169 Binary courses in Sheffield delivered Online

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.

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

The underlying patterns in your data hold vital insights; unearth them with cutting-edge clustering and classification techniques in R

C++ training course description A hands on introduction to programming in the C++ language. The course concentrates on aspects that will be new to experienced C programmers and so is not suitable for those without C knowledge. What will you learn Write C++ programs Debug C++ programs. Examine existing code and determine its function. Use classes, function overloading, operator overloading, inheritance and virtual functions within C++ programs. C++ training course details Who will benefit: Programmers needing to write C++ code. Programmers needing to maintain C++ code. Prerequisites: C programming foundation. Duration 5 days C++ training course contents The origins of C++ C++ as a better C, C++ and Object Oriented Programming, encapsulation, polymorphism, inheritance. C++ standards. Getting started Simple C++ programs. Classes Basics, constructor and destructor functions, member and friend functions. Using objects. Default, copy and conversion constructors. A better C Arrays, pointers and references, new and delete. Improved safety with smart pointers, Resource Acquisition in Initialization (RAII). Functions in C++ Function overloading, default arguments, inline functions, Lambda functions. Templates Template classes and functions. Standard Library Containers, Iterators, algorithms, function objects. Operator overloading Basics, binary operators, the this pointer, relational operators, unary operators. Members versus friends. Inheritance Base class access control, protected members, multiple inheritance, virtual base classes. More I/O Manipulators, customising inserters, extractors. File I/O. Virtual functions Pointers to derived classes, run time polymorphism. Exception handling Throwing errors, trying code and catching errors.

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? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards and CIQ after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the 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. 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

Sockets programming training course description A hands on course for programmers using Sockets. It is important to recognise that the course assumes that delegates are already familiar with TCP/IP and Python. Practical exercises follow all the major theory sessions. What will you learn Read Python programs which use Sockets. Write Python programs which use Sockets. Debug Python programs which use Sockets. Sockets programming training course details Who will benefit: Programmers working with network applications. Prerequisites: TCP/IP foundation for engineers Python for network engineers Duration 2 days Sockets programming training course contents What is a socket? Review of IP, ICMP, UDP vs TCP, IP addresses, protocol numbers, ports. API's, UNIX I/O, sockets. SOCK_STREAM, SOCK_DGRAM. Hands on Compile and run code. The systems calls Clients and servers, structs, socket(), bind(), connect(), listen(), accept(), send(), recv(), sendto (), recvfrom(), close(), shutdown(), getpeername(), gethostname(). Hands on Walk through of example client and server code. First code TCP connections, passive opens, active opens. Hands on Write a simple 'hello world' server and client. Application protocols User character stream, ASCII turn taking, binary protocols. Hands on Raw SMTP, Writing a mail client. Clients Concurrency, polling, threads, event driven programming. Hands on Conferencing application. Servers Concurrency, stateful, stateless. Forks and execs. inetd. Hands on Running servers with and without inetd, chroot jails, conferencing server modifications. Advanced techniques Blocking, select(), partial send(s). Raw sockets, example sockets using Java, Perl and PHP. Hands on A broadcast application.

Perl training course description A hands on introduction to programming in Perl. What will you learn Write Perl programs. Use Perl modules. Debug Perl programs. Examine existing code and determine its function. Perl training course details Who will benefit: Anyone wishing to learn Perl. Prerequisites: None although experience in another high level language would be useful. Duration 5 days Perl training course contents Introduction to Perl What is Perl? When to use Perl, downloading Perl, installing Perl, documentation, perldoc, running Perl, the Perl environment. Perl under UNIX, Perl under Windows. "Hello world". Variables Scalars, data types, $_, strings and numbers, assignment, constants, strict, scope, STDIN. Operators Number operators, string operators, precedence and associativity, converting numbers and strings, shortcut operators. Flow control Blocks, if, else, elseif, unless, foreach, while, for do, until. Regular expressions What are regular expressions? Pattern matching, Perl as a filter, file editing. Strings Comparing strings, concatenating, substrings, chomp, chop, formatting, string manipulation. Subroutines Comparing strings, concatenating, substrings, chomp, chop, formatting, string manipulation. Arrays and hashes Working with arrays, element access, push(), pop(), shift(), unshift(), <STDIN> as an array, associative arrays, hashes of arrays, hash references, arrays of hashes, hashes of hashes. Files Simple file handling, open, close, <FILEHANDLE>, <>, file tests, directory access, directory handles, database access, packing and packing binary data. I/O STDIN, STDOUT and STDERR, Command line arguments,@ARGV. Perl debugging The built in debugger, running the debugger, debugger commands, graphical debuggers. Script syntax errors, single stepping, breakpoints, watches. Packages and modules CPAN, Finding modules, installing modules, using modules, scope. Report formatting Formats, defining a format, invoking a format, field holders. Process management System interaction, system(), exec(), signals. Security issues.

Pronouns are a fundamental aspect of human communication, and recognizing their importance, especially in the context of gender identity, is essential for creating an inclusive and welcoming workspace. This comprehensive course is designed to foster an understanding of the significance of pronoun inclusivity in today's diverse work environments. Analyze the evolving landscape of gender identities and pronoun usage, shedding light on non-binary, genderqueer, and other identities that may not conform to traditional binary gender norms. Participants will explore the profound impact that pronoun inclusivity can have on teamwork, communication, and overall workplace satisfaction. Learning Objectives Define and explain the concept of pronoun inclusivity and its significance in fostering diversity and inclusion in the workplace.;Comprehend the evolving landscape of gender identities and be familiar with a variety of pronouns beyond the traditional he/him and she/her.;Recognize the importance of pronoun inclusivity in attracting and retaining diverse talent, creating a respectful work environment, and reducing communication breakdowns.;Implement practical strategies to promote pronoun inclusivity within your organization, including introducing pronouns during introductions, updating materials, and encouraging respectful language use.

Register on the Machine Learning Basics 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 Machine Learning Basics 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 Machine Learning Basics Receive an 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 Get instant feedback on assessments 24/7 help and advice via email or live chat Get full tutor support on weekdays (Monday to Friday) Certificate of Achievement Endorsed Certificate of Achievement from the Quality Licence Scheme Upon successful completion of the final assessment, you will be eligible to apply for the Quality Licence Scheme Endorsed Certificate of achievement. This certificate will be delivered to your doorstep through the post for £119. An extra £10 postage charge will be required for students leaving overseas.  CPD Accredited Certificate 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 Machine Learning Basics, 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 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 Total Charge: 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 Total Charge 00:14:00 The 'Just Right' Model for Toral Charge: 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 Order your Certificates & Transcripts Order your Certificates & Transcripts 00:00:00 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.

Apache training course description A hands on training course covering installation, configuration and management of the Apache web server. What will you learn Install Apache. Configure Apache. Manage Apache. Build static and dynamic web sites with Apache. Secure Apache. Apache training course details Who will benefit: Technical staff working with Apache. Prerequisites: TCP/IP foundation for engineers. UNIX fundamentals Duration 3 days Apache training course contents Installing Apache What is Apache? Apache versions, history, downloading Apache, source distribution, compilation, binary distribution, installation, platform considerations. Hands on Downloading and installing Apache. Controlling the Apache server Running Apache, automatic Apache start, starting, stopping, restarting Apache. Checking Apache status. Hands on Server control. Configuration Serving webpages, setting the document root, applying configuration changes, Configuration files, httpd.conf, syntax, directives, modules, utilities, turning features on/off. Hands on basic Apache configuration. More configuration MIME, URL mapping, content negotiation, indexing, performance tuning. Logging log file content, configuration, log file locations, error logging, browser errors, error page configuration, forbidden index pages. Hands on Log files. Security File permissions, .htaccess, protecting files with passwords, password files, authentication, restricting access by IP address. Secure HTTP HTTPS, installing mod_ssl, certificates, configuring mod_ssl, http and https coexistence Virtual hosts Multiple sites on one server, separate configuration files, IP based, name based, port based, virtual host names, enabling, defining, configuring, aliases, testing, https virtual hosts. Hands on Virtual hosts. Dynamic sites Dynamic sites, CGI, PHP, PERL, CGI programs, example CGI scripts, Apache and CGI, CGI parameters, CGI issues, PHP, mod_php, Perl and Apache, mod_perl, installing mod_perl. Hands on CGI, PHP and Perl with Apache. Modules What are modules, standard modules, loading modules, mod_speling, mod_rewrite, other special purpose modules, URL rewriting, redirection, URL transformation, browser dependent pages. Hands on Working with modules.