Duration 1 Days 6 CPD hours This course is intended for To ensure success, students should have completed Excel Essentials and Excel Functions Including Pivot Tables and Lookups or have the equivalent knowledge and experience. Overview Upon successful completion of this course, students will be able to enhance productivity and efficiency by streamlining workflow, collaborate with others, and audit and analyse data. This course is designed for students desiring to gain skills necessary to create macros, collaborate with others, audit and analyse data, incorporate multiple data sources, and import data. Working with Multiple Worksheets and Workbooks Working with Named Ranges Link Cells Across Worksheets and Workbooks Use 3D References to Calculate Across Worksheets Consolidate Data Use Formula Auditing and Error Checking Reveal Formulas Trace Cell Precedents and Dependents Locate Errors in Formulas Watch and Evaluate Formulas Reviewing and Protecting Workbooks Control Data Entry via Data Validation Protect Workbook Access Protect Worksheets and Cell Content Add and Edit Comments Prepare a Workbook for Distribution Modify Excel's Default Settings Using Macros to Automate Workbook Functionality Create Macros via Recording Run Macros via Buttons and Shortcuts Assign Macros to the Quick Access Toolbar and Ribbon Assign Macros to Objects View Macro Code Forecasting and Analysis Data Use Conditional Formatting to Highlight, Sort and Filter Key Data Advanced Conditional Formatting using Formulas Create Sparklines to Visualise Data Add Trendlines to Charts to Visualise and Forecast Trends Use Data Tables and Scenarios to Project Potential Outcomes Use Goal Seek to Calculate Outcomes Forecast Data Trends Using Solver
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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
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.
Data Visualization Courses London. In this Power BI Course, you will learn how to translate data trends, summaries, statistics and insights from your data into powerful and inspirational visualizations This course is ideal for managers and data analysts who need to make business decisions based on data.
his course covers the essential Python Basics, in our interactive, instructor led Live Virtual Classroom. This Python Basics course is a very good introduction to essential fundamental programming concepts using Python as programming language. These concepts are daily used by programmers and is your first step to working as a programmer. By the end, you'll be comfortable in programming Python code. You will have done small projects. This will serve for you as examples and samples that you can use to build larger projects.
Access Introduction generally navigate through Access Database, get data fast, update data records by entering, update data and delete data. Run available report, use available forms, create simple forms and reports via wizards. This style tuition helps to maximise the value that you get from the day.