159 Binary courses

OVERVIEW This comprehensive course on Data Structures Complete Course will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Data Structures Complete Course comes with accredited certification, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. HOW WILL I GET MY CERTIFICATE? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. WHO IS THIS COURSE FOR? There is no experience or previous qualifications required for enrolment on this Data Structures Complete Course. It is available to all students, of all academic backgrounds. REQUIREMENTS Our Data Structures Complete Course is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. CAREER PATH Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business & Management, Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc. COURSE CURRICULUM 14 sections • 55 lectures • 09:02:00 total length •Module 01: Promo Video: 00:02:00 •Module 02: Data Structure Introduction: 00:05:00 •Module 03: Computational Complexity Analysis: 00:13:00 •Module 01: Static and Dynamic Arrays: 00:12:00 •Module 02: Dynamic Arrays Source Code: 00:07:00 •Module 01: Singly and Doubly Linked Lists: 00:15:00 •Module 02: Doubly Linked Lists Source Code: 00:10:00 •Module 01: Stack: 00:12:00 •Module 02: Stack Implementation: 00:04:00 •Module 03: Stack Source Code: 00:04:00 •Module 01: Queues (Part-1): 00:06:00 •Module 02: Queues (Part-2): 00:06:00 •Module 03: Queue Source Code: 00:04:00 •Module 01: Priority Queues (PQs) with an interlude on heaps: 00:13:00 •Module 02: Turning Min PQ into Max PQ: 00:06:00 •Module 03: Adding Elements to Binary Heap: 00:10:00 •Module 04: Removing Elements from Binary Heap: 00:14:00 •Module 05: Priority Queue Binary Heap Source Code: 00:16:00 •Module 01: Disjoint Set: 00:06:00 •Module 02: Kruskal's Algorithm: 00:06:00 •Module 03: Union and Find Operations: 00:11:00 •Module 04: Path Compression Union Find: 00:07:00 •Module 05: Union Find Source Code: 00:08:00 •Module 01: Binary Trees and Binary Search Trees (BST): 00:13:00 •Module 02: Inserting Element into a Binary Search Tree (BST): 00:06:00 •Module 03: Removing Element from a Binary Search Tree (BST): 00:14:00 •Module 04: Tree Traversals: 00:12:00 •Module 05: Binary Search Source Code: 00:13:00 •Module 01: Fenwick Tree Construction: 00:06:00 •Module 02: Point Updates: 00:06:00 •Module 03: Binary Indexed Tree: 00:14:00 •Module 04: Fenwick Tree Source Code: 00:06:00 •Module 01: Hash Table: 00:17:00 •Module 02: Separate Chaining: 00:08:00 •Module 03: Separate Chaining Source Code: 00:12:00 •Module 04: Open Addressing: 00:11:00 •Module 05: Linear Probing: 00:14:00 •Module 06: Quadratic Probing: 00:09:00 •Module 07: Double Hashing: 00:15:00 •Module 08: Removing Element Open Addressing: 00:08:00 •Module 09: Open Addressing Code: 00:15:00 •Module 01: Introduction: 00:03:00 •Module 02: The Longest Common Prefix (LCP) Array: 00:03:00 •Module 03: Using SA/LCP Array to Find Unique Substrings: 00:05:00 •Module 04: Longest Common Substring (LCS): 00:11:00 •Module 05: Longest Common Substring (LCS) Full Example: 00:07:00 •Module 06: Longest Repeated Substring (LRS): 00:05:00 •Module 01: Balanced Binary Search Trees (BBSTs): 00:09:00 •Module 02: Inserting Elements into an AVL Tree: 00:10:00 •Module 03: Removing an AVL Tree: 00:09:00 •Module 04: AVL Tree Source Code: 00:17:00 •Module 01: Indexed Priority Queue (Part-1): 00:25:00 •Module 02: Indexed Priority Queue Source Code: 00:09:00 •Module 01: Sparse Table: 00:26:00 •Module 02: Sparse Table Source Code: 00:07:00

COURSE OVERVIEW Do you know, effective use of data structure can increase the efficiency of your software design process? To create efficient algorithms and continue a smooth software design process Data Structure is one of the most fundamental ingredients. Learn the basics of data structure and how you can use them from this Easy to Advanced Data Structures Masterclass course and create incredible software designs using that knowledge. This Easy to Advanced Data Structures Masterclass course will help you to strengthen your basics, clear misunderstandings and get hold of the functions of data structure and how you can use it. The animated video lessons will help you understand data Structure easily. You will learn about Static and dynamic arrays, linked lists, stacks, queues, search trees, hash tables, sparse tables and many other functions that will help you understand how you can use data structure and create efficient software designs. LEARNING OUTCOMES * Understand the basics of data structure  * Familiarize with the algorithms associated with data structure  * Be able to include linked lists, dynamic arrays, queues and stacks in your data structure project  * Learn what Static and dynamic arrays are  * Be able to Union or disjoint sets in your data table  * Get a clear understanding of hash tables and how they work WHO IS THIS COURSE FOR? This course is ideal for anyone who wants to learn about data structure or strengthen their basics. It is especially helpful for those who work in the IT industry and deal with database management. ENTRY REQUIREMENT * This course is available to all learners, of all academic backgrounds. * Learners should be aged 16 or over to undertake the qualification. * Good understanding of English language, numeracy and ICT are required to attend this course. CERTIFICATION * After you have successfully completed the course, you will be able to obtain an Accredited Certificate of Achievement. You can however also obtain a Course Completion Certificate following the course completion without sitting for the test. Certificates can be obtained either in hardcopy at the cost of £39 or in PDF format at the cost of £24. * PDF certificate's turnaround time is 24 hours, and for the hardcopy certificate, it is 3-9 working days. WHY CHOOSE US? * Affordable, engaging & high-quality e-learning study materials; * Tutorial videos/materials from the industry-leading experts; * Study in a user-friendly, advanced online learning platform; * Efficient exam systems for the assessment and instant result; * The UK & internationally recognized accredited qualification; * Access to course content on mobile, tablet or desktop from anywhere anytime; * The benefit of career advancement opportunities; * 24/7 student support via email. CAREER PATH Easy to Advanced Data Structures Masterclass is a useful qualification to possess and would be beneficial for any related profession or industry such as: * Software Engineers * Programmers  * Web Designers  * Web Developers  * App Developers Unit 01: Introduction Module 01: Promo Video 00:02:00 Module 02: Data Structure Introduction 00:05:00 Module 03: Computational Complexity Analysis 00:13:00 Unit 02: Arrays Module 01: Static and Dynamic Arrays 00:12:00 Module 02: Dynamic Arrays Source Code 00:07:00 Unit 03: Linked List Module 01: Singly and Doubly Linked Lists 00:15:00 Module 02: Doubly Linked Lists Source Code 00:10:00 Unit 04: Stack Module 01: Stack 00:12:00 Module 02: Stack Implementation 00:04:00 Module 03: Stack Source Code 00:04:00 Unit 05: Queues Module 01: Queues (Part-1) 00:06:00 Module 02: Queues (Part-2) 00:06:00 Module 03: Queue Source Code 00:04:00 Unit 06: Priority Queues (PQs) Module 01: Priority Queues (PQs) with an interlude on heaps 00:13:00 Module 02: Turning Min PQ into Max PQ 00:06:00 Module 03: Adding Elements to Binary Heap 00:10:00 Module 04: Removing Elements from Binary Heap 00:14:00 Module 05: Priority Queue Binary Heap Source Code 00:16:00 Unit 07: Union Find Module 01: Disjoint Set 00:06:00 Module 02: Kruskal's Algorithm 00:06:00 Module 03: Union and Find Operations 00:11:00 Module 04: Path Compression Union Find 00:07:00 Module 05: Union Find Source Code 00:08:00 Unit 08: Binary Search Trees Module 01: Binary Trees and Binary Search Trees (BST) 00:13:00 Module 02: Inserting Element into a Binary Search Tree (BST) 00:06:00 Module 03: Removing Element from a Binary Search Tree (BST) 00:14:00 Module 04: Tree Traversals 00:12:00 Module 05: Binary Search Source Code 00:13:00 Unit 09: Fenwick Tree Module 01: Fenwick Tree Construction 00:06:00 Module 02: Point Updates 00:05:00 Module 03: Binary Indexed Tree 00:14:00 Module 04: Fenwick Tree Source Code 00:06:00 Unit 10: Hash Tables Module 01: Hash Table 00:17:00 Module 02: Separate Chaining 00:08:00 Module 03: Separate Chaining Source Code 00:12:00 Module 04: Open Addressing 00:11:00 Module 05: Linear Probing 00:14:00 Module 06: Quadratic Probing 00:09:00 Module 07: Double Hashing 00:15:00 Module 08: Removing Element Open Addressing 00:08:00 Module 09: Open Addressing Code 00:15:00 Unit 11: Suffix Array Module 01: Introduction 00:03:00 Module 02: The Longest Common Prefix (LCP) Array 00:03:00 Module 03: Using SA/LCP Array to Find Unique Substrings 00:05:00 Module 04: Longest Common Substring (LCS) 00:11:00 Module 05: Longest Common Substring (LCS) Full Example 00:07:00 Module 06: Longest Repeated Substring (LRS) 00:05:00 Unit 12: AVL Trees Module 01: Balanced Binary Search Trees (BBSTs) 00:09:00 Module 02: Inserting Elements into an AVL Tree 00:10:00 Module 03: Removing an AVL Tree 00:09:00 Module 04: AVL Tree Source Code 00:17:00 Unit 13: Indexed Priority Queue Module 01: Indexed Priority Queue (Part-1) 00:25:00 Module 02: Indexed Priority Queue Source Code 00:09:00 Unit 14: Sparse Tables Module 01: Sparse Table 00:26:00 Module 02: Sparse Table Source Code 00:07:00 Certificate and Transcript Order Your Certificates and Transcripts 00:00:00

Register on the Data Structure today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study this course through online learning and take the first steps towards a long-term career. The course consists of a number of easy to digest, in-depth modules, designed to provide you with a detailed, expert level of knowledge. Learn through a mixture of instructional video lessons and online study materials. Receive online tutor support as you study the course, to ensure you are supported every step of the way. Get a digital certificate as a proof of your course completion. The Data Structure course is incredibly great value and allows you to study at your own pace. Access the course modules from any internet-enabled device, including computers, tablet, and smartphones. The course is designed to increase your employability and equip you with everything you need to be a success. Enrol on the now and start learning instantly! What You Get With The Data Structure Course * Receive a e-certificate upon successful completion of the course * Get taught by experienced, professional instructors * Study at a time and pace that suits your learning style * 24/7 help and advice via email or live chat * Get full tutor support on weekdays (Monday to Friday) Course Design The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace. You are taught through a combination of * Video lessons * Online study materials Certification After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for £9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for £15.99, which will reach your doorsteps by post. Who Is This Course For: The course is ideal for those who already work in this sector or are an aspiring professional. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge. Requirements: The online training is open to all students and has no formal entry requirements. To study the Data Structure course, all your need is a passion for learning, a good understanding of English, numeracy, and IT skills. You must also be over the age of 16. COURSE CONTENT Unit 01: Introduction Module 01: Promo Video 00:02:00 Module 02: Data Structure Introduction 00:05:00 Module 03: Computational Complexity Analysis 00:13:00 Unit 02: Arrays Module 01: Static and Dynamic Arrays 00:12:00 Module 02: Dynamic Arrays Source Code 00:07:00 Unit 03: Liked List Module 01: Singly and Doubly Linked Lists 00:15:00 Module 02: Doubly Linked Lists Source Code 00:10:00 Unit 04: Stack Module 01: Stack 00:12:00 Module 02: Stack Implementation 00:04:00 Module 03: Stack Source Code 00:04:00 Unit 05: Queues Module 01: Queues (Part-1) 00:06:00 Module 02: Queues (Part-2) 00:06:00 Module 03: Queue Source Code 00:04:00 Unit 06: Priority Queues (PQs) Module 01: Priority Queues (PQs) with an interlude on heaps 00:13:00 Module 02: Turning Min PQ into Max PQ 00:06:00 Module 03: Adding Elements to Binary Heap 00:10:00 Module 04: Removing Elements from Binary Heap 00:14:00 Module 05: Priority Queue Binary Heap Source Code 00:16:00 Unit 07: Union Find Module 01: Disjoint Set 00:06:00 Module 02: Kruskal's Algorithm 00:06:00 Module 03: Union and Find Operations 00:11:00 Module 04: Path Compression Union Find 00:07:00 Module 05: Union Find Source Code 00:08:00 Unit 08: Binary Search Trees Module 01: Binary Trees and Binary Search Trees (BST) 00:13:00 Module 02: Inserting Element into a Binary Search Tree (BST) 00:06:00 Module 03: Removing Element from a Binary Search Tree (BST) 00:14:00 Module 04: Tree Traversals 00:12:00 Module 05: Binary Search Source Code 00:13:00 Unit 09: Fenwick Tree Module 01: Fenwick Tree Construction 00:06:00 Module 02: Point Updates 00:06:00 Module 03: Binary Indexed Tree 00:14:00 Module 04: Fenwick Tree Source Code 00:06:00 Unit 10: Hash Tables Module 01: Hash Table 00:17:00 Module 02: Separate Chaining 00:08:00 Module 03: Separate Chaining Source Code 00:12:00 Module 04: Open Addressing 00:11:00 Module 05: Linear Probing 00:14:00 Module 06: Quadratic Probing 00:09:00 Module 07: Double Hashing 00:15:00 Module 08: Removing Element Open Addressing 00:08:00 Module 09: Open Addressing Code 00:15:00 Unit 11: Suffix Array Module 01: Introduction 00:03:00 Module 02: The Longest Common Prefix (LCP) Array 00:03:00 Module 03: Using SA/LCP Array to Find Unique Substrings 00:05:00 Module 04: Longest Common Substring (LCS) 00:11:00 Module 05: Longest Common Substring (LCS) Full Example 00:07:00 Module 06: Longest Repeated Substring (LRS) 00:05:00 Unit 12: AVL Trees Module 01: Balanced Binary Search Trees (BBSTs) 00:09:00 Module 02: Inserting Elements into an AVL Tree 00:10:00 Module 03: Removing an AVL Tree 00:09:00 Module 04: AVL Tree Source Code 00:17:00 Unit 13: Indexed Priority Queue Module 01: Indexed Priority Queue (Part-1) 00:25:00 Module 02: Indexed Priority Queue Source Code 00:09:00 Unit 14: Sparse Tables Module 01: Sparse Table 00:26:00 Module 02: Sparse Table Source Code 00:07:00 FREQUENTLY ASKED QUESTIONS Are there any prerequisites for taking the course? There are no specific prerequisites for this course, nor are there any formal entry requirements. All you need is an internet connection, a good understanding of English and a passion for learning for this course. Can I access the course at any time, or is there a set schedule? You have the flexibility to access the course at any time that suits your schedule. Our courses are self-paced, allowing you to study at your own pace and convenience. How long will I have access to the course? For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime. Is there a certificate of completion provided after completing the course? Yes, upon successfully completing the course, you will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and can be shared on your various social networks. Can I switch courses or get a refund if I'm not satisfied with the course? We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase. How do I track my progress in the course? Our platform provides tracking tools and progress indicators for each course. You can monitor your progress, completed lessons, and assessments through your learner dashboard for the course. What if I have technical issues or difficulties with the course? If you encounter technical issues or content-related difficulties with the course, our support team is available to assist you. You can reach out to them for prompt resolution.

Transgender Awareness and Understanding giving you a good overview of all trans issues, pronouns, workplace equality and so much more

Implement machine learning-based clustering and classification in Python for pattern recognition and data analysis

Delve into the intricate world of 'Data Structure' with our comprehensive course, meticulously crafted for those who have a penchant for understanding the skeleton of software engineering. Data structures form the backbone of algorithmic efficiency, and mastering them is akin to holding the master key to software optimisation. Our course is a confluence of foundational knowledge and complex data structuring, ensuring that you emerge not only informed but also invigorated, ready to tackle any computational challenge thrown your way. Learning Outcomes * Gain foundational understanding of different data structures and their implementations. * Discover the intricate details of arrays, linked lists, stacks, and queues. * Develop the ability to effectively utilise advanced structures like AVL trees and Fenwick trees. * Master techniques for optimising algorithmic efficiency using suitable data structures. * Enhance problem-solving skills related to data storage and retrieval. WHY CHOOSE THIS DATA STRUCTURE COURSE? * Unlimited access to the course for a lifetime. * Opportunity to earn a certificate accredited by the CPD Quality Standards after completing this course. * Structured lesson planning in line with industry standards. * Immerse yourself in innovative and captivating course materials and activities. * Assessments are designed to evaluate advanced cognitive abilities and skill proficiency. * Flexibility to complete the Diploma in Data Structure at QLS Level 5 Course at your own pace, on your own schedule. * Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. WHO IS THIS DATA STRUCTURE COURSE FOR? * Individuals keen on deepening their computer science foundations. * Software developers aiming to optimise their code. * Students pursuing computer science and related disciplines. * Competitive coders desiring an edge in algorithm competitions. * Tech enthusiasts eager to understand the underpinnings of efficient programming. CAREER PATH * Software Developer: £25,000 - £45,000 * Algorithm Engineer: £40,000 - £60,000 * Data Scientist: £35,000 - £55,000 * Backend Developer: £28,000 - £50,000 * Systems Architect: £45,000 - £70,000 * Data Engineer: £30,000 - £55,000 Prerequisites This Diploma in Data Structure at QLS Level 5 does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Diploma in Data Structure at QLS Level 5 was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. CERTIFICATION After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Endorsed Certificate of Achievement from the Quality Licence Scheme Learners will be able to achieve an endorsed certificate after completing the course as proof of their achievement. You can order the endorsed certificate for only £115 to be delivered to your home by post. For international students, there is an additional postage charge of £10. Endorsement The Quality Licence Scheme (QLS) has endorsed this course for its high-quality, non-regulated provision and training programmes. The QLS is a UK-based organisation that sets standards for non-regulated training and learning. This endorsement means that the course has been reviewed and approved by the QLS and meets the highest quality standards. Please Note: Studyhub is a Compliance Central approved resale partner for Quality Licence Scheme Endorsed courses. COURSE CURRICULUM Unit 01: Introduction Module 01: Promo Video 00:02:00 Module 02: Data Structure Introduction 00:05:00 Module 03: Computational Complexity Analysis 00:13:00 Unit 02: Arrays Module 01: Static and Dynamic Arrays 00:12:00 Module 02: Dynamic Arrays Source Code 00:07:00 Unit 03: Linked List Module 01: Singly and Doubly Linked Lists 00:15:00 Module 02: Doubly Linked Lists Source Code 00:10:00 Unit 04: Stack Module 01: Stack 00:12:00 Module 02: Stack Implementation 00:04:00 Module 03: Stack Source Code 00:04:00 Unit 05: Queues Module 01: Queues (Part-1) 00:06:00 Module 02: Queues (Part-2) 00:06:00 Module 03: Queue Source Code 00:04:00 Unit 06: Priority Queues (PQs) Module 01: Priority Queues (PQs) with an interlude on heaps 00:13:00 Module 02: Turning Min PQ into Max PQ 00:06:00 Module 03: Adding Elements to Binary Heap 00:10:00 Module 04: Removing Elements from Binary Heap 00:14:00 Module 05: Priority Queue Binary Heap Source Code 00:16:00 Unit 07: Union Find Module 01: Disjoint Set 00:06:00 Module 02: Kruskal's Algorithm 00:06:00 Module 03: Union and Find Operations 00:11:00 Module 04: Path Compression Union Find 00:07:00 Module 05: Union Find Source Code 00:08:00 Unit 08: Binary Search Trees Module 01: Binary Trees and Binary Search Trees (BST) 00:13:00 Module 02: Inserting Element into a Binary Search Tree (BST) 00:06:00 Module 03: Removing Element from a Binary Search Tree (BST) 00:14:00 Module 04: Tree Traversals 00:12:00 Module 05: Binary Search Source Code 00:13:00 Unit 09: Fenwick Tree Module 01: Fenwick Tree Construction 00:06:00 Module 02: Point Updates 00:05:00 Module 03: Binary Indexed Tree 00:14:00 Module 04: Fenwick Tree Source Code 00:06:00 Unit 10: Hash Tables Module 01: Hash Table 00:17:00 Module 02: Separate Chaining 00:08:00 Module 03: Separate Chaining Source Code 00:12:00 Module 04: Open Addressing 00:11:00 Module 05: Linear Probing 00:14:00 Module 06: Quadratic Probing 00:09:00 Module 07: Double Hashing 00:15:00 Module 08: Removing Element Open Addressing 00:08:00 Module 09: Open Addressing Code 00:15:00 Unit 11: Suffix Array Module 01: Introduction 00:03:00 Module 02: The Longest Common Prefix (LCP) Array 00:03:00 Module 03: Using SA/LCP Array to Find Unique Substrings 00:05:00 Module 04: Longest Common Substring (LCS) 00:11:00 Module 05: Longest Common Substring (LCS) Full Example 00:07:00 Module 06: Longest Repeated Substring (LRS) 00:05:00 Unit 12: AVL Trees Module 01: Balanced Binary Search Trees (BBSTs) 00:09:00 Module 02: Inserting Elements into an AVL Tree 00:10:00 Module 03: Removing an AVL Tree 00:09:00 Module 04: AVL Tree Source Code 00:17:00 Unit 13: Indexed Priority Queue Module 01: Indexed Priority Queue (Part-1) 00:25:00 Module 02: Indexed Priority Queue Source Code 00:09:00 Unit 14: Sparse Tables Module 01: Sparse Table 00:26:00 Module 02: Sparse Table Source Code 00:07:00 Assignment Assignment - Diploma in Data Structure at QLS Level 5 04:00:00 Order your QLS Endorsed Certificate Order your QLS Endorsed Certificate 00:00:00

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

QLS Endorsed + CPD QS Accredited - Dual Certification | Instant Access | 24/7 Tutor Support | All-Inclusive Cost

In this course we deepen participants understanding of systemic racism and the spectrum of privilege. It challenges the participants to examine their behaviours and take close looks at some of the views they have held since a very young age, e.g. this area is a “bad” area, because it has a high proportion of black/brown people living in it, or that young black men in tracksuits are “thugs”. We think about where these messages come from and how people are indoctrinated by the media. COURSE CATEGORY Inclusion Team Building Leadership Emotional needs DESCRIPTION In this course we deepen participants understanding of systemic racism and the spectrum of privilege. It challenges the participants to examine their behaviours and take close looks at some of the views they have held since a very young age, e.g. this area is a “bad” area, because it has a high proportion of black/brown people living in it, or that young black men in tracksuits are “thugs”. We think about where these messages come from and how people are indoctrinated by the media.  We explore the reasons why white people are so defensive when it comes to talking about race. We discuss having racial biases and the implications of them, such as unconsciously insulting people around us in the workplace. When we become aware of how our behaviours can affect people, we then look for solutions.  The course is designed for groups of professionals to come together as a team to try and take responsibility for the racism that goes on in their workplace – empowering the leadership to have difficult conversations with team members and create a paradigm shift across the entire organisation.  Please come with an open mind, and you might be surprised at what you find out. We are striving for a world where racism is an open conversation and not a topic that we shy away from.  TESTIMONIALS “That was a really insightful session and thought provoking. I would love to attend more sessions on racism. Thank you for the engaging questions and delivery” “Lots of things to reflect on!” LEARNING OBJECTIVES Participants will: 1. 1. Confront their own racism and unconscious biases  2. Become aware of the ways they treat people differently based on race  3. Think about practical changes they can make in their workplaces  WHO IS IT FOR? * Leadership teams seeking guidance and reflection  * Educators who want to get it right  * People who have had not had much contact with people outside their own race * People who believe they are “not racist”  COURSE CONTENT * Background – brief history of systemic racism  * Racism as a binary – the problem with thinking only “bad” people can be racist  * Trust – how do we feel around people we don’t trust?   * Difference – how do we act when we feel different?  * What do Good Manners look like around people of different cultures?  * What does Good Allyship look like in the workplace?  * Why don’t we talk about race?  * What does your race mean to you?  * Examining our privilege – activity  * Interracial friendship video * Visioning – what does the ideal workplace look like?  * Setting Actions – what achievable actions can we set to bring us closer to our dream future? 

Embark on a captivating journey into the world of artificial intelligence with our course, 'Machine Learning Basics.' This voyage begins with an immersive introduction, setting the stage for an exploration into the intricate and fascinating realm of machine learning. Envision yourself unlocking the mysteries of algorithms and data patterns, essential skills in today's technology-driven landscape. The course offers a comprehensive foray into the core principles of machine learning, starting from the very basics and gradually building to more complex concepts, making it an ideal path for beginners and enthusiasts alike. As you delve deeper, each section unravels a vital component of machine learning. Grasp the essentials of regression analysis, understand the role of predictors, and navigate through the functionalities of Minitab, a key tool in data analysis. Journey through the structured world of regression trees and binary logistic regression, and master the art of classification trees. The course also emphasizes the importance of data cleaning and constructing robust data models, culminating in the achievement of learning success. This course is not just an educational experience; it's a gateway to the future of data science and AI. Learning Outcomes * Comprehend the basic principles and applications of machine learning. * Develop proficiency in regression analysis and predictor identification. * Gain practical skills in Minitab for data analysis. * Understand and apply regression and classification trees. * Acquire expertise in data cleaning and model creation. WHY CHOOSE THIS MACHINE LEARNING BASICS COURSE? 1. Unlimited access to the course for a lifetime. 2. Opportunity to earn a certificate accredited by the CPD Quality Standards and CIQ after completing this course. 3. Structured lesson planning in line with industry standards. 4. Immerse yourself in innovative and captivating course materials and activities. 5. Assessments designed to evaluate advanced cognitive abilities and skill proficiency. 6. Flexibility to complete the Course at your own pace, on your own schedule. 7. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. 8. Unlock career resources for CV improvement, interview readiness, and job success. WHO IS THIS MACHINE LEARNING BASICS COURSE FOR? * Novices eager to delve into machine learning. * Data enthusiasts looking to enhance their analytical skills. * Professionals in IT and related fields expanding their expertise. * Academics and students in computer science and data studies. * Career changers interested in the field of data science and AI. CAREER PATH * Data Analyst - £30,000 to £55,000 * Machine Learning Engineer - £40,000 to £80,000 * AI Developer - £35,000 to £75,000 * Business Intelligence Analyst - £32,000 to £60,000 * Research Scientist (Machine Learning) - £45,000 to £85,000 * Software Engineer (AI Specialization) - £38,000 to £70,000 PREREQUISITES This Machine Learning Basics does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Machine Learning Basics was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. CERTIFICATION After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. COURSE CURRICULUM Section 01: Introduction Introduction to Supervised Machine Learning 00:06:00 Section 02: Regression Introduction to Regression 00:13:00 Evaluating Regression Models 00:11:00 Conditions for Using Regression Models in ML versus in Classical Statistics 00:21:00 Statistically Significant Predictors 00:09:00 Regression Models Including Categorical Predictors. Additive Effects 00:20:00 Regression Models Including Categorical Predictors. Interaction Effects 00:18:00 Section 03: Predictors Multicollinearity among Predictors and its Consequences 00:21:00 Prediction for New Observation. Confidence Interval and Prediction Interval 00:06:00 Model Building. What if the Regression Equation Contains 'Wrong' Predictors? 00:13:00 Section 04: Minitab Stepwise Regression and its Use for Finding the Optimal Model in Minitab 00:13:00 Regression with Minitab. Example. Auto-mpg: Part 1 00:17:00 Regression with Minitab. Example. Auto-mpg: Part 2 00:18:00 Section 05: Regression Trees The Basic idea of Regression Trees 00:18:00 Regression Trees with Minitab. Example. Bike Sharing: Part1 00:15:00 Regression Trees with Minitab. Example. Bike Sharing: Part 2 00:10:00 Section 06: Binary Logistics Regression Introduction to Binary Logistics Regression 00:23:00 Evaluating Binary Classification Models. Goodness of Fit Metrics. ROC Curve. AUC 00:20:00 Binary Logistic Regression with Minitab. Example. Heart Failure: Part 1 00:16:00 Binary Logistic Regression with Minitab. Example. Heart Failure: Part 2 00:18:00 Section 07: Classification Trees Introduction to Classification Trees 00:12:00 Node Splitting Methods 1. Splitting by Misclassification Rate 00:20:00 Node Splitting Methods 2. Splitting by Gini Impurity or Entropy 00:11:00 Predicted Class for a Node 00:06:00 The Goodness of the Model - 1. Model Misclassification Cost 00:11:00 The Goodness of the Model - 2 ROC. Gain. Lit Binary Classification 00:15:00 The Goodness of the Model - 3. ROC. Gain. Lit. Multinomial Classification 00:08:00 Predefined Prior Probabilities and Input Misclassification Costs 00:11:00 Building the Tree 00:08:00 Classification Trees with Minitab. Example. Maintenance of Machines: Part 1 00:17:00 Classification Trees with Miitab. Example. Maintenance of Machines: Part 2 00:10:00 Section 08: Data Cleaning Data Cleaning: Part 1 00:16:00 Data Cleaning: Part 2 00:17:00 Creating New Features 00:12:00 Section 09: Data Models Polynomial Regression Models for Quantitative Predictor Variables 00:20:00 Interactions Regression Models for Quantitative Predictor Variables 00:15:00 Qualitative and Quantitative Predictors: Interaction Models 00:28:00 Final Models for Duration and TotalCharge: Without Validation 00:18:00 Underfitting or Overfitting: The 'Just Right Model' 00:18:00 The 'Just Right' Model for Duration 00:16:00 The 'Just Right' Model for Duration: A More Detailed Error Analysis 00:12:00 The 'Just Right' Model for TotalCharge 00:14:00 The 'Just Right' Model for ToralCharge: A More Detailed Error Analysis 00:06:00 Section 10: Learning Success Regression Trees for Duration and TotalCharge 00:18:00 Predicting Learning Success: The Problem Statement 00:07:00 Predicting Learning Success: Binary Logistic Regression Models 00:17:00 Predicting Learning Success: Classification Tree Models 00:09:00