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ECS Health & Safety Course with MJ Electrical Training. Available every week, use this course to complete the application process when applying for an ECS Labourers Card.

ECS Health & Safety Course with MJ Electrical Training. Available every week, use this course to complete the application process when applying for an ECS Labourers Card.

ECS Health & Safety Course with MJ Electrical Training. Available every week, use this course to complete the application process when applying for an ECS Labourers Card.

ECS Health & Safety Course with MJ Electrical Training. Available every week, use this course to complete the application process when applying for an ECS Labourers Card.

ECS Health & Safety Course with MJ Electrical Training. Available every week, use this course to complete the application process when applying for an ECS Labourers Card.

Duration 2 Days 12 CPD hours This course is intended for Implementation Consultants Key Technical Business Users involved in a GRC 12.0 project IT Governance Experts Consultants for SAP Security and GRC IT Auditors Business Project Team Leaders Overview This course will prepare you to: Introduce SAP Governance, Risk, and Compliance (GRC) 12.0 Identify key governance, risk, and compliance processes supported in the SAP GRC 12.0 solution Describe key features and business benefits of the integrated solution Identify applications that integrate with the SAP GRC 12.0 solution Describe the purpose and location of key user interface components Discuss harmonized navigation and how authorizations affect what users see Describe how common functions and relative master data are shared across the SAP GRC solutions Describe the IMG organization for GRC 12.0 Describe a general implementation process and key steps This course provides foundation knowledge for SAP Governance, Risk and Compliance (GRC) 12.0. By gaining familiarity with this solution?s harmonized navigation, common functions, shared master data, and the harmonized reporting framework, you will be better able to get the most value out of using the integrated applications in SAP GRC. Introduction to SAP Governance, Risk, and compliance (GRC) 12.0 Solution Introduction Solution Overview ? Enterprise Risk and Compliance Solution Overview ? Access Governance Solution Overview ? International Trade Management Solution Overview ? Cybersecurity Information Architecture, Security, and Authorizations Understanding the SAP Fiori Concept and Architecture Security and Authorizations The SAP GRC 12.0 User Interface Work Centers SAP Fiori Common Functions and Data User Interface Configuration Framework Shared Master Data Implementation and Configuration Streamlined Configuration Reporting Harmonized Reporting Framework SAP HANA Integration

Explore China’s growing influence in Africa through this in-depth course. Weekly themes blend history, trends, and analysis to unpack the economic, political, and social layers of this evolving relationship. Gain a nuanced view of its impact on Africa’s global role

Explore China’s growing influence in Africa through this in-depth course. Weekly themes blend history, trends, and analysis to unpack the economic, political, and social layers of this evolving relationship. Gain a nuanced view of its impact on Africa’s global role

Working with trauma requires advanced knowledge and skills beyond basic tapping training. While tapping produces amazing results, trauma requires a higher level of training in order to be both safe and effective.

Duration 2 Days 12 CPD hours This course is intended for If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful. Overview By the end of this course, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from data. This course is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You?ll discover how to tune the algorithms to provide the best predictions on new and unseen data. As you delve into later sections, you?ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. Data Exploration and Cleaning Python and the Anaconda Package Management System Different Types of Data Science Problems Loading the Case Study Data with Jupyter and pandas Data Quality Assurance and Exploration Exploring the Financial History Features in the Dataset Activity 1: Exploring Remaining Financial Features in the Dataset Introduction to Scikit-Learn and Model Evaluation Introduction Model Performance Metrics for Binary Classification Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve Details of Logistic Regression and Feature Exploration Introduction Examining the Relationships between Features and the Response Univariate Feature Selection: What It Does and Doesn't Do Building Cloud-Native Applications Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients The Bias-Variance Trade-off Introduction Estimating the Coefficients and Intercepts of Logistic Regression Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters Activity 4: Cross-Validation and Feature Engineering with the Case Study Data Decision Trees and Random Forests Introduction Decision trees Random Forests: Ensembles of Decision Trees Activity 5: Cross-Validation Grid Search with Random Forest Imputation of Missing Data, Financial Analysis, and Delivery to Client Introduction Review of Modeling Results Dealing with Missing Data: Imputation Strategies Activity 6: Deriving Financial Insights Final Thoughts on Delivering the Predictive Model to the Client