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This qualification is designed for Water Network Field Managers, Operational Managers, Project Managers, Department Heads, and other practising Senior and Middle Managers. The qualification will develop your technical skills and understanding of the Management of demand Management & Leakage and improve your performance. It will encourage strategic thinking at this level of management to foster business improvement. Main themes Understand the role demand management plays in the water industry sustainability plan Understand the range of demand management tools available and their effectiveness Understand the application of leakage strategies, the economic value and the external influences Understand data handling, meaningful leakage calculations and the interpretation of leakage data Understand various leakage control methods used and their respective effectiveness and economic cost-benefit Understand network repair and maintenance methods and the provision of these services Instructions This online course will be delivered using CISCO Webex. You will need a suitable device with a camera and microphone like a laptop. If you have any questions about what is required, please contact us on enquiries@learninganddevelopment.associates. Qualification All learners taking the qualification will need to produce identification to enable ProQual registration. This can take the form of a: Passport Driving Licence Construction Skills Certification Scheme Card (CSCS Card) Construction Plant Competence Scheme Card (CPCS Card) If none of the above are available to the learner, a photograph with a statement confirming their identity, dated and signed, along with a utility bill, would be acceptable.

This Python Machine Learning online instructor led course is an excellent introduction to popular machine learning algorithms. Python Machine Learning 2-day Course Prerequisites: Basic knowledge of Python coding is a pre-requisite. Who Should Attend? This course is an overview of machine learning and machine learning algorithms in Python SciKitLearn. Practical: We cover the below listed algorithms, which is only a small collection of what is available. However, it will give you a good understanding, to plan your Machine Learning project We create, experiment and run machine learning sample code to implement a short selected but representative list of available the algorithms. Course Outline: Supervised Machine Learning: Classification Algorithms: Naive Bayes, Decision Tree, Logistic Regression, K-Nearest Neighbors, Support Vector Machine Regression Algorithms: Linear, Polynomial Unsupervised Machine Learning: Clustering Algorithms: K-means clustering, Hierarchical Clustering Dimension Reduction Algorithms: Principal Component Analysis Latent Dirichlet allocation (LDA) Association Machine Learning Algorithms: Apriori, Euclat Other machine learning Algorithms: Ensemble Methods ( Stacking, bagging, boosting ) Algorithms: Random Forest, Gradient Boosting Reinforcement learning Algorithms: Q-Learning Neural Networks and Deep Leaning Algorithms: Convolutional Network (CNN) Data Exploration and Preprocessing: The first part of a Machine Learning project understands the data and the problem at hand. Data cleaning, data transformation and data pre-processing are covered using Python functions to make data exploration and preprocessing relatively easy. What is included in this Python Machine Learning: Python Machine Learning Certificate on completion Python Machine Learning notes Practical Python Machine Learning exercises and code examples After the course, 1 free, online session for questions or revision Python Machine Learning. Max group size on this Python Machine Learning is 4. Refund Policy No Refunds

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Get involved in a learning adventure, mastering R from foundational basics to advanced techniques. This course is a gateway to the realm of data science. Explore statistical machine learning models and intricacies of deep learning and create interactive Shiny apps. Unleash the power of R and elevate your proficiency in data-driven decision-making.