314 Algorithms courses in Coventry delivered Online

Artificial neural networks (ANNs) are the most powerful machine learning algorithms available today. They are capable of learning complex relationships in data, and they have been used to achieve state-of-the-art results in a wide variety of fields, including image recognition, natural language processing, and speech recognition. The Future of Machine Learning is Here! This Project on Deep Learning - Artificial Neural Network course will teach you how to build and train ANNs from scratch. You will learn about the different components of an ANN, such as the input layer, hidden layers, and output layer. You will also learn about the different activation functions that can be used in ANNs, and you will see how to optimise ANNs for different tasks. In addition to the theoretical concepts, you will also get experience with ANNs. You will work on a project where you will build an ANN to classify images. You will use the TensorFlow library to build your ANN, and you will see how to train your ANN on a dataset of images. By the end of this Project on Deep Learning - Artificial Neural Network course, you will have a deep understanding of ANNs and how to use them. You will be able to build your own ANNs to solve a variety of problems. You will also be able to use the TensorFlow library to build and train ANNs. So what are you waiting for? Enrol in this course today and start learning about the future of machine learning!   Learning Outcomes: Through this comprehensive course, you should be able to: Understand the fundamental concepts of deep learning and artificial neural networks. Install and configure an artificial neural network framework. Preprocess and structure data for optimal model performance. Encode data effectively for neural network training and predictions. Build and deploy artificial neural networks for real-world applications. Address data imbalance challenges and optimise model accuracy.   Who is this course for? This Project on Deep Learning - Artificial Neural Network course is ideal for: Data scientists and machine learning practitioners seeking to expand their knowledge. Software engineers interested in leveraging deep learning techniques. Students pursuing a career in artificial intelligence and machine learning. Professionals looking to enhance their skills in neural network development. Individuals with a passion for exploring advanced machine learning techniques.   Career Path  Our course will prepare you for a range of careers, including: Deep Learning Engineer: £40,000 - £100,000 per year. Machine Learning Researcher: £45,000 - £120,000 per year. Data Scientist: £50,000 - £110,000 per year. Artificial Intelligence Specialist: £55,000 - £130,000 per year. Software Engineer (specialising in AI): £45,000 - £100,000 per year. Research Scientist (Machine Learning): £50,000 - £120,000 per year. AI Consultant: £60,000 - £150,000 per year. Certification After studying the course materials of the Project on Deep Learning - Artificial Neural Network (ANNs) there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Prerequisites This Project on Deep Learning - Artificial Neural Network (ANNs) does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Project on Deep Learning - Artificial Neural Network (ANNs) 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.   Course Curriculum Section 01: Introduction Introduction of Project 00:03:00 Section 02: ANN Installation Setup Environment for ANN 00:11:00 ANN Installation 00:09:00 Section 03: Data Preprocessing Import Libraries and Data Preprocessing 00:11:00 Data Preprocessing 00:07:00 Data Preprocessing Continue 00:10:00 Section 04: Data Encoding Data Exploration 00:10:00 Encoding 00:07:00 Encoding Continue 00:06:00 Preparation of Dataset for Training 00:04:00 Section 05: Steps to Build ANN Steps to Build ANN Part 1 00:06:00 Steps to Build ANN Part 2 00:06:00 Steps to Build ANN Part 3 00:06:00 Steps to Build ANN Part 4 00:09:00 Section 06: Predictions and Imbalance-Learn Predictions 00:11:00 Predictions Continue 00:08:00 Resampling Data with Imbalance-Learn 00:09:00 Resampling Data with Imbalance-Learn Continue 00:08:00

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python-experienced attendees who wish to be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Overview Working in a hands-on learning environment, guided by our expert team, attendees will learn to: Understand how data analysts and scientists gather and analyze data Perform data analysis and data wrangling using Python Combine, group, and aggregate data from multiple sources Create data visualizations with pandas, matplotlib, and seaborn Apply machine learning (ML) algorithms to identify patterns and make predictions Use Python data science libraries to analyze real-world datasets Use pandas to solve common data representation and analysis problems Build Python scripts, modules, and packages for reusable analysis code Perform efficient data analysis and manipulation tasks using pandas Apply pandas to different real-world domains with the help of step-by-step demonstrations Get accustomed to using pandas as an effective data exploration tool. Data analysis has become a necessary skill in a variety of domains where knowing how to work with data and extract insights can generate significant value. Geared for data team members with incoming Python scripting experience, Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will be able to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding lessons, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. Students will leave the course armed with the skills required to use pandas to ensure the veracity of their data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Introduction to Data Analysis Fundamentals of data analysis Statistical foundations Setting up a virtual environment Working with Pandas DataFrames Pandas data structures Bringing data into a pandas DataFrame Inspecting a DataFrame object Grabbing subsets of the data Adding and removing data Data Wrangling with Pandas What is data wrangling? Collecting temperature data Cleaning up the data Restructuring the data Handling duplicate, missing, or invalid data Aggregating Pandas DataFrames Database-style operations on DataFrames DataFrame operations Aggregations with pandas and numpy Time series Visualizing Data with Pandas and Matplotlib An introduction to matplotlib Plotting with pandas The pandas.plotting subpackage Plotting with Seaborn and Customization Techniques Utilizing seaborn for advanced plotting Formatting Customizing visualizations Financial Analysis - Bitcoin and the Stock Market Building a Python package Data extraction with pandas Exploratory data analysis Technical analysis of financial instruments Modeling performance Rule-Based Anomaly Detection Simulating login attempts Exploratory data analysis Rule-based anomaly detection Getting Started with Machine Learning in Python Learning the lingo Exploratory data analysis Preprocessing data Clustering Regression Classification Making Better Predictions - Optimizing Models Hyperparameter tuning with grid search Feature engineering Ensemble methods Inspecting classification prediction confidence Addressing class imbalance Regularization Machine Learning Anomaly Detection Exploring the data Unsupervised methods Supervised methods Online learning The Road Ahead Data resources Practicing working with data Python practice

This comprehensive course will help you learn how to use the power of Python to evaluate your deep learning-based recommender system data sets based on user ratings and choices with a practical approach to building a deep learning-based recommender system by adopting a retrieval-based approach based on a two-tower model.

Use deep learning, Computer Vision, and machine learning techniques to build an autonomous car with Python

The Computer Vision course with C++ and OpenCV with GPU support provides an introduction to computer vision and its applications using C++ and OpenCV with GPU acceleration. Students will learn about setting up the necessary environments, basic examples, background segmentation, object detection with OpenCV's ML module using C++ and CUDA, and optical flow. Learning Outcomes: Set up the required environments for C++ and OpenCV with GPU support. Understand the fundamentals of computer vision and its applications. Implement background segmentation techniques to extract relevant objects from the environment. Use OpenCV's ML module with C++ and CUDA to perform object detection efficiently. Apply optical flow algorithms to track object movements and analyze motion patterns. Why buy this Computer Vision: C++ and OpenCV with GPU support? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Computer Vision: C++ and OpenCV with GPU support there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this course for? This Computer Vision: C++ and OpenCV with GPU support course is ideal for Students Recent graduates Job Seekers Anyone interested in this topic People already working in the relevant fields and want to polish their knowledge and skill. Prerequisites This Computer Vision: C++ and OpenCV with GPU support does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Computer Vision: C++ and OpenCV with GPU support 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. Career path As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This Computer Vision: C++ and OpenCV with GPU support is a great way for you to gain multiple skills from the comfort of your home. Course Curriculum Unit 01: Set up Necesssary Environments Module 01: Driver installation 00:06:00 Module 02: Cuda toolkit installation 00:01:00 Module 03: Compile OpenCV from source with CUDA support part-1 00:06:00 Module 04: Compile OpenCV from source with CUDA support part-2 00:05:00 Module 05: Python environment for flownet2-pytorch 00:09:00 Unit 02: Introduction with a few basic examples! Module 01: Read camera & files in a folder (C++) 00:11:00 Module 02: Edge detection (C++) 00:08:00 Module 03: Color transformations (C++) 00:07:00 Module 04: Using a trackbar (C++) 00:06:00 Module 05: Image filtering with CUDA (Introduction to using OpenCV GPU methods on C++) 00:13:00 Unit 03: Background segmentation Module 01: Background segmentation with MOG (C++) 00:04:00 Module 02: MOG and MOG2 cuda implementation (C++ - CUDA) 00:03:00 Module 03: Special app: Track class 00:06:00 Module 04: Special app: Track bgseg Foreground objects 00:08:00 Unit 04: Object detection with openCV ML module (C++ CUDA) Module 01: A simple application to prepare dataset for object detection (C++) 00:08:00 Module 02: Train model with openCV ML module (C++ and CUDA) 00:13:00 Module 03: Object detection with openCV ML module (C++ CUDA) 00:06:00 Unit 05: Optical Flow Module 01: Optical flow with Farneback (C++) 00:08:00 Module 02: Optical flow with Farneback (C++ CUDA) 00:06:00 Module 03: Optical flow with Nvidia optical flow SDK (C++ CUDA) 00:05:00 Module 04: Optical flow with Nvidia Flownet2 (Python) 00:05:00 Module 05: Performance Comparison 00:07:00 Additional Resource Resources - Computer Vision: C++ and OpenCV with GPU support 00:00:00 Assignment Assignment - Computer Vision: C++ and OpenCV with GPU support 00:00:00

Welcome to this project-based course where you will build and deploy a realtor clone application using the latest version of React, Firebase, and Tailwind CSS. Learn to create and deploy a website professionally for showcasing to friends and clients, or adding it to your portfolio. Basic knowledge of HTML, CSS, and JavaScript is expected.

Duration 3 Days 18 CPD hours This course is intended for Experienced system administrators or network administrators, Network professionals who have experience working with VMware NSX Advanced Load Balancer and are responsible for designing or deploying Application Delivery Controllers solutions Overview By the end of the course, you should be able to meet the following objectives: Describe the NSX Advanced Load Balancer components and main functions Describe NSX Advanced Load Balancer Global Server Load Balancing architecture Explain NSX Advanced Load Balancer key features and benefits Understand and apply a Global Server Load Balancing design framework Deploy and configure NSX Advanced Load Balancer Global Server Load Balancing infrastructure Explain and Configure Global Server Load Balancing Application components such as Global Server Load Balancing Service, Global Server Load Balancing Pools and Health Monitors with related components Gather relevant information and perform basic troubleshooting of Global Server Load Balancing applications leveraging built-in NSX Advanced Load Balancer tooling Describe and Configure NSX Advanced Load Balancer application and infrastructure monitoring This 3-day course prepares you to lead VMware NSX Advanced Load Balancer (Avi Networks) Global Server Load Balancing design and deployment projects by providing an understanding of general design processes, frameworks and configurations. You look at the design and deployment considerations for Global Server Load Balancing as part of an overall software-defined data center design. This course covers key NSX Advanced Load Balancer (Avi Networks) Global Server Load Balancing features and functionalities offered in the NSX Advanced Load Balancer 18.2 release. Access to a software-defined data center environment is provided through hands-on labs to reinforce the skills and concepts presented in the course. Course Introduction Introductions and course logistics Course objectives Introduction to NSX Advanced Load Balancer Introduce NSX Advanced Load Balancer Discuss NSX Advanced Load Balancer use cases and benefits Explain NSX Advanced Load Balancer architecture and components Explain the management, control, data, and consumption planes and functions Virtual Services Configuration Concepts Explain Virtual Service components Explain Virtual Service types Explain and configure basic virtual services components such as Application Profiles, Network Profiles, Pools and Health Monitors DNS Foundations Review, discuss and explain DNS fundamentals Describe NSX Advanced Load Balancer DNS and IPAM providers Global Server Load Balancing Introduce Global Server Load Balancing concepts and benefits Explain and configure NSX Advanced Load Balancer infrastructure Explain and configure DNS Virtual Service components Explain and configure GSLB Service Engine Group Describe and configure GSLB Sites Explain and configure basic GSLB Services, to include pools and health monitors Describe GSLB Service Load Balancing algorithms Explain and configure Data and Control Plane-based Health Monitors Describe GSLB Health Monitor Proxy Global Server Load Balancing Advanced Topics Explain and configure advanced GSLB service properties such as different type of pool members, Host Header and TLS SNI extensions handling within GSLB Health Monitors Describe EDNS Client Subnet Describe Geo-aware Global Server Load Balancing Design and configure Geo-aware Global Server Load Balancing Describe and leverage DNS Policies to customize client experience Explain and configure Topology-aware Global Server Load Balancing Explain and configure GSLB 3rd party sites Describe GSLB Health Monitor sharding Describe GSLB Service Engine sizing implications Troubleshooting NSX Advanced Load Balancer GSLB Solution Introduce Infrastructure and Application troubleshooting Concepts Describe Control Plane and Data Plane-based troubleshooting Describe GSLB Infrastructure troubleshooting Describe GSLB Services troubleshooting Explain Health Monitors troubleshooting Describe Geo-aware and Topology-based GSLB Services troubleshooting Explain Application Analytics and Logs Describe Client Logs analysis Leverage CLI for advanced data plane troubleshooting Monitoring NSX Advanced Load Balancer Solution Describe NSX Advanced Load Balancer Events Describe and configure NSX Advanced Load Balancer Alerts Describe NSX Advanced Load Balancer monitoring capabilities leveraging SNMP, Syslog and Email

Description Learn the methods, techniques, and vivid functions of hacking tools practically and theoretically doing the Network Hacking Diploma Level 3 course. Its precise contents guide you on your quest to become efficient in this field. If you are a network and system engineer, security officer, or IT passionate, this course is very effective for you. The course is designed in such a way that will assist you to become an ethical hacker knowing the facts about how to scan a network to identify its strength and weakness and perform in system hacking. The lab-based practical approaches of this course will assist you to know some vivid activities of Virus and Worms, Trojans, and Backdoors along with how to penetrate on the wireless network. At the end of the course, knowing the penetration system, you can mastery of hacking techniques and methods efficiently. Assessment: This course does not involve any MCQ test. Students need to answer assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Once the answers are submitted, the instructor will check and assess the work. Certification: After completing and passing the course successfully, you will be able to obtain an Accredited Certificate of Achievement. Certificates can be obtained either in hard copy at a cost of £39 or in PDF format at a cost of £24. Who is this Course for? Network Hacking Diploma Level 3 is certified by CPD Qualifications Standards and CiQ. This makes it perfect for anyone trying to learn potential professional skills. As there is no experience and qualification required for this course, it is available for all students from any academic background. Requirements Our Network Hacking Diploma Level 3 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. Career Path After completing this course you will be able to build up accurate knowledge and skills with proper confidence to enrich yourself and brighten up your career in the relevant job market. Introduction Introduction 00:01:00 Introduction to Ethical Hacking. Footprinting and Reconnaissance Introduction to Ethical Hacking. Footprinting and Reconnaissance 00:25:00 Demo - Information Gathering using Google Dorks and DNS Queris 00:04:00 Demo - Scanning and Enumeration 00:08:00 Scanning Networks, Enumeration and Discovering Vulnearbilities Scanning and enumeration 00:09:00 Vulnerabilties Identification 00:08:00 Demo - Installing Nessus Scanner 00:04:00 Demo - Use Nessus to Discover Vulnerabilities 00:05:00 Demo - Using Nikto to discover Web Vulnerabilities 00:05:00 Demo - Using Paros for Vulnerability Discovery 00:05:00 Demo - Use Dirbuster to brute force sub-directories and filenames 00:03:00 System Hacking and Vulnerability Exploitation System hacking - vulnerability exploitation 00:06:00 Passwords 00:12:00 Authentication 00:07:00 Basics of Sniffing Sniffing 00:15:00 Metasploit Metasploit 00:17:00 Demo - Exploiting FTP Server Vulnerability using Metasploit 00:12:00 Demo - Post Exploitation Example 00:01:00 Demo - Exploiting NFS Vulnerability and exporting SSH Keys to the 00:10:00 Demo - Eploiting Samba Service on Linux using Metasploit 00:03:00 Demo - Windows backdoor using Metasploit 00:14:00 Trojans, Backdoors, Viruses and Worms Trojans and Backdoors 00:05:00 Computer viruses and worms 00:09:00 Cryptography Cryptography concepts 00:05:00 Cryptographic Algorithms 00:11:00 Cryptography and cryptanalysis tools. Cryptography attacks 00:03:00 Demo - Hack SSH passwords using Medusa 00:05:00 Hack the SSH Password using Hydra 00:05:00 Hack Linux Passwords using John the Ripper 00:03:00 Penetration Testing on Wireless Networks Penetration Testing on Wireless Networks 00:07:00 Case Study - Windows Hosted Network Bug or Feature 00:11:00 Penetration Testing Overview. Final words Penetration Testing Overview. Final Words 00:06:00 Bonus - OWASP Top 10 Vulnerabilities 00:18:00 (Bonus) Attacking the users trough websites - XSS and Beef-XSS Introduction to Cross-Site Scripting and Beef-XSS 00:08:00 XSS example - reflected 00:10:00 XSS example - stored 00:07:00 Beef-XSS Demo 00:16:00 Certificate and Transcript Order Your Certificates and Transcripts 00:00:00

Getting Started OTHM Level 4 Diploma in Information Technology gives an overview of how the information technology sector can influence the scope of the learning environment in the present scenario. This course helps learners learn about the role of Information technology in business communication. The qualification helps the learner to understand the role of IT in productivity and profitability in high levels of business operations. Key Benefits The qualification will benefit learners to: To understand the role of IT in productivity and profitability in high levels of business operations. To attain the practical knowledge, skills, capabilities and competencies assessed in academic terms as equivalent to Higher National Certificates (HNC) and Year 1 of a three-year UK Bachelor's degree programme. To learn the fundamentals of cyber security, protection methods and how to manage a cyber-security attack. To attain a basic understanding of object-oriented programming languages and how to produce effective code. To gain a perspective on software development and the basic principles of algorithms. To create awareness of system analysis and design in an organisational context. To provide learners with an understanding of current web and mobile application design technology and the practices and tools used. To understand computer networking essentials and cloud technologies, their operating principles, protocols, standards, security considerations, and prototypes associated with this field. To understand the interaction between communications, knowledge and information. Completing the OTHM Level 4 Diploma in Information Technology provides learners with the opportunity for various academic progressions, including the OTHM Level 5 Diploma in Information Technology. Ofqual (Office of the Qualifications and Examinations Regulation) approves and regulates the qualification.   Key Highlights Do you want to avoid the recent technological know-how of Information Technology in Business Communication? Then, the OTHM Level 4 Diploma in Information Technology is the ideal starting point for your career journey. The program allows the learners to develop a broad base of knowledge and skills that will enable them to work in various roles in the IT industry. Remember! The assessment for the qualification is done based on assignments only, and you do not need to worry about writing any exam. With the School of Business and Technology London, you can complete the qualification at your own pace, choosing online or blended learning from the comfort of your home. Learning and pathway materials and study guides developed by our OTHM-approved tutors, who would be available around the clock in our cutting-edge learning management system. Most importantly, at the School of Business and Technology London, we will provide comprehensive tutor support through our dedicated support desk. If you choose your course with blended learning, you will also enjoy live sessions with an assigned tutor, which you can book at your convenience. Career Pathways The OTHM Level 4 Diploma in Information Technology can open many career pathways including, but not limited to: Application Analyst with an estimated average salary of £36,015 per annum Database Administrator, with an estimated average salary of £38,246 per annum Games Developer with an estimated average salary of £28,905 per annum Information Systems Manager, with an estimated average salary of £44,785 per annum IT Consultant with an estimated average salary of £37,485 per annum Systems Analyst, with an estimated average salary of £37,500 per annum Web Designer with an estimated average salary of £29,235 per annum About Awarding Body OTHM is an established and recognised Awarding Organisation (Certification Body) launched in 2003. OTHM has already made a mark in the UK and global online education scenario by creating and maintaining a user-friendly and skill based learning environment. OTHM has both local and international recognition which aids OTHM graduates to enhance their employability skills as well as allowing them to join degree and/or Master top-up programmes. OTHM qualifications has assembled a reputation for maintaining significant skills in a wide range of job roles and industries which comprises Business Studies, Leadership, Tourism and Hospitality Management, Health and Social Care, Information Technology, Accounting and Finance, Logistics and Supply Chain Management. What is included? Outstanding tutor support that gives you supportive guidance all through the course accomplishment through the SBTL Support Desk Portal. Access our cutting-edge learning management platform to access vital learning resources and communicate with the support desk team. Quality learning materials such as structured lecture notes, study guides, and practical applications, which include real-world examples and case studies, will enable you to apply your knowledge. Learning materials are provided in one of the three formats: PDF, PowerPoint, or Interactive Text Content on the learning portal. The tutors will provide Formative assessment feedback to improve the learners' achievements. Assessment materials are accessible through our online learning platform. Supervision for all modules Multiplatform accessibility through an online learning platform. This facilitates SBTL in providing learners with course materials directly through smartphones, laptops, tablets or desktops, allowing students to study at their convenience. Live Classes (for Blended Learning Students only). Assessment Time-constrained scenario-based assignments. No examinations. Entry Requirements Learners must be 18 years old or older. Relevant NQF/QCF/RQF Level 3 Diploma or equivalent. International students whose first language is not English must score 5.5 or above in the IELTS Examination or equivalent. Progression Learners completing the OTHM Level 4 Diploma in Information Technology will allow progress to: OTHM Level 5 Diploma in Information Technology. Why gain a OTHM Qualification? Industry-focused programmes which meet global standards. Programs reviewed by highly qualified experts in the relevant sector. Career enhancement through advanced knowledge and skills that meet 21st-century employer needs. Availability of globally approved centres for enrolling in the desired program. Availability of flexible study options. The OTHM Level 4 Diploma in Information Technology consists of 6 mandatory units for a combined total of 120 credits, 1200 hours Total Qualification Time (TQT) and 480 Guided Learning Hours (GLH) for the completed qualification Learners must request before enrolment to interchange unit(s) other than the preselected units shown in the SBTL website because we need to make sure the availability of learning materials for the requested unit(s). SBTL will reject an application if the learning materials for the requested interchange unit(s) are unavailable. Learners are not allowed to make any request to interchange unit(s) once enrolment is complete. UNIT1- Programming Foundations Reference No : A/617/2265 Credit : 20 || TQT : 200 Unit I entitles basic understanding of Object-oriented programming languages and how to produce effective code. Unit I also helps the non-programming technical staff to gain an idea about software development. The course is language neutral and teaches general concepts. UNIT2- System Analysis and Design Reference No : F/617/2266 Credit : 20 || TQT : 200 Unit II focuses on developing learners' awareness of analysis and design in an organisational context. The unit also contains various techniques used within systems analysis and design and the methodologies used in the system development process. UNIT3- Web and Multimedia Applications Reference No : J/617/2267 Credit : 20 || TQT : 200 The unit aims to provide learners with an understanding of current design technology and the practices and tools used. The learner will develop the ability to create new websites and will gain advance skills in web development. UNIT4- Computer and Network Technology Reference No : L/617/2268 Credit : 20 || TQT : 200 The Unit enables the learners with knowledge of computer networking essentials, how they operate, protocols, standards, security considerations and the prototypes associated with a range of networking technologies. Learners will also explore a range of hardware and related software and will learn to configure and install these UNIT5- Software Development Reference No : R/617/2269 Credit : 20 || TQT : 200 Unit V introduces the learners about the fundamental concepts of programming by focusing on software development process. It also briefs about the tools that assist in this process. Learners are given the choice to use a programming language of their choice. UNIT6- Managing Business Information Reference No : J/617/2270 Credit : 20 || TQT : 200 Unit covers the influence of IT system in keeping a company up to date with communication and knowledge. Learners will understand the interaction between communications, knowledge and information. It also briefs how IT systems can be used as a management tool for collecting, storing, disseminating and providing access to knowledge and information. Delivery Methods School of Business & Technology London provides various flexible delivery methods to its learners, including online learning and blended learning. Thus, learners can choose the mode of study as per their choice and convenience. The program is self-paced and accomplished through our cutting-edge Learning Management System. Learners can interact with tutors by messaging through the SBTL Support Desk Portal System to discuss the course materials, get guidance and assistance and request assessment feedbacks on assignments. We at SBTL offer outstanding support and infrastructure for both online and blended learning. We indeed pursue an innovative learning approach where traditional regular classroom-based learning is replaced by web-based learning and incredibly high support level. Learners enrolled at SBTL are allocated a dedicated tutor, whether online or blended learning, who provide learners with comprehensive guidance and support from start to finish. The significant difference between blended learning and online learning methods at SBTL is the Block Delivery of Online Live Sessions. Learners enrolled at SBTL on blended learning are offered a block delivery of online live sessions, which can be booked in advance on their convenience at additional cost. These live sessions are relevant to the learners' program of study and aim to enhance the student's comprehension of research, methodology and other essential study skills. We try to make these live sessions as communicating as possible by providing interactive activities and presentations. Resources and Support School of Business & Technology London is dedicated to offering excellent support on every step of your learning journey. School of Business & Technology London occupies a centralised tutor support desk portal. Our support team liaises with both tutors and learners to provide guidance, assessment feedback, and any other study support adequately and promptly. Once a learner raises a support request through the support desk portal (Be it for guidance, assessment feedback or any additional assistance), one of the support team members assign the relevant to request to an allocated tutor. As soon as the support receives a response from the allocated tutor, it will be made available to the learner in the portal. The support desk system is in place to assist the learners adequately and streamline all the support processes efficiently. Quality learning materials made by industry experts is a significant competitive edge of the School of Business & Technology London. Quality learning materials comprised of structured lecture notes, study guides, practical applications which includes real-world examples, and case studies that will enable you to apply your knowledge. Learning materials are provided in one of the three formats, such as PDF, PowerPoint, or Interactive Text Content on the learning portal. How does the Online Learning work at SBTL? We at SBTL follow a unique approach which differentiates us from other institutions. Indeed, we have taken distance education to a new phase where the support level is incredibly high.Now a days, convenience, flexibility and user-friendliness outweigh demands. Today, the transition from traditional classroom-based learning to online platforms is a significant result of these specifications. In this context, a crucial role played by online learning by leveraging the opportunities for convenience and easier access. It benefits the people who want to enhance their career, life and education in parallel streams. SBTL's simplified online learning facilitates an individual to progress towards the accomplishment of higher career growth without stress and dilemmas. How will you study online? With the School of Business & Technology London, you can study wherever you are. You finish your program with the utmost flexibility. You will be provided with comprehensive tutor support online through SBTL Support Desk portal. How will I get tutor support online? School of Business & Technology London occupies a centralised tutor support desk portal, through which our support team liaise with both tutors and learners to provide guidance, assessment feedback, and any other study support adequately and promptly. Once a learner raises a support request through the support desk portal (Be it for guidance, assessment feedback or any additional assistance), one of the support team members assign the relevant to request to an allocated tutor. As soon as the support receive a response from the allocated tutor, it will be made available to the learner in the portal. The support desk system is in place to assist the learners adequately and to streamline all the support process efficiently. Learners should expect to receive a response on queries like guidance and assistance within 1 - 2 working days. However, if the support request is for assessment feedback, learners will receive the reply with feedback as per the time frame outlined in the Assessment Feedback Policy.

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python experienced developers, analysts or others who are intending to learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based) and deploying them to the web. Overview Working in a hands-on lab environment led by our expert instructor, attendees will Understand the different kinds of recommender systems Master data-wrangling techniques using the pandas library Building an IMDB Top 250 Clone Build a content-based engine to recommend movies based on real movie metadata Employ data-mining techniques used in building recommenders Build industry-standard collaborative filters using powerful algorithms Building Hybrid Recommenders that incorporate content based and collaborative filtering Recommendation systems are at the heart of almost every internet business today; from Facebook to Net?ix to Amazon. Providing good recommendations, whether its friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform.This course shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory?you will get started with building and learning about recommenders as quickly as possible. In this course, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You will also use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques.Students will learn to build industry-standard recommender systems, leveraging basic Python syntax skills. This is an applied course, so machine learning theory is only used to highlight how to build recommenders in this course.This skills-focused ccombines engaging lecture, demos, group activities and discussions with machine-based student labs and exercises.. Our engaging instructors and mentors are highly-experienced practitioners who bring years of current, modern 'on-the-job' modern applied datascience, AI and machine learning experience into every classroom and hands-on project. Getting Started with Recommender Systems Technical requirements What is a recommender system? Types of recommender systems Manipulating Data with the Pandas Library Technical requirements Setting up the environment The Pandas library The Pandas DataFrame The Pandas Series Building an IMDB Top 250 Clone with Pandas Technical requirements The simple recommender The knowledge-based recommender Building Content-Based Recommenders Technical requirements Exporting the clean DataFrame Document vectors The cosine similarity score Plot description-based recommender Metadata-based recommender Suggestions for improvements Getting Started with Data Mining Techniques Problem statement Similarity measures Clustering Dimensionality reduction Supervised learning Evaluation metrics Building Collaborative Filters Technical requirements The framework User-based collaborative filtering Item-based collaborative filtering Model-based approaches Hybrid Recommenders Technical requirements Introduction Case study and final project ? Building a hybrid model Additional course details: Nexus Humans Applied AI: Building Recommendation Systems with Python (TTAI2360) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Applied AI: Building Recommendation Systems with Python (TTAI2360) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.