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Are you intrigued by how speech recognition is driving the growth of the AI market? This course is a reliable guide if you're looking to pursue a career as a speech recognition professional and understand industry best practices.

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About this training course Artificial lift systems are an important part of production operations for the entire lifecycle of an asset. Often, oil and gas wells require artificial lift for most of the life cycle. This 5-day training course offers a thorough treatment of artificial lift techniques including design and operation for production optimization. With the increasing need to optimize dynamic production in highly constrained cost environments, opportunities and issues related to real-time measurements and optimization techniques needs to be discussed and understood. Artificial lift selection and life cycle analysis are covered. These concepts are discussed and reinforced using case studies, quizzing tools, and exercises with software. Participants solve examples and class problems throughout the course. Animations and videos reinforce the concepts under discussion. Understanding of these important production concepts is a must have to exploit the existing assets profitably. Unique Features: Hands-on usage of SNAP Software to solve gas-lift exercises Discussion on digital oil field Machine learning applications in gas-lift optimization Training Objectives After the completion of this training course, participants will be able to: Understand the basics and advanced concepts of each form of artificial lift systems including application envelope, relative strengths, and weaknesses Easily recognize the different components from downhole to the surface and their basic structural and operational features Design and analyze different components using appropriate software tools Understand challenges facing artificial lift applications and the mitigation of these challenges during selection, design, and operation Learn about the role of digital oilfield tools and techniques and their applications in artificial lift and production optimization Learn about use cases of Machine learning and artificial intelligence in the artificial lift Target Audience This training course is suitable and will greatly benefit the following specific groups: Production, reservoir, completion, drilling and facilities engineers, analysts, and operators Anyone interested in learning about selection, design, analysis and optimum operation of artificial lift and related production systems will benefit from this course. Course Level Intermediate Advanced Training Methods The training instructor relies on a highly interactive training method to enhance the learning process. This method ensures that all participants gain a complete understanding of all the topics covered. The training environment is highly stimulating, challenging, and effective because the participants will learn by case studies which will allow them to apply the material taught in their own organization. Course Duration: 5 days in total (35 hours). Training Schedule 0830 - Registration 0900 - Start of training 1030 - Morning Break 1045 - Training recommences 1230 - Lunch Break 1330 - Training recommences 1515 - Evening break 1530 - Training recommences 1700 - End of Training The maximum number of participants allowed for this training course is 20. This course is also available through our Virtual Instructor Led Training (VILT) format. Prerequisites: Understanding of petroleum production concepts. Each participant needs a laptop/PC for solving class examples using software to be provided during class. Laptop/PC needs to have a current Windows operating system and at least 500 MB free disk space. Participants should have administrator rights to install software. Trainer Your expert course leader has over 35 years' work-experience in multiphase flow, artificial lift, real-time production optimization and software development/management. His current work is focused on a variety of use cases like failure prediction, virtual flow rate determination, wellhead integrity surveillance, corrosion, equipment maintenance, DTS/DAS interpretation. He has worked for national oil companies, majors, independents, and service providers globally. He has multiple patents and has delivered a multitude of industry presentations. Twice selected as an SPE distinguished lecturer, he also volunteers on SPE committees. He holds a Bachelor's and Master's in chemical engineering from the Gujarat University and IIT-Kanpur, India; and a Ph.D. in Petroleum Engineering from the University of Tulsa, USA. Highlighted Work Experience: At Weatherford, consulted with clients as well as directed teams on digital oilfield solutions including LOWIS - a solution that was underneath the production operations of Chevron and Occidental Petroleum across the globe. Worked with and consulted on equipment's like field controllers, VSDs, downhole permanent gauges, multiphase flow meters, fibre optics-based measurements. Shepherded an enterprise-class solution that is being deployed at a major oil and gas producer for production management including artificial lift optimization using real time data and deep-learning data analytics. Developed a workshop on digital oilfield approaches for production engineers. Patents: Principal inventor: 'Smarter Slug Flow Conditioning and Control' Co-inventor: 'Technique for Production Enhancement with Downhole Monitoring of Artificially Lifted Wells' Co-inventor: 'Wellbore real-time monitoring and analysis of fracture contribution' Worldwide Experience in Training / Seminar / Workshop Deliveries: Besides delivering several SPE webinars, ALRDC and SPE trainings globally, he has taught artificial lift at Texas Tech, Missouri S&T, Louisiana State, U of Southern California, and U of Houston. He has conducted seminars, bespoke trainings / workshops globally for practicing professionals: Companies: Basra Oil Company, ConocoPhillips, Chevron, EcoPetrol, Equinor, KOC, ONGC, LukOil, PDO, PDVSA, PEMEX, Petronas, Repsol, , Saudi Aramco, Shell, Sonatrech, QP, Tatneft, YPF, and others. Countries: USA, Algeria, Argentina, Bahrain, Brazil, Canada, China, Croatia, Congo, Ghana, India, Indonesia, Iraq, Kazakhstan, Kenya, Kuwait, Libya, Malaysia, Oman, Mexico, Norway, Qatar, Romania, Russia, Serbia, Saudi Arabia, S Korea, Tanzania, Thailand, Tunisia, Turkmenistan, UAE, Ukraine, Uzbekistan, Venezuela. Virtual training provided for PetroEdge, ALRDC, School of Mines, Repsol, UEP-Pakistan, and others since pandemic. POST TRAINING COACHING SUPPORT (OPTIONAL) To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster. Request for further information post training support and fees applicable Accreditions And Affliations

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CPD 100 CPD hours / points Accredited by CPD Quality Standards Who is this course for? This course is tailored to suit a broad spectrum of learners, including: Aspiring Data Analysts who want to launch a new career in data analytics. Current analysts or professionals from similar fields looking to hone their Python skills and modernise their toolset. Business and IT students or recent graduates desiring a competitive edge in the job market. Career shifters interested in a more dynamic and high-paying field. Any individual who loves working with numbers, solving problems, and has a curiosity for uncovering insights in data. Requirements No experience required. Just enrol & start learning. Career path Data Analyst: £27,000 - £60,000 Senior Data Analyst: £40,000 - £75,000 Data Scientist: £50,000 - £100,000 Data Engineer: £45,000 - £90,000 Analytics Manager: £60,000 - £120,000 Director of Data Science: £90,000 - £150,000 Certificates CPD Accredited e-Certificate Digital certificate - Included CPD Accredited Framed (Hardcopy) Certificate Hard copy certificate - Included Enrolment Letter Digital certificate - Included QLS Endorsed Hard Copy Certificate Hard copy certificate - Included Student ID Card Digital certificate - Included

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Python for Data Analysis & Visualization Python for Machine Learning (ML) - scikit-learn - Linear Regression Model Python for Machine Learning - scikit-learn - Logistic Regression Model Python for Machine Learning - scikit-learn - K Nearest Neighbors Python for Machine Learning - scikit-learn - Decision Tree and Random Forests Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs) Python for Machine Learning - scikit-learn - K Means Clustering Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) Recommender Systems with Python - (Additional Topic) Python for Natural Language Processing (NLP) - NLTK - (Additional Topic) Course 07: Data Analysis in Excel Level 3 Course Modifying a Worksheet Working with Lists Analyzing Data Visualizing Data with Charts Using PivotTables and PivotCharts Working with Multiple Worksheets and Workbooks Using Lookup Functions and Formula Auditing Automating Workbook Functionality Creating Sparklines and Mapping Data Forecasting Data Course 08: Microsoft Excel: Automated Dashboard Using Advanced Formula, VBA, Power Query Introduction Prepaid Expenses Models: Resources Download Accounting for Prepaid Expenses Excel Formulas Detailed: Introduction to three Excel Models Formula Based Prepaid Expenses Model Calculate Prepaid Expenses Amortisation from Exact Start Date Prepaid Expenses Summary with Power Query and Pivot Table Advanced VBA Prepaid Expenses Amortisation Model BONUS: Dynamic Dashboard for Divisional Profit and Loss statements: Easy Way Power Query & Pivot Tables based Dashboard without any Formulas, Fully Dynamic Thankyou Course 09: GDPR Data Protection Level 5 Module 01: GDPR Basics Module 02: GDPR Explained Module 03: Lawful Basis for Preparation Module 04: Rights and Breaches Module 05: Responsibilities and Obligations Course 10: Level 3 Cyber Security FUNDAMENTALS OF NETWORKING GETTING STARTED WITH CYBER SECURITY LET'S BEGIN - 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Dive into the transformative world of Artificial Intelligence through the course titled 'Foundations of Artificial Intelligence: Building Intelligent Systems.' This comprehensive curriculum sweeps across an array of subjects, from the rudimentary introduction to AI to the intricate nuances of building AI applications. Embrace a holistic understanding of core modules like Machine Learning, Natural Language Processing, and Robotics. The content, framed meticulously, beckons those inquisitive minds eager to craft, innovate, and change the world with AI's limitless possibilities. Deepen your conceptual clarity with two-part modules that delve into Knowledge Representation and Machine Learning, ensuring that learners grasp intricate details without feeling overwhelmed. With sections dedicated to Computer Vision and Deep Learning, individuals will find themselves proficiently navigating the vibrant ecosystems these technologies encompass. 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Who is this Foundations of Artificial Intelligence: Building Intelligent Systems course for? This Foundations of Artificial Intelligence: Building Intelligent Systems does not require you to have any prior qualifications or experience. You can just enrol and start learning. Aspiring AI enthusiasts keen on building a robust foundation in the subject. Technologists aiming to pivot into AI-centric roles. Researchers eager to enhance their knowledge spectrum in intelligent systems. University students studying computer science or related disciplines, looking to supplement their academic pursuits. Entrepreneurs eyeing opportunities in AI-driven ventures. Prerequisites This Foundations of Artificial Intelligence: Building Intelligent Systems does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Foundations of Artificial Intelligence: Building Intelligent Systems 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 AI Research Scientist - Average Salary Range: £60,000 - £85,000 per annum Machine Learning Engineer - Average Salary Range: £55,000 - £80,000 per annum NLP Specialist - Average Salary Range: £50,000 - £75,000 per annum Computer Vision Engineer - Average Salary Range: £52,000 - £77,000 per annum Robotics Engineer - Average Salary Range: £48,000 - £73,000 per annum AI Application Developer - Average Salary Range: £54,000 - £79,000 per annum Course Curriculum Module 01: Introduction to Artificial Intelligence Introduction to Artificial Intelligence 00:21:00 Module 02: Mathematics for AI Mathematics for AI 00:17:00 Module 03: Knowledge Representation in AI - Part 1 Knowledge Representation in AI - Part 1 00:18:00 Module 04: Knowledge Representation in AI - Part 2 Knowledge Representation in AI - Part 2 00:16:00 Module 05: Machine Learning - Part 1 Machine Learning - Part 1 00:16:00 Module 06: Machine Learning - Part 2 Machine Learning - Part 2 00:15:00 Module 07: Deep Learning Deep Learning 00:16:00 Module 08: Natural Language Processing Natural Language Processing 00:22:00 Module 09: Computer Vision Computer Vision 00:14:00 Module 10: Robotics Robotics 00:18:00 Module 11: Building AI Applications Building AI Applications 00:24:00

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