2702 Learning courses in London delivered Live Online

Duration 3 Days 18 CPD hours This course is intended for This course is geared for experienced Scala developers who are new to the world of machine learning and are eager to expand their skillset. Professionals such as data engineers, data scientists, and software engineers who want to harness the power of machine learning in their Scala-based projects will greatly benefit from attending. Additionally, team leads and technical managers who oversee Scala development projects and want to integrate machine learning capabilities into their workflows can gain valuable insights from this course Overview Working in a hands-on learning environment led by our expert instructor you'll: Grasp the fundamentals of machine learning and its various categories, empowering you to make informed decisions about which techniques to apply in different situations. Master the use of Scala-specific tools and libraries, such as Breeze, Saddle, and DeepLearning.scala, allowing you to efficiently process, analyze, and visualize data for machine learning projects. Develop a strong understanding of supervised and unsupervised learning algorithms, enabling you to confidently choose the right approach for your data and effectively build predictive models Gain hands-on experience with neural networks and deep learning, equipping you with the know-how to create advanced applications in areas like natural language processing and image recognition. Explore the world of generative AI and learn how to utilize GPT-Scala for creative text generation tasks, broadening your skill set and making you a more versatile developer. Conquer the realm of scalable machine learning with Scala, learning the secrets to tackling large-scale data processing and analysis challenges with ease. Sharpen your skills in model evaluation, validation, and optimization, ensuring that your machine learning models perform reliably and effectively in any situation. Machine Learning Essentials for Scala Developers is a three-day course designed to provide a solid introduction to the world of machine learning using the Scala language. Throughout the hands-on course, you?ll explore a range of machine learning algorithms and techniques, from supervised and unsupervised learning to neural networks and deep learning, all specifically crafted for Scala developers. Our expert trainer will guide you through real-world, focused hands-on labs designed to help you apply the knowledge you gain in real-world scenarios, giving you the confidence to tackle machine learning challenges in your own projects. You'll dive into innovative tools and libraries such as Breeze, Saddle, DeepLearning.scala, GPT-Scala (and Generative AI with Scala), and TensorFlow-Scala. These cutting-edge resources will enable you to build and deploy machine learning models for a wide range of projects, including data analysis, natural language processing, image recognition and more. Upon completing this course, you'll have the skills required to tackle complex projects and confidently develop intelligent applications. You?ll be able to drive business outcomes, optimize processes, and contribute to innovative projects that leverage the power of data-driven insights and predictions. Introduction to Machine Learning and Scala Learning Outcome: Understand the fundamentals of machine learning and Scala's role in this domain. What is Machine Learning? Machine Learning with Scala: Advantages and Use Cases Supervised Learning in Scala Learn the basics of supervised learning and how to apply it using Scala. Supervised Learning: Regression and Classification Linear Regression in Scala Logistic Regression in Scala Unsupervised Learning in Scala Understand unsupervised learning and how to apply it using Scala. Unsupervised Learning:Clustering and Dimensionality Reduction K-means Clustering in Scala Principal Component Analysis in Scala Neural Networks and Deep Learning in Scala Learning Outcome: Learn the basics of neural networks and deep learning with a focus on implementing them in Scala. Introduction to Neural Networks Feedforward Neural Networks in Scala Deep Learning and Convolutional Neural Networks Introduction to Generative AI and GPT in Scala Gain a basic understanding of generative AI and GPT, and how to utilize GPT-Scala for natural language tasks. Generative AI: Overview and Use Cases Introduction to GPT (Generative Pre-trained Transformer) GPT-Scala: A Library for GPT in Scala Reinforcement Learning in Scala Understand the basics of reinforcement learning and its implementation in Scala. Introduction to Reinforcement Learning Q-learning and Value Iteration Reinforcement Learning with Scala Time Series Analysis using Scala Learn time series analysis techniques and how to apply them in Scala. Introduction to Time Series Analysis Autoregressive Integrated Moving Average (ARIMA) Models Time Series Analysis in Scala Natural Language Processing (NLP) with Scala Gain an understanding of natural language processing techniques and their application in Scala. Introduction to NLP: Techniques and Applications Text Processing and Feature Extraction NLP Libraries and Tools for Scala Image Processing and Computer Vision with Scala Learn image processing techniques and computer vision concepts with a focus on implementing them in Scala. Introduction to Image Processing and Computer Vision Feature Extraction and Image Classification Image Processing Libraries for Scala Model Evaluation and Validation Understand the importance of model evaluation and validation, and how to apply these concepts using Scala. Model Evaluation Metrics Cross-Validation Techniques Model Selection and Tuning in Scala Scalable Machine Learning with Scala Learn how to handle large-scale machine learning problems using Scala. Challenges of Large-Scale Machine Learning Data Partitioning and Parallelization Distributed Machine Learning with Scala Machine Learning Deployment and Production Understand the process of deploying machine learning models into production using Scala. Deployment Challenges and Best Practices Model Serialization and Deserialization Monitoring and Updating Models in Production Ensemble Learning Techniques in Scala Discover ensemble learning techniques and their implementation in Scala. Introduction to Ensemble Learning Bagging and Boosting Techniques Implementing Ensemble Models in Scala Feature Engineering for Machine Learning in Scala Learn advanced feature engineering techniques to improve machine learning model performance in Scala. Importance of Feature Engineering in Machine Learning Feature Scaling and Normalization Techniques Handling Missing Data and Categorical Features Advanced Optimization Techniques for Machine Learning Understand advanced optimization techniques for machine learning models and their application in Scala. Gradient Descent and Variants Regularization Techniques (L1 and L2) Hyperparameter Tuning Strategies

THIS COURSE PACKAGE INCLUDES: 1: INTRODUCTION TO ECG COURSE - RECORDING & BASIC INTERPRETATION (GPT009) 2: ADVANCED ECG COURSE - INTERPRETATION & ANALYSIS (GPT010) Learn how to set up and record a basic ECG trace, followed by advanced analysis and interpretation FAST-TRACK YOUR ECG TRAINING WITH OUR BEGINNER TO ADVANCED TRAINING PACKAGE 20% off - Multi-Course Discount Cover all stages from Level 1 through to Level 4 (FDSc) Cover your theory training online Practical training in Classroom or Virtual Classroom Comprehensive Practise@Home training kits for VC Awards 2 accredited qualifications Dual Accreditations are awarded for all courses (Open College Network and CPD) Covers all steps required to competently set up and perform an ECG trace. Practical sessions include electrode placement on mannequin, running traces and identifying anomalies. Learn beginner to advanced skills and interpretation. Basic understanding of English language required. OPEN TO ALL APPLICANTS About these courses 1: INTRODUCTION TO ECG COURSE - RECORDING AND BASIC INTERPRETATION (GPT009) PART 1 - Theory Allow approx. 5-6 hours PART 2 - Practical Training Attend a classroom location or join us in our virtual classroom * - 3-4 hours ACCREDITED LEVEL 3 QUALIFICATION * Virtual Classroom option includes a free comprehensive Practise@Home ECG training kit. 2: ADVANCED ECG COURSE - INTERPRETATION AND ANALYSIS (GPT010) E-LEARNING - Theory Allow approx. 6-8 hours ACCREDITED LEVEL 4 QUALIFICATION OPTIONAL: GETTING STARTED IN ECG (GPT002) A free starter ECG Course (unassessed) developed to help you understand the basics of ECG recording: 3 modules in total with no Questions! If you are already familiar with ECGs then you may prefer to save time and opt out of this mini-course at booking stage. This "mini-course" is available at no charge. Learning Outcomes GPT009: Understanding different ECG equipment types ECG equipment - set-up and calibration Includes professionalism, consent, IPC and legal requirement Patient preparation How to correctly apply electrodes to limbs and chest Identify artifacts (equipment and patients Identify and recognise routine traces Identify and recognise non-routine traces Identify traces requiring urgent attention Labelling and reporting GPT010: Understand the acceptable variations within the normal ECG of healthy adults. Recognise the expected patterns of an ECG from a healthy child from birth onwards and identify abnormalities. Interpret abnormal ECG patterns in adults. Diagnose arrhythmias as an underlying cause of palpitations and syncope. Exploring sinus rhythm, extrasystoles, paroxysmal tachycardia and the importance of a physical examination. Identifying syncopal episodes attributable to cardiovascular disease as opposed to arrhythmias. Recognise ECG markers for tachycardias, bradycardias, pre-excitation syndromes, bi-fascicular block, and first-degree block with bundle branch block. Differentiate between supraventricular and ventricular extrasystoles and be able to diagnose broad complex tachycardias, ventricular flutter and fibrillation, sick sinus syndrome, and Stokes-Adams attacks. Recognise and identify symptoms associated with the causes of acute or chronic chest pain in patients who present with myocardial infarction (heart attack), pulmonary embolism, significant central pulmonary embolism, pericarditis, aortic dissection, oesophageal rupture, spinal disorders, vertebral collapse, posterior infarction, and angina. Recognise symptoms indicative of conditions such as pulmonary oedema, chest diseases, and pulmonary congestion. After the course GPT009: Safely and competently set up an ECG machine Introduce patients to the ECG test, adhering to compliancy requirements before and after testing Perform an ECG test to national guidelines Understand basic traces and their correlation to cardiac issues Recognise normal and erroneous recordings Recognise recordings that require urgent medical follow-up Complete the recording and label (or record digital copies) as per guidelines GPT010: Appreciate normal and abnormal ECG variations in the context of varying pathologies. Be able to determine whether an arrhythmia has an underlying cause that requires medical intervention. Interpret ECGs as a function of the patient's ongoing cardiac management. Understand and apply the Burce Protocol exercise test in relevant clinical situations. Know how to clinically respond to a patient with chest pain including further investigations required, pain relief, history and examination and echocardiogram. Understand and apply the fundamental principles of arrhythmia management. Understand the primary causes of heart disease and the diagnostic process. Appreciate the importance of the ECG as a diagnostic tool alongside the patient’s history and clinical presentation and recognising its limitations. Course Package Components: PACKAGE - Beginner to Advanced ECG - Virtual Classroom - INTRO - Part 1 online Part 2 Virtual Classroom (AM) + ADVANCED - E-learning

Transform stress into strength with this 4-week course, enhancing resilience, emotional skills, and effective communication for professionals.

Learn and practice the skills needed to deliver a brilliant presentation.

This half-day workshop delivered face-to-face or online is designed for anyone in your organisation that wants to become a Neurodiversity Champion - someone who wants to educate and change the way that Neurodiversity is viewed in the workplace.

The course covers research design principles and all main quantitative evaluation methods: randomised experiments, instrumental variables, sharp and fuzzy regression discontinuity designs, regression methods, matching methods and longitudinal methods (before-after, difference-in-differences and synthetic controls).

CPD accredited Level 3 Train the Trainer, Speaking & Presenting Skills course. Delivered online (Zoom) by a live tutor. Exam and Certificate fee included in the price.

Duration 4 Days 24 CPD hours This course is intended for This course is intended for: Developers Solutions Architects Data Engineers Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker Overview In this course, you will learn to: Select and justify the appropriate ML approach for a given business problem Use the ML pipeline to solve a specific business problem Train, evaluate, deploy, and tune an ML model using Amazon SageMaker Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS Apply machine learning to a real-life business problem after the course is complete This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem. Module 0: Introduction Pre-assessment Module 1: Introduction to Machine Learning and the ML Pipeline Overview of machine learning, including use cases, types of machine learning, and key concepts Overview of the ML pipeline Introduction to course projects and approach Module 2: Introduction to Amazon SageMaker Introduction to Amazon SageMaker Demo: Amazon SageMaker and Jupyter notebooks Hands-on: Amazon SageMaker and Jupyter notebooks Module 3: Problem Formulation Overview of problem formulation and deciding if ML is the right solution Converting a business problem into an ML problem Demo: Amazon SageMaker Ground Truth Hands-on: Amazon SageMaker Ground Truth Practice problem formulation Formulate problems for projects Module 4: Preprocessing Overview of data collection and integration, and techniques for data preprocessing and visualization Practice preprocessing Preprocess project data Class discussion about projects Module 5: Model Training Choosing the right algorithm Formatting and splitting your data for training Loss functions and gradient descent for improving your model Demo: Create a training job in Amazon SageMaker Module 6: Model Evaluation How to evaluate classification models How to evaluate regression models Practice model training and evaluation Train and evaluate project models Initial project presentations Module 7: Feature Engineering and Model Tuning Feature extraction, selection, creation, and transformation Hyperparameter tuning Demo: SageMaker hyperparameter optimization Practice feature engineering and model tuning Apply feature engineering and model tuning to projects Final project presentations Module 8: Deployment How to deploy, inference, and monitor your model on Amazon SageMaker Deploying ML at the edge Demo: Creating an Amazon SageMaker endpoint Post-assessment Course wrap-up Additional course details: Nexus Humans The Machine Learning Pipeline on AWS 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 The Machine Learning Pipeline on AWS 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.

Duration 4 Days 24 CPD hours This course is intended for This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. Overview Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow. Prerequisites Creating cloud resources in Microsoft Azure. Using Python to explore and visualize data. Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow. Working with containers AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience. 1 - Design a data ingestion strategy for machine learning projects Identify your data source and format Choose how to serve data to machine learning workflows Design a data ingestion solution 2 - Design a machine learning model training solution Identify machine learning tasks Choose a service to train a machine learning model Decide between compute options 3 - Design a model deployment solution Understand how model will be consumed Decide on real-time or batch deployment 4 - Design a machine learning operations solution Explore an MLOps architecture Design for monitoring Design for retraining 5 - Explore Azure Machine Learning workspace resources and assets Create an Azure Machine Learning workspace Identify Azure Machine Learning resources Identify Azure Machine Learning assets Train models in the workspace 6 - Explore developer tools for workspace interaction Explore the studio Explore the Python SDK Explore the CLI 7 - Make data available in Azure Machine Learning Understand URIs Create a datastore Create a data asset 8 - Work with compute targets in Azure Machine Learning Choose the appropriate compute target Create and use a compute instance Create and use a compute cluster 9 - Work with environments in Azure Machine Learning Understand environments Explore and use curated environments Create and use custom environments 10 - Find the best classification model with Automated Machine Learning Preprocess data and configure featurization Run an Automated Machine Learning experiment Evaluate and compare models 11 - Track model training in Jupyter notebooks with MLflow Configure MLflow for model tracking in notebooks Train and track models in notebooks 12 - Run a training script as a command job in Azure Machine Learning Convert a notebook to a script Run a script as a command job Use parameters in a command job 13 - Track model training with MLflow in jobs Track metrics with MLflow View metrics and evaluate models 14 - Perform hyperparameter tuning with Azure Machine Learning Define a search space Configure a sampling method Configure early termination Use a sweep job for hyperparameter tuning 15 - Run pipelines in Azure Machine Learning Create components Create a pipeline Run a pipeline job 16 - Register an MLflow model in Azure Machine Learning Log models with MLflow Understand the MLflow model format Register an MLflow model 17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning Understand Responsible AI Create the Responsible AI dashboard Evaluate the Responsible AI dashboard 18 - Deploy a model to a managed online endpoint Explore managed online endpoints Deploy your MLflow model to a managed online endpoint Deploy a model to a managed online endpoint Test managed online endpoints 19 - Deploy a model to a batch endpoint Understand and create batch endpoints Deploy your MLflow model to a batch endpoint Deploy a custom model to a batch endpoint Invoke and troubleshoot batch endpoints

Duration 1 Days 6 CPD hours This course is intended for This course is intended for individuals who want to develop a core set of soft skill. Overview Upon successful completion of this course, students will be able to better interact and communicate in the workplace. In this course, students will develop a core set of soft skills by managing and looking at the way people interact and seeing things in a new light. 1 - GETTING STARTED Housekeeping Items Pre-Assignment Review Workshop Objectives The Parking Lot Action Plan 2 - WHAT ARE SOFT SKILLS? Definition of Soft Skills Empathy and the Emotional Intelligence Quotient Professionalism Learned vs. Inborn Traits 3 - SOFT SKILL 1: COMMUNICATION Ways We Communicate Improving Non-Verbal Communication Listening Openness and Honesty 4 - SOFT SKILL 2: TEAMWORK Identifying Capabilities Get Into Your Role Learn the Whole Process The Power of Flow 5 - SOFT SKILL 3: PROBLEM SOLVING Define the Problem Generate Alternative Solutions Evaluate the Plans Implementation and Re-evaluation 6 - SOFT SKILL 4: TIME MANAGEMENT The Art of Scheduling Prioritizing Managing Distractions The Multitasking Myth 7 - SOFT SKILL 5 AND 6: ATTITUDE AND WORK ETHIC What Are You Working For? Caring for Others vs. Self Building Trust Work Is Its Own Reward 8 - SOFT SKILL 7: ADAPTABILITY/FLEXIBILITY Getting over the Good Old Days Syndrome Changing to Manage Process Changing to Manage People Showing You're Worth Your Weight in Adaptability 9 - SOFT SKILL 8: SELF-CONFIDENCE Confident Traits Self-Questionnaire Surefire Confidence Building Tactics Build Up Others 10 - SOFT SKILL 9: ABILITY TO LEARN FROM CRITICISM Wow, You Mean I'm Not Perfect? Listen With An Open Mind Analyze and Learn Clear the Air and Don't Hold Any Grudges 11 - SOFT SKILL 10: NETWORKING Redefining Need Identifying Others' Interests Reaching Out When to Back Off 12 - WRAPPING UP Words From The Wise Review Of The Parking Lot Lessons Learned Recommended Reading Completion Of Action Plans And Evaluations Additional course details: Nexus Humans 10 Soft Skills You Need 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 10 Soft Skills You Need 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.