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Gain expertise in managing digitalized oil and gas assets with EnergyEdge's course, focusing on practical applications of digital transformation.

Duration 5 Days 30 CPD hours This course is intended for The primary audience for this course is any IT, facilities or data centre professional who works in and around the data centre and has the responsibility to achieve and improve efficiency and environmental sustainability, whilst maintaining the availability and manageability of the data centre. Overview After completion of the course the participant will be able to: Understand the impact of data centres on the environment Describe the various environmental/energy management standards Understand the purpose and goals of the legally binding international treaties on climate change Implement various sustainable performance metrics and how to use them in the data centre environment Manage data centre environmental sustainability using international standards Set up the measurement, monitoring and reporting of energy usage Use power efficiency indicators in a variety of data centre designs Use best practices for energy savings in the electrical infrastructure and in the mechanical (cooling) infrastructure Use best practices for energy savings for the ICT equipment and data storage Understand the importance of water management and waste management Understand the different ways to use sustainable energy in the data centre Get practical tips and innovative ideas to make a data centre more sustainable The CDESS© course is aimed at providing knowledge of the standards and guidelines related to environmental sustainability, and how to move your data centre (existing or new) to a more environmentally sustainable design and operations. Impact of Data Centres on the Environment Predictions in 2010 Current situation Outlook and commitments What is Environmental Sustainability The importance of sustainability Senior management commitment Environmental sustainability framework Sustainability policies Performance standards and metrics Information policies Transparency Awareness Service charging models Environmental Management Environmental sustainability framework (ISO 14001) Standards and guidelines ? ISO 50001 / ISO 30134 Measurement and categories Baselining Trend analysis Reporting Power Effiðciency Indicators Various eðfficiency indicators Power Usage Effectiveness (PUE) PUE measurement levels Factors affecting PUE Measurement points and intervals PUE in mixed source environments Measuring PUE in a mixed-use building PUE reporting Impact of PUE after optimising IT load Electrical Energy Savings (Electrical) Identifying the starting point for saving energy Sizing of power DC power Generators UPS systems Power Factor (PF) Energy savings on lighting Electrical Energy Savings (Mechanical) Energy savings on the cooling infrastructure Temperature and humidity setpoints Various energy eðcient cooling technologies Energy savings on the airflow Liquid cooling Energy reusage PUE, ERE/ERF and Control Volume Electrical Energy Savings (ICT) Procurement IT equipment energy eðfficiency ITEEsv, SMPE, SMPO IT equipment utilisation Server virtualisation Open compute project Electrical Energy Savings (Data Storage) Data management Data storage management Data storage equipment effiðciency Water Management Water Usage Effectiveness (WUE) Improving WUE Water usage at the power generation source Energy Water Intensity Factor (EWIF) Waste Management Waste management policies Life-cycle assessment (Cradle to the grave) 3 R?s for waste management Reduce Reuse Second-hand market Recycle Sustainable Energy Usage Sustainable energy sources Power purchase agreements Energy attribute certificates Renewable Energy Factor (REF) Matching renewable energy supply and demand Sustainable energy storage Carbon trading Automated Environmental Management Systems Use of AI and machine learning Load migration Data Centre Infrastructure Management (DCIM) solutions

Python Machine Learning algorithms can derive trends (learn) from data and make predictions on data by extrapolating on existing trends. Companies can take advantage of this to gain insights and ultimately improve business. Using Python Machine Learning scikit-learn, practice how to use Python Machine Learning algorithms to perform predictions on data. Learn the below listed algorithms, a small collection of available Python Machine Learning algorithms.

Python Data Analytics boot camp. 12 1 day lessons, learn Python Basics through to machine learning and front-ends. With practical project to give you full confidence and credibility.

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

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

🚀 Unlock the Power of Data with Our Machine Learning Course! 🤖 Are you ready to dive into the revolutionary world of Machine Learning? Welcome to our comprehensive course designed to equip you with the skills and knowledge needed to harness the potential of data-driven decision-making. 🎓 Machine Learning has rapidly emerged as one of the most transformative technologies of the 21st century. From powering intelligent virtual assistants to revolutionizing healthcare diagnostics, its applications are boundless. With our expertly crafted course, you'll embark on a journey that will demystify the complexities of Machine Learning and empower you to leverage its capabilities for diverse purposes. 💡 Why Machine Learning? In today's data-driven world, organizations across industries are seeking professionals who can extract actionable insights from vast amounts of data. Machine Learning offers the tools and techniques necessary to analyze complex datasets, identify patterns, and make predictions with unprecedented accuracy. By mastering Machine Learning, you'll gain a competitive edge in the job market and position yourself as a valuable asset to any organization. 📈 What You'll Learn: Our Machine Learning course covers a wide array of topics, including: Fundamentals of Machine Learning algorithms Supervised, unsupervised, and reinforcement learning techniques Data preprocessing and feature engineering Model evaluation and validation Deep learning and neural networks Practical applications and case studies With hands-on projects and real-world examples, you'll not only understand the theory behind Machine Learning but also gain practical experience in implementing algorithms and solving complex problems. Whether you're a beginner or an experienced data professional, our course is tailored to accommodate learners of all levels. 📊 Who is this for? Our Machine Learning course is ideal for: Aspiring data scientists and analysts Software engineers looking to transition into Machine Learning roles Business professionals seeking to leverage data for strategic decision-making Students and academics interested in exploring the forefront of technology No matter your background or experience level, our course provides a solid foundation in Machine Learning principles and techniques, setting you on the path to success in this rapidly evolving field. 🌟 Career Path: By mastering Machine Learning, you'll open doors to a myriad of exciting career opportunities, including: Data Scientist Machine Learning Engineer AI Researcher Business Intelligence Analyst Data Engineer With the demand for Machine Learning professionals on the rise, employers are actively seeking individuals with the skills and expertise to drive innovation and deliver impactful solutions. Whether you're looking to advance your current career or embark on a new professional journey, our course will equip you with the tools and knowledge needed to thrive in today's competitive job market. 💼 FAQ: Q: Is prior programming experience required to enroll in the course? A: While prior programming experience can be beneficial, our course is designed to accommodate learners of all backgrounds. We provide comprehensive tutorials and resources to help you grasp the fundamentals of programming and get started with Machine Learning. Q: How long does it take to complete the course? A: The duration of the course varies depending on your pace and level of commitment. On average, most learners complete the course within 3 to 6 months. However, you have the flexibility to study at your own pace and revisit materials as needed. Q: Are there any prerequisites for enrolling in the course? A: While there are no strict prerequisites, familiarity with basic mathematics, statistics, and programming concepts can be advantageous. We provide supplementary materials and support to help you build the necessary foundation for success in the course. Q: Will I receive a certificate upon completion of the course? A: Yes, upon successfully completing the course requirements, you'll receive a certificate of completion that validates your proficiency in Machine Learning concepts and techniques. This certificate can enhance your credentials and demonstrate your expertise to potential employers. Q: How does the course structure accommodate working professionals? A: Our course offers flexible scheduling options, allowing you to balance your studies with your professional and personal commitments. With on-demand access to course materials and resources, you can learn at your own convenience and progress at a pace that suits your lifestyle. Don't miss out on the opportunity to unlock your full potential with our Machine Learning course! Enroll today and embark on a transformative journey that will shape the future of your career. 🌐✨ Course Curriculum Module 1_ Introduction to Machine Learning Introduction to Machine Learning 00:00 Module 2_ Linear Regression Linear Regression 00:00 Module 3_ Logistic Regression Logistic Regression 00:00 Module 4_ Decision Trees and Random Forests Decision Trees and Random Forests 00:00 Module 5_ Support Vector Machines (SVMs) Support Vector Machines (SVMs) 00:00 Module 6_ k-Nearest Neighbors (k-NN) k-Nearest Neighbors (k-NN) 00:00 Module 7_ Naive Bayes Naive Bayes 00:00 Module 8_ Clustering Clustering 00:00 Module 9_ Dimensionality Reduction Dimensionality Reduction 00:00 Module 10_ Neural Networks Neural Networks 00:00

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

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

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