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Recognised Accreditation This course is accredited by continuing professional development (CPD). CPD UK is globally recognised by employers, professional organisations, and academic institutions, thus a certificate from CPD Certification Service creates value towards your professional goal and achievement. The Quality Licence Scheme is a brand of the Skills and Education Group, a leading national awarding organisation for providing high-quality vocational qualifications across a wide range of industries. What is CPD? Employers, professional organisations, and academic institutions all recognise CPD, therefore a credential from CPD Certification Service adds value to your professional goals and achievements. Benefits of CPD Improve your employment prospects Boost your job satisfaction Promotes career advancement Enhances your CV Provides you with a competitive edge in the job market Demonstrate your dedication Showcases your professional capabilities What is IPHM? The IPHM is an Accreditation Board that provides Training Providers with international and global accreditation. The Practitioners of Holistic Medicine (IPHM) accreditation is a guarantee of quality and skill. Benefits of IPHM It will help you establish a positive reputation in your chosen field You can join a network and community of successful therapists that are dedicated to providing excellent care to their client You can flaunt this accreditation in your CV It is a worldwide recognised accreditation What is Quality Licence Scheme? This course is endorsed by the Quality Licence Scheme for its high-quality, non-regulated provision and training programmes. The Quality Licence Scheme is a brand of the Skills and Education Group, a leading national awarding organisation for providing high-quality vocational qualifications across a wide range of industries. Benefits of Quality License Scheme Certificate is valuable Provides a competitive edge in your career It will make your CV stand out Course Curriculum Welcome to the course Introduction 00:02:00 Setting up R Studio and R crash course Installing R and R studio 00:05:00 Basics of R and R studio 00:10:00 Packages in R 00:10:00 Inputting data part 1: Inbuilt datasets of R 00:04:00 Inputting data part 2: Manual data entry 00:03:00 Inputting data part 3: Importing from CSV or Text files 00:06:00 Creating Barplots in R 00:13:00 Creating Histograms in R 00:06:00 Basics of Statistics Types of Data 00:04:00 Types of Statistics 00:02:00 Describing the data graphically 00:11:00 Measures of Centers 00:07:00 Measures of Dispersion 00:04:00 Introduction to Machine Learning Introduction to Machine Learning 00:16:00 Building a Machine Learning Model 00:08:00 Data Preprocessing for Regression Analysis Gathering Business Knowledge 00:03:00 Data Exploration 00:03:00 The Data and the Data Dictionary 00:07:00 Importing the dataset into R 00:03:00 Univariate Analysis and EDD 00:03:00 EDD in R 00:12:00 Outlier Treatment 00:04:00 Outlier Treatment in R 00:04:00 Missing Value imputation 00:03:00 Missing Value imputation in R 00:03:00 Seasonality in Data 00:03:00 Bi-variate Analysis and Variable Transformation 00:16:00 Variable transformation in R 00:09:00 Non Usable Variables 00:04:00 Dummy variable creation: Handling qualitative data 00:04:00 Dummy variable creation in R 00:05:00 Correlation Matrix and cause-effect relationship 00:10:00 Correlation Matrix in R 00:08:00 Linear Regression Model The problem statement 00:01:00 Basic equations and Ordinary Least Squared (OLS) method 00:08:00 Assessing Accuracy of predicted coefficients 00:14:00 Assessing Model Accuracy - RSE and R squared 00:07:00 Simple Linear Regression in R 00:07:00 Multiple Linear Regression 00:05:00 The F - statistic 00:08:00 Interpreting result for categorical Variable 00:05:00 Multiple Linear Regression in R 00:07:00 Test-Train split 00:09:00 Bias Variance trade-off 00:06:00 Test-Train Split in R 00:08:00 Regression models other than OLS Linear models other than OLS 00:04:00 Subset Selection techniques 00:11:00 Subset selection in R 00:07:00 Shrinkage methods - Ridge Regression and The Lasso 00:07:00 Ridge regression and Lasso in R 00:12:00 Classification Models: Data Preparation The Data and the Data Dictionary 00:08:00 Importing the dataset into R 00:03:00 EDD in R 00:11:00 Outlier Treatment in R 00:04:00 Missing Value imputation in R 00:03:00 Variable transformation in R 00:06:00 Dummy variable creation in R 00:05:00 The Three classification models Three Classifiers and the problem statement 00:03:00 Why can't we use Linear Regression? 00:04:00 Logistic Regression Logistic Regression 00:08:00 Training a Simple Logistic model in R 00:03:00 Results of Simple Logistic Regression 00:05:00 Logistic with multiple predictors 00:02:00 Training multiple predictor Logistic model in R 00:01:00 Confusion Matrix 00:03:00 Evaluating Model performance 00:07:00 Predicting probabilities, assigning classes and making Confusion Matrix in R 00:06:00 Linear Discriminant Analysis Linear Discriminant Analysis 00:09:00 Linear Discriminant Analysis in R 00:09:00 K-Nearest Neighbors Test-Train Split 00:09:00 Test-Train Split in R 00:08:00 K-Nearest Neighbors classifier 00:08:00 K-Nearest Neighbors in R 00:08:00 Comparing results from 3 models Understanding the results of classification models 00:06:00 Summary of the three models 00:04:00 Simple Decision Trees Basics of Decision Trees 00:10:00 Understanding a Regression Tree 00:10:00 The stopping criteria for controlling tree growth 00:03:00 The Data set for this part 00:03:00 Importing the Data set into R 00:06:00 Splitting Data into Test and Train Set in R 00:05:00 Building a Regression Tree in R 00:14:00 Pruning a tree 00:04:00 Pruning a Tree in R 00:09:00 Simple Classification Tree Classification Trees 00:06:00 The Data set for Classification problem 00:01:00 Building a classification Tree in R 00:09:00 Advantages and Disadvantages of Decision Trees 00:01:00 Ensemble technique 1 - Bagging Bagging 00:06:00 Bagging in R 00:06:00 Ensemble technique 2 - Random Forest Random Forest technique 00:04:00 Random Forest in R 00:04:00 Ensemble technique 3 - GBM, AdaBoost and XGBoost Boosting techniques 00:07:00 Gradient Boosting in R 00:07:00 AdaBoosting in R 00:09:00 XGBoosting in R 00:16:00 Maximum Margin Classifier Content flow 00:01:00 The Concept of a Hyperplane 00:05:00 Maximum Margin Classifier 00:03:00 Limitations of Maximum Margin Classifier 00:02:00 Support Vector Classifier Support Vector classifiers 00:10:00 Limitations of Support Vector Classifiers 00:01:00 Support Vector Machines Kernel Based Support Vector Machines 00:06:00 Creating Support Vector Machine Model in R The Data set for the Classification problem 00:01:00 Importing Data into R 00:08:00 Test-Train Split 00:09:00 Classification SVM model using Linear Kernel 00:16:00 Hyperparameter Tuning for Linear Kernel 00:06:00 Polynomial Kernel with Hyperparameter Tuning 00:10:00 Radial Kernel with Hyperparameter Tuning 00:06:00 The Data set for the Regression problem 00:03:00 SVM based Regression Model in R 00:11:00 Assessment Assessment - Machine Learning Masterclass 00:10:00 Certificate of Achievement Certificate of Achievement 00:00:00 Get Your Insurance Now Get Your Insurance Now 00:00:00 Feedback Feedback 00:00:00

24 Hours Left! Don't Let the Winter Deals Slip Away - Enrol Now! Are you passionate about plants and flowers? Do you want to develop a deeper understanding of horticulture & floristry? Look no further than our comprehensive Horticulture & Floristry course! Our Horticulture & Floristry course covers everything from basic botany to advanced plant propagation techniques. You will gain a solid understanding of soil science, plant nutrition, and pest control. You will also learn about plant selection, native and exotic plants, indoor plants, lawns, and landscaping. In addition, our professional floristry course covers everything from flower colours and symbolism to floral design, wedding bouquets, and funeral flowers. By the end of our Horticulture & Floristry course, you will have a comprehensive understanding of horticulture & floristry, and you will be able to apply your knowledge in a variety of settings. Whether you are looking to start a career in the horticulture or floristry industry, or you simply want to develop your knowledge for personal use, our Horticulture & Floristry course is the perfect choice for you. After this Horticulture & Floristry course, you will be able to learn: Understand the basic principles of plant nutrition. Demonstrate an understanding of plant propagation techniques. Identify common plant pests and diseases and know how to control them. Understand the principles of garden design and landscaping. Understand the significance of flower colours and symbolism. Develop skills in floral design and arrangement. Understand the different types of floral arrangements for weddings and funerals. Why Prefer This Horticulture & Floristry Course? Opportunity to earn a certificate accredited by CPDQS. Get a free student ID card! (£10 postal charge will be applicable for international delivery) Innovative and Engaging Content. Free Assessments 24/7 Tutor Support. Take a step toward a brighter future! *** Course Curriculum *** Here is the curriculum breakdown of the Horticulture & Floristry course: Horticulture Module 01: Basic Botany Module 02: Plant Classification Module 03: Soil Science Module 04: Plant Nutrition Requirements Module 05: Plant Pests and Diseases Module 06: Basic Concepts of Organic Gardening Module 07: Weed Control Module 08: Plant Propagation Module 09: Amenity Horticulture, Plant Selection, and Native Plants Module 10: Exotic Plants Module 11: Indoor Plants Module 12: Lawns Module 13: Planting and Plant Care Module 14: Plant Nodes and Indigenous Plants Module 15: Plant Protection, Landscaping, and Garden Design Module 16: Ornamental Horticulture Module 17: Permaculture Module 18: Arboriculture Module 19: Horticulture Statistics Module 20: Results from the 2018 Seasonal Labour in Horticulture End of Year Professional Floristry Course Module 01: Introduction to Floristry Module 02: The Florist Module 03: Flower Colors & Symbolism Module 04: Different Flowers and Their Meaning Module 05: Potted Plant Care Module 06: Cut Flowers Module 07: Plant Diseases Module 08: Common Cultural Disorders Module 09: Insects and Pests of Roses Module 10: Garden Care to Prevent Diseases Module 11: Principles of Floral Arrangement Module 12: Floral Design Module 13: Types of Greenery Module 14: Role of Foliage in Arrangements Module 15: Popular Styles and Arrangements Module 16: Making Floral Arrangements Module 17: Container Preparation Module 18: Corsage & Boutonniere Module 19: Wedding Bouquet Module 20: Funeral Flowers & Meanings Module 21: Funeral Wreath Module 22: Surviving in Florist Sector Assessment Process Your skills and knowledge will be tested with an automated multiple-choice assessment. You will then receive instant results to let you know if you have successfully passed the Horticulture & Floristry course. CPD 20 CPD hours / points Accredited by CPD Quality Standards Who is this course for? This Horticulture & Floristry course is ideal for: Individuals looking to start a career in horticulture or floristry. Gardeners looking to improve their knowledge and skills. Homeowners looking to improve their gardening and landscaping abilities. Requirements You will not need any prior background or expertise to enrol in this Horticulture & Floristry course. Career path After completing this Horticulture & Floristry course, you are to start your career or begin the next phase of your career. Like as: Horticultural Technician - Avg. Salary: £20,000 Arboricultural Officer - Avg. Salary: £24,000 Florist - Avg. Salary: £18,000 Landscape Gardener - Avg. Salary: £22,000 Nursery Worker - Avg. Salary: £17,000 Grounds Maintenance Worker - Avg. Salary: £19,000 Certificates CPDQS Accredited Certificate Digital certificate - £10 CPDQS Accredited Certificate Hard copy certificate - £29 If you are an international student, then you have to pay an additional 10 GBP as an international delivery charge.

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Duration 3 Days 18 CPD hours This course is intended for This course is aimed at anyone who wants to harness the power of data analytics in their organization. Overview After completing this course delegates will be capable of writing effective R code to manipulate, analyse and visualise data to enable their organisations make better, data-driven decisions. This course teaches delegates with no prior programming or data analytics experience how to perform data manipulation, data analysis and data visualisation in R. Course Outline Becoming a world class data analytics practitioner requires mastery of the most sophisticated data analytics tools. The R programming language is one of the most powerful and flexible tools in the data analytics toolkit. This course teaches delegates with no prior programming or data analytics experience how to perform data manipulation, data analysis and data visualisation in R. Mastery of these techniques will allow delegates to immediately add value in their work place by extracting valuable insight from company data to allow better, data-driven decisions. The course will explore the following topics through a series of interactive workshop sessions: What is R? Basic R programming conventions Data structures in R Accessing data in R Descriptive statistics in R Statistical analysis in R Data manipulation in R Data visualisation in R Additional course details: Nexus Humans Beginning Data Analytics With R training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Beginning Data Analytics With R course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

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In this self-paced course, you will learn how to use TensorFlow 2 to build recurrent neural networks (RNNs). You will learn about sequence data, forecasting, Elman Unit, GRU, and LSTM. You will also learn how to work with image classification and how to get stock return predictions using LSTMs. We will also cover Natural Language Processing (NLP) and learn about text preprocessing and classification.

This one-day course focuses on issues such as writing formulas and accessing help while writing them, and taking formulas to the next level by nesting one inside another for a powerful formula result. It also looks at ways of analysing data with reports, summarised by varying criteria. A range of time-saving tips and tricks are shared. This course will help participants: Calculate with absolute reference Group worksheets Link to tables Use the function library effectively Get to grips with the logical IF function Use conditional formatting Create pivot table reports Use data validation Master the VLOOKUP function 1 Calculating with absolute reference The difference between a relative and absolute formula Changing a relative formula to an absolute Using $ signs to lock cells when copying formulas 2 Grouping worksheets Grouping sheets together Inputting data into multiple sheets Writing a 3D formula to sum tables across sheets 3 Linking to tables Linking to a source table Using paste link to link a table to another file Using edit links to manage linked tables 4 The function library Benefits of writing formulas in the function library Finding the right formula using insert function Outputting statistics with COUNTA and COUNTBLANK Counting criteria in a list with COUNTIFS 5 Logical IF Function Outputting results from tests Running multiple tests for multiple results The concept of outputting results from numbers 6 Conditional formatting Enabling text and numbers to standout Applying colour to data using rules Managing rules Copying rules with the format painter 7 View side by side Comparing two Excel tables together Comparing two sheets together in the same file 8 Pivot table reports Analysing data with pivot tables Managing a pivot table's layout Outputting statistical reports Controlling number formats Visualising reports with pivot charts Inserting slicers for filtering data 9 Data validation Restricting data input with data validation Speeding up data entry with data validation 10 VLOOKUP function Best practices for writing a VLOOKUP A false type lookup A true type lookup Enhance formula results with IFNA 11 Print options Getting the most from print Printing page titles across pages Scaling content for print