508 Courses

Instructor training enables companies to have their own AITT Forklift Instructor. On completion they are able to instruct and examine operators on company premises. Also it could be an opportunity for an individual to embark upon a rewarding career as a recognised AITT instructor. The course complies with the approved code of practice issued by the Health & Safety Executive. We offer the AITT Instructor training course at our training centre in Warrington, Cheshire. We use all the best tools and equipment to assist trainee development. Our Instructor’s Mike Hammett and Stephen McCann have a lot of experience in this course, both have very good success rates and offer alot of after care too! Once an Instructor has passed their AITT Instructor training course they can always come back and receive professional advice. We always go the extra mile! AITT Accredited Novice Course: The Instructor training course caters for candidates seeking to become an AITT Registered Instructor. Previous fork lift experience is strongly recommended and candidates must have a current counterbalance certificate dated within 36 months prior to the course start date. Refresher courses are available prior to the instructor course extending the duration by one day to 11 days. Objectives: On successful completion of the course the candidate will be qualified to teach and train on all Industrial Counterbalance and Reach trucks for which they are certificated to use as operators. Target Group: The employer should carefully select the correct person for the job as an instructor. They should be literate and numerate with good presentation skills. The AITT recommend that candidates have a minimum of 12 months operating experience before attending the course. During the course candidates will be progressively assessed in all key areas. Candidates therefore must have a good knowledge of each subject and are provided with some excellent materials to assist them on completion of the course. AITT Instructor Training Course Duration: 3 or 5 days for Re-qualification or Re-Registration courses. 5 days for Assimilation Courses. 10-12 days for the Novice AITT instructor training course. Contents: Principles of instruction. Instructional techniques. HASAWA 1974/PUWER 1998/LOLER 1998/L117. Setting up courses. Administering the tests etc. All original documentation supplied by examining body and HSE. Prices are available on request and should you require any further information please do not hesitate to contact us. We also offer In-House Instructor training to suit companies needs and these are of five day durations, please contact for further details. Please feel free to download our Course Syllabus’s below and decide which course best meets your needs. See Mike at work demonstrating a lesson of De-stacking from High Level. In-House Courses: These courses are aimed at companies wishing to use their own Instructors to train staff. IN-HOUSE BASIC INSTRUCTOR COURSE PDF AITT Instructor Training Courses: On completion of these courses candidates will be registered as an AITT Instructor and be able to train on anything they are currently qualified to operate. Courses vary depending on experience and current qualifications so please have a look at the following courses to see which suits best. More information is available at www.aitt.co.uk.

This course comes bundled with basic to advanced level knowledge on Spring and Spring Boot with 200+ questions and answers. With REST API, JPA, Hibernate, and Spring Data JPA, you have the upper hand in your interviews for sure.

Introduction to 'Early Years Foundation Stage' Approved Online Training

Workplace Emergency First Aid 'Refresher' Approved Online Training for Irish Legislation

Watch a film, don't read a powerpoint! Our Food Safety Level 2 course is an interactive and engaging online learning experience. Developed by industry experts, it is designed to meet the needs of individuals who work with food or handle food-related activities. The online course is delivered through video-based training modules, making it easy to follow and comprehend the content.

Microsoft Excel Complete Course - Beginner Intermediate & Advanced To make learning Microsoft Excel easier for you, we have thoughtfully bundled our three greatest courses: Microsoft Excel Beginners, Intermediate, and Advanced. At this price, you won't find a better deal anywhere else. One of the most popular applications for visualizing and analyzing data that has been created to date is Microsoft Excel. These days, practically every industry and household use this helpful program for personal purposes. Excel is used by business owners for a plethora of tasks, including data analysis, visualizing data, tracking hours worked, money, and statements. This Microsoft Excel Complete Course can be very helpful to you whether you are a newbie, have some training and experience with the program, or haven't used Excel in a long time and need a thorough refresher to develop your skills. After completing this course, you will be a proficient Excel user. In a short period of time, our simple lessons will impart the knowledge in a very easy way. There won't be a rush because you can study whenever you want and at your own speed. After completing the course, your confidence in using Excel will increase. Course Highlights Microsoft Excel Complete Course - Beginner Intermediate & Advanced is an award winning and the best selling course that has been given the CPD Certification & IAO accreditation. It is the most suitable course anyone looking to work in this or relevant sector. It is considered one of the perfect courses in the UK that can help students/learners to get familiar with the topic and gain necessary skills to perform well in this field. We have packed Microsoft Excel Complete Course - Beginner Intermediate & Advanced into 73 modules for teaching you everything you need to become successful in this profession. To provide you ease of access, this course is designed for both part-time and full-time students. You can become accredited in just 11 hours, 6 minutes hours and it is also possible to study at your own pace. We have experienced tutors who will help you throughout the comprehensive syllabus of this course and answer all your queries through email. For further clarification, you will be able to recognize your qualification by checking the validity from our dedicated website. Why You Should Choose Microsoft Excel Complete Course - Beginner Intermediate & Advanced Lifetime access to the course No hidden fees or exam charges CPD Accredited certification on successful completion Full Tutor support on weekdays (Monday - Friday) Efficient exam system, assessment and instant results Download Printable PDF certificate immediately after completion Obtain the original print copy of your certificate, dispatch the next working day for as little as £9. Improve your chance of gaining professional skills and better earning potential. Who is this Course for? Microsoft Excel Complete Course - Beginner Intermediate & Advanced is CPD certified and IAO accredited. This makes it perfect for anyone trying to learn potential professional skills. As there is no experience and qualification required for this course, it is available for all students from any academic backgrounds. Requirements Our Microsoft Excel Complete Course - Beginner Intermediate & Advanced is fully compatible with any kind of device. Whether you are using Windows computer, Mac, smartphones or tablets, you will get the same experience while learning. Besides that, you will be able to access the course with any kind of internet connection from anywhere at any time without any kind of limitation. Career Path You will be ready to enter the relevant job market after completing this course. You will be able to gain necessary knowledge and skills required to succeed in this sector. All our Diplomas' are CPD and IAO accredited so you will be able to stand out in the crowd by adding our qualifications to your CV and Resume. Microsoft Excel Complete Course - Beginner Intermediate & Advanced Microsoft Excel 2019 New Features Introduction to Microsoft Excel 2019 New Features 00:07:00 CONCAT 00:02:00 IFS 00:01:00 MAXIFS 00:01:00 MINIFS 00:01:00 SWITCH 00:02:00 TEXTJOIN 00:01:00 Map Chart 00:02:00 Funnel Chart 00:01:00 Better Visuals 00:06:00 Pivot Table Enhancements 00:02:00 Power Pivot Updates 00:01:00 Getting Started With Microsoft Office Excel Navigate the Excel User Interface 00:28:00 Use Excel Commands 00:28:00 Create and Save a Basic Workbook 00:19:00 Enter Cell Data 00:12:00 Use Excel Help 00:05:00 Performing Calculations Create Worksheet Formulas 00:15:00 Insert Functions 00:17:00 Reuse Formulas and Functions 00:17:00 Modifying A Worksheet Insert, Delete, and Adjust Cells, Columns, and Rows 00:10:00 Search for and Replace Data 00:09:00 Use Proofing and Research Tools 00:07:00 Formatting A Worksheet Apply Text Formats 00:16:00 Apply Number Format 00:08:00 Align Cell Contents 00:09:00 Apply Styles and Themes 00:12:00 Apply Basic Conditional Formatting 00:11:00 Create and Use Templates 00:08:00 Printing Workbooks Preview and Print a Workbook 00:10:00 Set Up the Page Layout 00:09:00 Configure Headers and Footers 00:07:00 Managing Workbooks Manage Worksheets 00:05:00 Manage Workbook and Worksheet Views 00:07:00 Manage Workbook Properties 00:06:00 Working With Functions Work with Ranges 00:18:00 Use Specialized Functions 00:11:00 Work with Logical Functions 00:24:00 Work with Date & Time Functions 00:08:00 Work with Text Functions 00:11:00 Working With Lists Sort Data 00:10:00 Filter Data 00:10:00 Query Data with Database Functions 00:09:00 Outline and Subtotal Data 00:09:00 Analyzing Data Apply Intermediate Conditional Formatting 00:07:00 Apply Advanced Conditional Formatting 00:06:00 Visualizing Data With Charts Create Charts 00:13:00 Modify and Format Charts 00:12:00 Use Advanced Chart Features 00:13:00 Using PivotTables And Pivot Charts Create a PivotTable 00:13:00 Analyze PivotTable Data 00:12:00 Present Data with Pivot Charts 00:08:00 Filter Data by Using Timelines and Slicers 00:11:00 Working With Multiple Worksheets And Workbooks Use Links and External References 00:12:00 Use 3-D References 00:06:00 Consolidate Data 00:06:00 Using Lookup Functions And Formula Auditing Use Lookup Functions 00:13:00 Trace Cells 00:09:00 Watch and Evaluate Formulas 00:09:00 Sharing And Protecting Workbooks Collaborate on a Workbook 00:20:00 Protect Worksheets and Workbooks 00:08:00 Automating Workbook Functionality Apply Data Validation 00:13:00 Search for Invalid Data and Formulas with Errors 00:04:00 Work with Macros 00:18:00 Creating Sparklines And Mapping Data Create Sparklines 00:07:00 MapData 00:07:00 Forecasting Data Determine Potential Outcomes Using Data Tables 00:09:00 Determine Potential Outcomes Using Scenarios 00:09:00 Use the Goal Seek Feature 00:04:00 Forecasting Data Trends 00:05:00 Excel Templates Excel Templates 00:00:00 Resources Microsoft Excel 2019 00:00:00 Assignment Assignment - Microsoft Excel Complete Course - Beginner Intermediate & Advanced 00:00:00

Use Delegation to Multiply your Effectiveness, Build Team Resilience, and Increase Motivation in your Team

Welcome to Calculus Level 1 - Learn Differentiation, the course that will turn you into a differentiation expert. This course is the perfect stepping-stone to ignite your passion for calculus and launch you into the world of mathematical complexities with ease. We've designed this Calculus Level 1 - Learn Differentiation course to be your comprehensive guide, taking you from basic rules to advanced techniques in the realm of differentiation. In the initial sections, you'll find a clear, understandable introduction to the field of calculus and the fundamental rules of differentiation. We will then be diving into the differentiation of trigonometric, exponential, and logarithmic functions. This will equip you with the tools to handle any type of function thrown your way. As we progress, the course will gently introduce the Chain Rule and strengthen your understanding of it, making complex calculations a breeze. The advanced sections venture into intriguing areas like the differentiation of inverse and hyperbolic trig functions, implicit functions, and parametric functions. Techniques like Logarithmic Differentiation and the understanding of higher order derivatives are broken down and explained in an easy-to-digest manner. You will not only have mastered the skill of differentiation after completing the Calculus Level 1 - Learn Differentiation course, but you will also have laid a solid basis for higher calculus. This course combines theory, problem-solving, and revision portions, making it ideal for people new to the field and those wishing to improve their knowledge. Join us on this mathematical adventure to discover the brilliance of calculus in a whole new light. Sign up now! Learning Outcomes: Upon completion of the Calculus Level 1 - Learn Differentiation course, you should be able to: Understand the basics and fundamental principles of differentiation. Differentiate trigonometric and exponential functions with ease. Master the application of the Chain Rule in differentiation. Execute differentiation of inverse and hyperbolic trig functions. Comprehend and apply differentiation to implicit functions. Gain proficiency in logarithmic differentiation. Derive and solve higher order derivative functions. Who is this course for? This course is perfect for: High school students seeking a firm grasp on calculus. Undergraduates looking to bolster their mathematics foundation. Professionals needing a refresher course in calculus. Aspiring mathematicians and engineers who use calculus extensively. Career Path: Upon completion of the Calculus Level 1 - Learn Differentiation course, you open up a world of opportunities. This foundational knowledge in calculus can lead you to a wide range of careers in fields such as engineering, physics, computer science, economics, and more. Further, it serves as a stepping-stone for advanced studies in mathematics, paving the path for academic and research roles. This course ensures you have the mathematical prowess required in today's data-driven world. Certification After studying the course materials of the Calculus Level 1 - Learn Differentiation there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Prerequisites This Calculus Level 1 - Learn Differentiation does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Calculus Level 1 - Learn Differentiation was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Course Curriculum Section 01: Introduction Module 01: Promotional video 00:02:00 Module 02: Quick Guide 00:01:00 Section 02: Fundamental Rules for Differentiation Module 01: Power Rule 00:14:00 Module 02: Practice Problems Part 1 00:09:00 Module 03: Practice Problems Part 2 00:06:00 Module 04: Product Rule 00:13:00 Module 05: Quotient Rule 00:06:00 Module 06: Chain Rule For Differentiation 00:10:00 Section 03: Differentiation of Trigonometric Functions Module 01: Derivatives of Trigonometric functions 00:08:00 Module 02: Product Rule with Trigonometric Functions 00:11:00 Module 03: Quotient Rule with Trigonometric Functions 00:13:00 Module 04: Chain Rule with Trigonometric Functions Part 1 00:11:00 Module 05: Chain Rule with Trigonometric Functions Part 2 00:10:00 Module 06: Chain Rule with Trigonometric Functions Part 3 00:10:00 Section 04: Differentiation of Exponential Functions Module 01: Exponential Derivatives 00:13:00 Module 02: Chain Rule for Exponential Functions 00:14:00 Module 03: Derivatives of Exponential functions involving Trig Functions 00:12:00 Section 05: Differentiation of Logarithmic Functions Module 01: Derivatives of Logarithmic functions Part 1 00:09:00 Module 02: Derivatives of Logarithmic functions Part 2 00:10:00 Module 03: Derivatives of Logarithmic functions Part 3 00:06:00 Module 04: Problems involving Logarithmic and Trig functions part 1 00:09:00 Module 05: Problems involving Logarithmic and Trig functions part 2 00:07:00 Module 06: Problems involving Logarithmic and Trig functions part 3 00:05:00 Section 06: Revision Section 0on Chain Rule Module 01: Revision of Chain Rule Part 1 00:08:00 Module 02: Revision of Chain Rule Part 2 00:12:00 Module 03: Practice Problems Part 1 00:09:00 Module 04: Practice Problems Part 2 00:07:00 Section 07: Differentiation of inverse Trig Function Module 01: Derivatives of Inverse Trig Functions Part 1 00:09:00 Module 02: Derivatives of Inverse Trig Functions Part 2 00:11:00 Section 08: Differentiation of Hyperbolic Trig Functions Module 01: Derivatives of Hyperbolic Trig functions part 1 00:07:00 Module 02: Derivatives of Hyperbolic Trig functions part 2 00:07:00 Module 03: Derivatives of Inverse Hyperbolic Trig functions 00:09:00 Section 09: Differentiation of Implicit functions Module 01: Differentiation of Implicit functions Part 1 00:11:00 Module 02: Differentiation of Implicit functions Part 2 00:06:00 Module 03: Differentiation of Implicit functions involving Trig functions-1 00:16:00 Module 04: Differentiation of Implicit functions involving Trig functions-2 00:10:00 Section 10: Logarithmic Differentiation Module 01: Logarithmic Differentiation Part 1 00:13:00 Module 02: Logarithmic Differentiation Part 2 00:07:00 Module 03: Logarithmic Differentiation Part 3 00:13:00 Module 04: Logarithmic Differentiation Part 4 00:08:00 Module 05: Logarithmic Differentiation Part 5 00:10:00 Module 06: Logarithmic Differentiation Part 6 00:09:00 Module 07: Logarithmic Differentiation Part 7 00:11:00 Section 11: Differentiation of Parametric Functions Module 01: Differentiation of Parametric Functions Part 1 00:12:00 Module 02: Differentiation of Parametric Functions Part 2 00:09:00 Module 03: Differentiation of a function w. r. t. another function Part 1 00:11:00 Module 04: Differentiation of a function w. r. t. another function Part 2 00:05:00 Section 12: Differentiation of Higher order derivatives Module 01: Higher order derivatives Part 1 00:09:00 Module 02: Higher order derivatives Part 2 00:04:00 Module 03: Higher order derivatives Part 3 00:10:00 Module 04: Higher order derivatives Part 4 00:09:00 Module 05: Higher Order Derivatives involving Trig Functions 00:06:00 Module 06: Second order derivatives with Parametric functions 00:13:00

Discover the power of data science and machine learning with Python! Learn essential techniques, algorithms, and tools to analyze data, build predictive models, and unlock insights. Dive into hands-on projects, from data manipulation to advanced machine learning applications. Elevate your skills and unleash the potential of Python for data-driven decision-making.

Overview Mastering data science skills and expertise can open new doors of opportunities for you in a wide range of fields. Learn the fundamentals and develop a solid grasp of Python data science with the comprehensive Data Science with Python course. This course is designed to assist you in securing a valuable skill set and boosting your career. This course will provide you with quality training on the fundamentals of data analysis with Python. From the step-by-step learning process, you will learn the techniques of setting up the system. Then the course will teach you Python data structure and functions. You will receive detailed lessons on NumPy, Matplotlib, and Pandas. Furthermore, you will develop the skills for Algorithm Evaluation Techniques, visualising datasets and much more. After completing the course you will receive a certificate of achievement. This certificate will help you create an impressive resume. So join today! How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? This course Data Science with Python course is ideal for beginners in data science. It will help them develop a solid grasp of Python and help them pursue their dream career in the field of data science. Requirements The students will not require any formal qualifications or previous experience to enrol in this course. Anyone can learn from the course anytime from anywhere through smart devices like laptops, tabs, PC, and smartphones with stable internet connections. They can complete the course according to their preferable pace so, there is no need to rush. Career Path This course will equip you with valuable knowledge and effective skills in this area. After completing the course, you will be able to explore career opportunities in the fields such as Data Analyst Data Scientist Data Manager Business Analyst And much more! Course Curriculum 90 sections • 90 lectures • 10:19:00 total length •Course Overview & Table of Contents: 00:09:00 •Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types: 00:05:00 •Introduction to Machine Learning - Part 2 - Classifications and Applications: 00:06:00 •System and Environment preparation - Part 1: 00:04:00 •System and Environment preparation - Part 2: 00:06:00 •Learn Basics of python - Assignment 1: 00:10:00 •Learn Basics of python - Assignment 2: 00:09:00 •Learn Basics of python - Functions: 00:04:00 •Learn Basics of python - Data Structures: 00:12:00 •Learn Basics of NumPy - NumPy Array: 00:06:00 •Learn Basics of NumPy - NumPy Data: 00:08:00 •Learn Basics of NumPy - NumPy Arithmetic: 00:04:00 •Learn Basics of Matplotlib: 00:07:00 •Learn Basics of Pandas - Part 1: 00:06:00 •Learn Basics of Pandas - Part 2: 00:07:00 •Understanding the CSV data file: 00:09:00 •Load and Read CSV data file using Python Standard Library: 00:09:00 •Load and Read CSV data file using NumPy: 00:04:00 •Load and Read CSV data file using Pandas: 00:05:00 •Dataset Summary - Peek, Dimensions and Data Types: 00:09:00 •Dataset Summary - Class Distribution and Data Summary: 00:09:00 •Dataset Summary - Explaining Correlation: 00:11:00 •Dataset Summary - Explaining Skewness - Gaussian and Normal Curve: 00:07:00 •Dataset Visualization - Using Histograms: 00:07:00 •Dataset Visualization - Using Density Plots: 00:06:00 •Dataset Visualization - Box and Whisker Plots: 00:05:00 •Multivariate Dataset Visualization - Correlation Plots: 00:08:00 •Multivariate Dataset Visualization - Scatter Plots: 00:05:00 •Data Preparation (Pre-Processing) - Introduction: 00:09:00 •Data Preparation - Re-scaling Data - Part 1: 00:09:00 •Data Preparation - Re-scaling Data - Part 2: 00:09:00 •Data Preparation - Standardizing Data - Part 1: 00:07:00 •Data Preparation - Standardizing Data - Part 2: 00:04:00 •Data Preparation - Normalizing Data: 00:08:00 •Data Preparation - Binarizing Data: 00:06:00 •Feature Selection - Introduction: 00:07:00 •Feature Selection - Uni-variate Part 1 - Chi-Squared Test: 00:09:00 •Feature Selection - Uni-variate Part 2 - Chi-Squared Test: 00:10:00 •Feature Selection - Recursive Feature Elimination: 00:11:00 •Feature Selection - Principal Component Analysis (PCA): 00:09:00 •Feature Selection - Feature Importance: 00:06:00 •Refresher Session - The Mechanism of Re-sampling, Training and Testing: 00:12:00 •Algorithm Evaluation Techniques - Introduction: 00:07:00 •Algorithm Evaluation Techniques - Train and Test Set: 00:11:00 •Algorithm Evaluation Techniques - K-Fold Cross Validation: 00:09:00 •Algorithm Evaluation Techniques - Leave One Out Cross Validation: 00:05:00 •Algorithm Evaluation Techniques - Repeated Random Test-Train Splits: 00:07:00 •Algorithm Evaluation Metrics - Introduction: 00:09:00 •Algorithm Evaluation Metrics - Classification Accuracy: 00:08:00 •Algorithm Evaluation Metrics - Log Loss: 00:03:00 •Algorithm Evaluation Metrics - Area Under ROC Curve: 00:06:00 •Algorithm Evaluation Metrics - Confusion Matrix: 00:10:00 •Algorithm Evaluation Metrics - Classification Report: 00:04:00 •Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction: 00:06:00 •Algorithm Evaluation Metrics - Mean Absolute Error: 00:07:00 •Algorithm Evaluation Metrics - Mean Square Error: 00:03:00 •Algorithm Evaluation Metrics - R Squared: 00:04:00 •Classification Algorithm Spot Check - Logistic Regression: 00:12:00 •Classification Algorithm Spot Check - Linear Discriminant Analysis: 00:04:00 •Classification Algorithm Spot Check - K-Nearest Neighbors: 00:05:00 •Classification Algorithm Spot Check - Naive Bayes: 00:04:00 •Classification Algorithm Spot Check - CART: 00:04:00 •Classification Algorithm Spot Check - Support Vector Machines: 00:05:00 •Regression Algorithm Spot Check - Linear Regression: 00:08:00 •Regression Algorithm Spot Check - Ridge Regression: 00:03:00 •Regression Algorithm Spot Check - Lasso Linear Regression: 00:03:00 •Regression Algorithm Spot Check - Elastic Net Regression: 00:02:00 •Regression Algorithm Spot Check - K-Nearest Neighbors: 00:06:00 •Regression Algorithm Spot Check - CART: 00:04:00 •Regression Algorithm Spot Check - Support Vector Machines (SVM): 00:04:00 •Compare Algorithms - Part 1 : Choosing the best Machine Learning Model: 00:09:00 •Compare Algorithms - Part 2 : Choosing the best Machine Learning Model: 00:05:00 •Pipelines : Data Preparation and Data Modelling: 00:11:00 •Pipelines : Feature Selection and Data Modelling: 00:10:00 •Performance Improvement: Ensembles - Voting: 00:07:00 •Performance Improvement: Ensembles - Bagging: 00:08:00 •Performance Improvement: Ensembles - Boosting: 00:05:00 •Performance Improvement: Parameter Tuning using Grid Search: 00:08:00 •Performance Improvement: Parameter Tuning using Random Search: 00:06:00 •Export, Save and Load Machine Learning Models : Pickle: 00:10:00 •Export, Save and Load Machine Learning Models : Joblib: 00:06:00 •Finalizing a Model - Introduction and Steps: 00:07:00 •Finalizing a Classification Model - The Pima Indian Diabetes Dataset: 00:07:00 •Quick Session: Imbalanced Data Set - Issue Overview and Steps: 00:09:00 •Iris Dataset : Finalizing Multi-Class Dataset: 00:09:00 •Finalizing a Regression Model - The Boston Housing Price Dataset: 00:08:00 •Real-time Predictions: Using the Pima Indian Diabetes Classification Model: 00:07:00 •Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset: 00:03:00 •Real-time Predictions: Using the Boston Housing Regression Model: 00:08:00 •Resources - Data Science & Machine Learning with Python: 00:00:00