone - The Complete SFML C++ Game Course</li><li><strong>Course 05: </strong>Flappy Bird Clone: SFML C++ Game</li><li><strong>Course 06: </strong>C# Basics</li><li><strong>Course 07: </strong>C# Programming - Beginner to Advanced</li><li><strong>Course 08: </strong>Mastering SQL Programming</li><li><strong>Course 09: </strong>Python Programming Bible</li><li><strong>Course 10: </strong>Three.js &amp; WebGL 3D Programming</li><li><strong>Course 11: </strong>Basics of WordPress: Create Unlimited Websites</li><li><strong>Course 12: </strong>MySql Masterclass</li><li><strong>Course 13: </strong>Stripe with C#</li><li><strong>Course 14: </strong>Coding Essentials - Javascript, ASP. Net, C# - Bonus HTML</li><li><strong>Course 15: </strong>Ultimate PHP &amp; MySQL Web Development &amp; OOP Coding</li><li><strong>Course 16: </strong>HTML Web Development Crash Course</li><li><strong>Course 17: </strong>CSS Web Development</li><li><strong>Course 18: </strong>Microsoft SQL Server Development for Everyone</li><li><strong>Course 19: </strong>Machine Learning with Python Course</li><li><strong>Course 20: </strong>Unreal Engine 4 for Beginners</li></ul><p>This comprehensive Advanced C++ Programming bundle empowers you to journey from C++ novice to game development pro. Learn from industry experts, build practical projects, and unlock advanced concepts. Master essential programming skills in C++, SFML, Cocos2d-x v3, SQL, Python, and 3D graphics, opening doors to exciting career opportunities.</p><p>Don't miss this chance to elevate your career and skillset. Enrol in Advanced C++ Programming today and take the first step towards achieving your goals and dreams.</p><p><strong>Why buy this Advanced C++ Programming?</strong></p><ul><li>Get a Free CPD Accredited Certificate upon completion of the course</li><li>Get a free student ID card with this training program</li><li>The course is affordable and simple to understand</li><li>This course is entirely online, interactive lesson with voiceover audio</li><li>Lifetime access to the course materials</li><li>The training program comes with 24/7 tutor support</li></ul> <p><strong>Start your learning journey straightaway with our Advanced C++ Programming Training!</strong></p><p>Advanced C++ Programming premium bundle consists of 20 precisely chosen courses on a wide range of topics essential for anyone looking to excel in this field. Each segment of Advanced C++ Programming is meticulously designed to maximise learning and engagement, blending interactive content and audio-visual modules for a truly immersive experience.</p><p><strong>Certification of Advanced C++ Programming<br /></strong></p><p>You have to complete the assignment given at the end of the Advanced C++ Programming course and score a minimum of 60% to pass each exam. Our expert trainers will assess your assignment and give you feedback after you submit the assignment. After passing the Advanced C++ Programming exam You will be entitled to claim a PDF &amp; Hardcopy certificate accredited by CPD Quality standards completely free.</p> <h2>
        
        <div>CPD</div>
    </h2>

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                                    <div>
                                        200 CPD hours / points
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                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div> <h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>This bundle is ideal for:</p><ul><li>Students seeking mastery in this field</li><li>Professionals seeking to enhance their skills</li><li>Anyone who is passionate about this topic</li></ul>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>This Advanced C++ Programming doesn't require prior experience and is suitable for diverse learners.</p>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>This Advanced C++ Programming bundle will allow you to kickstart or take your career in the related sector to the next stage.</p>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited  Digital certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>CPD Accredited  Hard copy certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div><p>If you are an <strong>international student</strong>, then you have to pay an additional <strong>10 GBP</strong> for each certificate as an international delivery charge.</p></div>

                        </div>

Academy for Health and Fitness

Certificate in Teaching Program (CTP)

Computer Programming

Certificate in Advanced English (CAE)

Continuous Professional Development (CPD)

ICDL (International Certification of Digital Literacy)

Advanced C++ Programming

<strong>This comprehensive course will help you learn the basics to advanced mechanisms of chatbot development using deep learning with Python. This course is a complete package for beginners to learn chatbot fundamentals with deep learning and its applications and build it from scratch using deep learning (RNNs) with Python.</strong>
        <p>AI-powered chatbots are also capable of automating various tasks, including sales and marketing, customer service, and administrative and operational tasks. In this course about developing advanced chatbots with deep learning, we will understand their applications and build from scratch using deep learning with Python.

The course begins with a brief overview and the fundamentals of deep learning for chatbots. We will understand and compare conventional chatbots with deep learning-based chatbots. Then, we will explore self-learning chatbots, including generative chatbots and retrieval chatbots. You will learn more about deep learning-empowered chatbot features and compare and distinguish the abilities of conventional chatbots and self-learning chatbots in real action. We will focus on chatbot development with deep learning, tokenization, setting up an Encoder-Decoder, implementing RNN-based model development, and finally, training, testing, and validating the chatbot we developed.

Upon completing this course successfully, you will relate concepts and understand theories of chatbots in various domains, understand and implement deep learning models for building real-world chatbots, and evaluate deep learning-based chatbot models.

All resources are available at: https://github.com/PacktPublishing/Advanced-Chatbots-with-Deep-Learning-Python</p>
        <h2>What You Will Learn</h2><p>Relate the concepts and theories for chatbots in various domains<br>Compare conventional chatbots with deep learning-based chatbots<br>Understand deep learning algorithms for chatbots<br>Implement deep learning models for building real-world chatbots<br>Learn about tokenization and setting up an encoder-decoder<br>Implement recurrent neural network-based model development</p>
        <h2>Audience</h2><p>This course is designed for individuals looking to advance their skills in applied deep learning, acquire knowledge regarding the relationships of data analysis with deep learning, wish to build customized chatbots for their applications, learn to implement deep learning algorithms for chatbots, and are passionate about rule-based and self-learning chatbots. Deep learning practitioners/scholars working on chatbot concepts would benefit from this course. No prior knowledge of chatbots, deep learning, data analysis, or mathematics is needed. Basic to intermediate Python knowledge is required.</p>
        <h2>Approach</h2><p>The course is crafted to help you understand the impact of chatbots in real-world applications and provides a unique hands-on experience using a project. This course is easily understandable, with engaging content covering necessary theoretical concepts, a practical approach with live coding, and an in-depth project covering the complete course content.</p>
        <h2>Key Features</h2><p>Learn basic to advanced mechanisms of developing chatbots using deep learning with Python * Use Python to evaluate datasets based on conversational notes, online resources, and websites * Explore conventional/self-learning chatbots in action and chatbot development with deep learning</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Advanced-Chatbots-with-Deep-Learning-Python </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</h4><p>This section focuses on introducing the author and the AI Sciences group and describes the course content you will be learning.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Course and Instructor Introduction</p><p style='margin: 0'>In this video, we will be introduced to the course's author and his qualifications and experience in teaching learners on the online platform.</p></td></tr><tr><td style='padding: 15px;'><p>2. AI Sciences Introduction</p><p style='margin: 0'>This video briefly introduces the AI Sciences group and discusses the benefits of learning through AI Sciences.</p></td></tr><tr><td style='padding: 15px;'><p>3. Course Description</p><p style='margin: 0'>In this lesson, we will get an overview of what is covered in the course, including a comparison of chatbots and the benefits of chatbots. We will look at the chatbots in action. We will also look at deep learning frameworks in chatbots.</p></td></tr></tbody></table><br><h4>2. Fundamentals of Chatbots for Deep Learning</h4><p>This section focuses on the basics of chatbots, how deep learning benefits chatbots, conventional versus AI chatbots, the use of chatbots in the medical field, and the use of chatbots in business and ecommerce.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Module Introduction</p><p style='margin: 0'>This introduction video elaborates on the concepts to be covered in this module, including comparing chatbots, the conventional versus AI chatbots, and use of chatbots in various fields of applications.</p></td></tr><tr><td style='padding: 15px;'><p>2. Conventional Versus AI Chatbots</p><p style='margin: 0'>In this video, we will understand conventional and artificial intelligence-based chatbots and understand the differences between the two kinds.</p></td></tr><tr><td style='padding: 15px;'><p>3. Generative Versus Retrieval Chatbots</p><p style='margin: 0'>In this lecture, you will learn about a generative and retrieval chatbot and compare the differences between the two chatbots.</p></td></tr><tr><td style='padding: 15px;'><p>4. Benefits of Deep Learning Chatbots</p><p style='margin: 0'>In this video, we will look at what deep learning chatbots are and how they work. We will also understand the benefits of deep learning chatbots, including improved customer experience, service integration, customer care, personalized services, and saving resources.</p></td></tr><tr><td style='padding: 15px;'><p>5. Chatbots in Medical Domain</p><p style='margin: 0'>We will explore the first kind of chatbot operating in the medical domain. We will look at the benefits, including the ability to schedule appointments, check patient symptoms, provide support, help with coverage and insurance claims, and improve the patient experience.</p></td></tr><tr><td style='padding: 15px;'><p>6. Chatbots in Business</p><p style='margin: 0'>In this video, we will understand the benefits of chatbots in businesses, including lead qualification, lead nurturing, and data mining techniques.</p></td></tr><tr><td style='padding: 15px;'><p>7. Chatbots in Ecommerce</p><p style='margin: 0'>Let's understand the benefits of chatbots in ecommerce, including personalized services, real-time interaction, collection of feedback, provide metrics, lead generation, deep analytics, and storytelling.</p></td></tr></tbody></table><br><h4>3. Deep Learning-Based Chatbot Architecture and Development</h4><p>This section focuses on chatbot architecture and development based on deep learning, including understanding deep learning architecture, encoders and decoders, data preparation, importing libraries, and making predictions.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Module Introduction</p><p style='margin: 0'>In this video, you will learn more about deep learning and the general architecture, chatbot development with deep learning, and its related concepts.</p></td></tr><tr><td style='padding: 15px;'><p>2. Deep Learning Architecture</p><p style='margin: 0'>This video elaborates on the deep learning architecture in chatbots, integration of deep learning with chatbots, natural language processing (NLP), source content, interaction history, and analytics.</p></td></tr><tr><td style='padding: 15px;'><p>3. Encoder Decoder</p><p style='margin: 0'>In this video, we will understand what encoder and decoder are, their architecture, and long short-term memory (LSTM), a neural network used in deep learning.</p></td></tr><tr><td style='padding: 15px;'><p>4. Steps Involved</p><p style='margin: 0'>In this lesson, you will learn about the various steps involved in developing chatbots based on deep learning techniques. We will install the required packages, define chat models and tokenization, and set up the encoder-decoder model.</p></td></tr><tr><td style='padding: 15px;'><p>5. Project Overview and Packages</p><p style='margin: 0'>After theoretically learning about chatbots, we will now develop a chatbot practically by importing the requisite libraries-TensorFlow NumPy, Keras, and Pickle. We will build a chatbot based on a story and make predictions of true or false.</p></td></tr><tr><td style='padding: 15px;'><p>6. Importing Libraries</p><p style='margin: 0'>This video demonstrates how to import step by step the four main libraries required for our project, including TensorFlow, NumPy, Keras, and Pickle, and you will learn about importing Sequential and Model.</p></td></tr><tr><td style='padding: 15px;'><p>7. Data Preparation</p><p style='margin: 0'>We will use the two datasets available for our project, the training QA and the testing QA. We will save them in our project folder and invoke them as needed.</p></td></tr><tr><td style='padding: 15px;'><p>8. Develop Vocabulary</p><p style='margin: 0'>In this video, you will learn to combine the datasets, test data, and train data; we will use the all_data function to combine the datasets.</p></td></tr><tr><td style='padding: 15px;'><p>9. Max Story and Question Length</p><p style='margin: 0'>In this video, you will learn to define the project's story length and question length, determined by the number of words in the story or question.</p></td></tr><tr><td style='padding: 15px;'><p>10. Tokenizer</p><p style='margin: 0'>You will learn to import the tokenizer with and without filters. We will fit the tokenizer into our vocabulary using the fit_on_texts built-in command.</p></td></tr><tr><td style='padding: 15px;'><p>11. Separation and Sequence</p><p style='margin: 0'>In this video, you will learn to make the train-test story questions and answers. We will then use a for loop to enter the story, question, and answer into the respective parts.</p></td></tr><tr><td style='padding: 15px;'><p>12. Vectorize Stories</p><p style='margin: 0'>In this video, we will understand how to vectorize the story by defining functions, using the data, and developing an index and tokenizer. We will also determine the maximum story length calculated.</p></td></tr><tr><td style='padding: 15px;'><p>13. Vectorizing Train and Test Data</p><p style='margin: 0'>After creating the vectorization function, we will input the stories, queries, and answers using the vectorize function that we created using the train data.</p></td></tr><tr><td style='padding: 15px;'><p>14. Encoding</p><p style='margin: 0'>We will begin to develop our deep learning model and input placeholders to store the maximum story length, question length, and define the vocabulary size. We will now build our encoder using the sequential model.</p></td></tr><tr><td style='padding: 15px;'><p>15. Answer and Response</p><p style='margin: 0'>After learning to create our encoders for the input sequences and the questions, the encoders will match the data and obtain the responses.</p></td></tr><tr><td style='padding: 15px;'><p>16. Model Completion</p><p style='margin: 0'>In this video, after training and testing the data, querying the questions, and obtaining responses, we are now at model completion with compiling the questions and responses to check for accuracy.</p></td></tr><tr><td style='padding: 15px;'><p>17. Predictions</p><p style='margin: 0'>After checking our model for accuracy, we will make predictions of our results from the model we created. We will visualize the predictions using the test data.</p></td></tr></tbody></table>

Web Development

Python

This comprehensive course will help you learn the basics to advanced mechanisms of chatbot development using deep learning with Python. This course is a complete package for beginners to learn chatbot fundamentals with deep learning and its applications and build it from scratch using deep learning (RNNs) with Python.

Advanced Chatbots with Deep Learning and Python

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

Data Engineering

Microsoft AZ-900

Microsoft Azure

Microsoft AZ-303

Microsoft AZ-700

Azure Machine Learning

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

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

Python Machine Learning Bootcamp

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

Machine Learning with Real World Projects

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

Machine Learning (ML)

Machine Learning for Apps Level 4

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

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

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

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

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

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

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

Python Machine Learning Crash Course for Beginners

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

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

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

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

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

Machine Learning 101 with Scikit-learn and StatsModels

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

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

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

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

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

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

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

Machine Learning for Absolute Beginners - Level 2

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

TensorFlow

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

The Complete Machine Learning Course with Python

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

Learning To Learn

Machine Learning - Use in Flutter

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

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

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

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

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

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        <h2>Course Outline</h2><h4>1. Getting Started with Level 1!</h4><p></p><br><h4>2. The Rise of Artificial Intelligence</h4><p></p><br><h4>3. Introduction to Machine Learning</h4><p></p><br><h4>4. Classification of ML Systems</h4><p></p><br><h4>5. Course Summary</h4><p></p>

Deep Learning

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

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