10837 Mac courses

This course is specifically for Sunbelt Rentals team members. This module covers the requirements for TExT and how to carry out the Thorough Examination and Test process for on-tool dust extraction machines. To complete the training you will need a working knowledge of machinery and have operated or completed Combined Inspections and Tests (PAT) on machines.

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As a telehandler operator, understand industry dangers, follow safety protocols, operate machine correctly, perform checks, handle loads efficiently and safely.

Forklift truck - Operators will learn industry basics, machine components, control usage, pre-operation checks, and safe lifting.

Operate machine effectively, understand industry risks, perform checks. Identify controls, handle underslung loads, and manage environmental impacts.

Understand excavator risks and operator duties, know the handbook, perform checks, operate and manoeuvre the machine, complete tasks and shutdown procedures

Operate a tractor safely by understanding risks, machine controls, PPE, and safety checks, while ensuring proper setup, operation, and shutdown procedures.

Machine Learning for Business Professionals This Machine Learning for Business Professionals course provides an in-depth exploration of how machine learning and data science can revolutionise business decision-making. Starting with the basics of R programming, you’ll learn how to manage and visualise data effectively, progressing to more complex machine learning techniques. By the end of this course, you’ll be equipped to apply predictive models and generate insights that can significantly impact business strategies. Course Curriculum Data Science and Machine Learning Course Intro Getting Started with R Data Types and Structures in R Intermediate R Data Manipulation in R Data Visualization in R Creating Reports with R Markdown Building Webapps with R Shiny Introduction to Machine Learning Starting A Career in Data Science Resources (Learn more about this online course)

Blue CSCS Card NVQ Level 2 Plant This qualification provides you with the opportunity to showcase their knowledge, skills and understanding in their chosen specialism. You will have the relevant experience in one of the specific areas. You will be operating on one of the following machines: Forward Tipping Dumper Ride on Roller Excavator Telehandler Induction As soon as you register you will be given a dedicated assessor. They will arrange an induction and together with your assessor, you will get to decide on the pathway which best proves your competency. The induction is used to plan out how you will gather the relevant evidence to complete the course. During the course The assessor will work with you to build a portfolio of evidence that allows you to showcase your knowledge, skills and experience. The assessor will also regularly review and provide you with feedback. This will allow you to keep on track to progress quickly. You will be assessed through various methods such as observations, written questions, evidence generated from the workplace, professional discussion, and witness testimonials. On completion Once all feedback has been agreed, the Internal Quality Assurer will review your portfolio and in agreement with your assessor the certificate will be applied for. To download our PDF for this course then please click here.

Duration 5 Days 30 CPD hours This course is intended for The skills covered in this course converge on four areas-software development, IT operations, applied math and statistics, and business analysis. Target students for this course should be looking to build upon their knowledge of the data science process so that they can apply AI systems, particularly machine learning models, to business problems. So, the target student is likely a data science practitioner, software developer, or business analyst looking to expand their knowledge of machine learning algorithms and how they can help create intelligent decisionmaking products that bring value to the business. A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming. This course is also designed to assist students in preparing for the CertNexus Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification Overview In this course, you will develop AI solutions for business problems. You will: Solve a given business problem using AI and ML. Prepare data for use in machine learning. Train, evaluate, and tune a machine learning model. Build linear regression models. Build forecasting models. Build classification models using logistic regression and k -nearest neighbor. Build clustering models. Build classification and regression models using decision trees and random forests. Build classification and regression models using support-vector machines (SVMs). Build artificial neural networks for deep learning. Put machine learning models into operation using automated processes. Maintain machine learning pipelines and models while they are in production Artificial intelligence (AI) and machine learning (ML) have become essential parts of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, all while following a methodical workflow for developing data-driven solutions. Solving Business Problems Using AI and ML Topic A: Identify AI and ML Solutions for Business Problems Topic B: Formulate a Machine Learning Problem Topic C: Select Approaches to Machine Learning Preparing Data Topic A: Collect Data Topic B: Transform Data Topic C: Engineer Features Topic D: Work with Unstructured Data Training, Evaluating, and Tuning a Machine Learning Model Topic A: Train a Machine Learning Model Topic B: Evaluate and Tune a Machine Learning Model Building Linear Regression Models Topic A: Build Regression Models Using Linear Algebra Topic B: Build Regularized Linear Regression Models Topic C: Build Iterative Linear Regression Models Building Forecasting Models Topic A: Build Univariate Time Series Models Topic B: Build Multivariate Time Series Models Building Classification Models Using Logistic Regression and k-Nearest Neighbor Topic A: Train Binary Classification Models Using Logistic Regression Topic B: Train Binary Classification Models Using k-Nearest Neighbor Topic C: Train Multi-Class Classification Models Topic D: Evaluate Classification Models Topic E: Tune Classification Models Building Clustering Models Topic A: Build k-Means Clustering Models Topic B: Build Hierarchical Clustering Models Building Decision Trees and Random Forests Topic A: Build Decision Tree Models Topic B: Build Random Forest Models Building Support-Vector Machines Topic A: Build SVM Models for Classification Topic B: Build SVM Models for Regression Building Artificial Neural Networks Topic A: Build Multi-Layer Perceptrons (MLP) Topic B: Build Convolutional Neural Networks (CNN) Topic C: Build Recurrent Neural Networks (RNN) Operationalizing Machine Learning Models Topic A: Deploy Machine Learning Models Topic B: Automate the Machine Learning Process with MLOps Topic C: Integrate Models into Machine Learning Systems Maintaining Machine Learning Operations Topic A: Secure Machine Learning Pipelines Topic B: Maintain Models in Production