177 Managed Learning Programme (MLP) courses

Duration 1 Days 6 CPD hours This course is intended for This course is intended for managers and supervisors engaged in working with the Millennial generation workforce. Overview Upon successful completion of this course, participants will be able to define onboarding, discuss the characteristics of Millennials, and develop action plans for working with them. In this course, participants will learn to build an onboarding process that recognizes the challenges and strengths of the Millennial workforce. Getting Started Workshop Objectives Action Plan Purpose of Onboarding Start Up Costs Employee Anxiety Employee Turnover Realistic Expectations Practical Illustration Introduction Why Onboarding? Importance of Onboarding Making Employees Feel Welcome First Day Checklist Practical Illustration Millennials and Onboarding Who are Millennials? How Do Millennials Differ from Other Workers? Investiture Socialization ? Let Them Be Themselves! Informal Rather than Formal Onboarding Processes Practical Illustration Onboarding Checklist Pre-Arrival Arrival First Day First Week First Month Practical Illustration Engaging the Millennial Employee Create an Informal Program Engage Employees One-on-one The Role of Human Resources The Role of Managers Practical Illustration Following Up with the Millennial Employee Initial Check-In ? One-on-one Following up ? Regular, Informal Follow Ups Setting Schedules ? Millennials and Work-Life Mentoring and the Millennial Practical Illustration Setting Expectations with the Millennial Employee Define Requirements ? Provide Specific Instructions Identify Opportunities for Improvement and Growth Set Verbal Expectations Put It in Writing Practical Illustration Mentoring the Millennial Be Hands-On and Involved Serial Mentoring Be a Mentor, Not an Authority Figure Focus Millennia?s Exploratory Drive on Work Practical Illustration Assigning Work to the Millennial Employee Provide Clear Structure and Guidelines Provide Specific Benchmarks Set Boundaries and Provide Reality Checks Practical Illustration Providing Feedback Millennials Thrive on Feedback! Characteristics of Quality Feedback Informal Feedback Formal Feedback Practical Illustration Wrapping Up Words From the Wise Additional course details: Nexus Humans Millennial Onboarding 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 Millennial Onboarding 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 1 Days 6 CPD hours This course is intended for Software Engineers Overview The objective of this course is to learn the key language concepts to machine learning, Spark MLlib, and Spark ML. This course will teach you the key language concepts to machine learning, Spark MLlib, and Spark ML. The course includes coverage of collaborative filtering, clustering, classification, algorithms, and data volume. This course will teach you the key language concepts to machine learning, Spark MLlib, and Spark ML. The course includes coverage of collaborative filtering, clustering, classification, algorithms, and data volume.

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 2 Days 12 CPD hours This course is intended for This course benefits individuals responsible for configuring and monitoring devices running the Junos OS. Overview Describe the history and rationale for MPLS, as well as its basic terminology. Explain the MPLS label operations (push, pop, swap) and the concept of label-switched path (LSP). Describe the configuration and verification of MPLS forwarding. Describe the functionalities and operation of RSVP and LDP. Configure and verify RSVP-signaled and LDP-signaled LSPs. Select and configure the appropriate label distribution protocol for a given set of requirements. Describe the default Junos OS MPLS traffic engineering behavior. Explain the Interior Gateway Protocol (IGP) extensions used to build the Traffic Engineering Database (TED). Describe the Constrained Shortest Path First (CSPF) algorithm, its uses, and its path selection process. Describe administrative groups and how they can be used to influence path selection. Describe the default traffic protection behavior of RSVP-signaled LSPs. Explain the use of primary and secondary LSPs. Describe the operation and configuration of fast reroute. Describe the operation and configuration of link and node protection. Describe the operation and configuration of LDP loop-free alternate. Describe the LSP optimization options. Explain LSP priority and preemption. Describe the behavior of fate sharing. Describe how SRLG changes the CSPF algorithm when computing the path of a secondary LSP. Explain how extended admin groups can be used to influence path selection. Explain the purpose of several miscellaneous MPLS features. This two-day course is designed to provide students with a solid foundation on Multiprotocol Label Switching (MPLS). Course Outline Course Introduction MPLS Fundamentals MPLS Foundation Terminology MPLS Configuration MPLS Packet Forwarding Label Distribution Protocols Label Distribution Protocols RSVP LDP Routing Table Integration Mapping Next-Hops to LSPs Route Resolution Example Route Resolution Summary IGP Passive Versus Next-Hop Self for BGP Destinations Constrained Shortest Path First RSVP Behavior Without CSPF CSPF Algorithm CSPF Tie Breaking Administrative Groups Inter-area Traffic Engineered LSPs Traffic Protection and LSP Optimization Default Traffic Protection Behavior Primary and Secondary LSPs Fast Reroute RSVP Link Protection LDP LFA and Link Protection LSP Optimization Fate Sharing Junos OS Fate Sharing SRLG Extended Admin Groups Miscellaneous MPLS Features Forwarding Adjacencies Policy Control over LSP Selection LSP Metrics Automatic Bandwidth Container LSPs TTL Handling Explicit Null Configuration MPLS Pings

Duration 2 Days 12 CPD hours This course is intended for This course benefits individuals responsible for configuring and monitoring devices running the Junos OS. Course Level : Junos Layer 2 VPNs (JL2V) is an advanced-level course. Overview Define the term virtual private network. Describe the business drivers for MPLS VPNs. Describe the differences between Layer 2 VPNs and Layer 3 VPNs. List advantages for the use of MPLS Layer 3 VPNs and Layer 2 VPNs. Describe the roles of a CE device, PE router, and P router in a BGP Layer 2 VPN. Explain the flow of control traffic and data traffic for a BGP Layer 2 VPN. Configure a BGP Layer 2 VPN and describe the benefits and requirements of over-provisioning. Monitor and troubleshoot a BGP Layer 2 VPN. Explain the BGP Layer 2 VPN scaling mechanisms and route reflection. Describe the Junos OS BGP Layer 2 VPN CoS support. Describe the flow of control and data traffic for an LDP Layer 2 circuit. Configure an LDP Layer 2 circuit. Monitor and troubleshoot an LDP Layer 2 circuit. Describe the operation of FEC 129 BGP autodiscovery for Layer 2 VPNs. Configure a FEC 129 BGP autodiscovery Layer 2 VPN. Monitor and troubleshoot a FEC 129 BGP autodiscovery for Layer 2 VPNs. Describe the difference between Layer 2 MPLS VPNs and VPLS. Explain the purpose of the PE device, the CE device, and the P device. Explain the provisioning of CE and PE routers. Describe the signaling process of VPLS. Describe the learning and forwarding process of VPLS. Describe the potential loops in a VPLS environment. Configure BGP, LDP, and FEC 129 BGP autodiscovery VPLS. Troubleshoot VPLS. Describe the purpose and features of Ethernet VPN. Configure Ethernet VPN. Monitor and troubleshoot Ethernet VPN. Describe the Junos OS support for hierarchical VPN models. Describe the Junos OS support for Carrier-of-Carriers VPN Option C. Configure the interprovider VPN Option C. Describe the Junos OS support for multisegment pseudowire for FEC 129. Describe and configure circuit cross-connect (CCC). This two-day course is designed to provide students with MPLS-based Layer 2 virtual private network (VPN) knowledge and configuration examples. Course IntroductionMPLS VPNs MPLS VPNs Provider-Provisioned VPNs BGP Layer 2 VPNs Overview of Layer 2 Provider-Provisioned VPNs BGP Layer 2 VPN Operational Model: Control Plane BGP Layer 2 VPN Operational Model: Data Plane Preliminary BGP Layer 2 VPN Configuration BGP Layer 2 Configuration Monitoring and Troubleshooting BGP Layer 2 VPNs Lab: BGP Layer 2 VPNs Layer 2 VPN Scaling and CoS Review of VPN Scaling Mechanisms Layer 2 VPNs and CoS LDP Layer 2 Circuits LDP Layer 2 Circuit Operation LDP Layer 2 Circuit Configuration LDP Layer 2 Circuit Monitoring and Troubleshooting FEC 129 BGP Autodiscovery Layer 2 Circuit Operation FEC 129 BGP Autodiscovery Layer 2 Circuit Configuration FEC 129 BGP Autodiscovery Monitoring and Troubleshooting Virtual Private LAN Services Layer 2 MPLS VPNs Versus VPLS BGP VPLS Control Plane BGP VPLS Data Plane Learning and Forwarding Process Loops VPLS Configuration VPLS Configuration VPLS Troubleshooting Ethernet VPN (EVPN) EVPN Overview EVPN Control Plane EVPN Operation EVPN Configuration EVPN Troubleshooting

Duration 3 Days 18 CPD hours This course is intended for This course is geared for attendees with solid Python skills who wish to learn and use basic machine learning algorithms and concepts Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below Getting Started & Optional Python Quick Refresher Statistics and Probability Refresher and Python Practice Probability Density Function; Probability Mass Function; Naive Bayes Predictive Models Machine Learning with Python Recommender Systems KNN and PCA Reinforcement Learning Dealing with Real-World Data Experimental Design / ML in the Real World Time Permitting: Deep Learning and Neural Networks Machine Learning Essentials with Python is a foundation-level, three-day hands-on course that teaches students core skills and concepts in modern machine learning practices. This course is geared for attendees experienced with Python, but new to machine learning, who need introductory level coverage of these topics, rather than a deep dive of the math and statistics behind Machine Learning. Students will learn basic algorithms from scratch. For each machine learning concept, students will first learn about and discuss the foundations, its applicability and limitations, and then explore the implementation and use, reviewing and working with specific use casesWorking in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:Popular machine learning algorithms, their applicability and limitationsPractical application of these methods in a machine learning environmentPractical use cases and limitations of algorithms Getting Started Installation: Getting Started and Overview LINUX jump start: Installing and Using Anaconda & Course Materials (or reference the default container) Python Refresher Introducing the Pandas, NumPy and Scikit-Learn Library Statistics and Probability Refresher and Python Practice Types of Data Mean, Median, Mode Using mean, median, and mode in Python Variation and Standard Deviation Probability Density Function; Probability Mass Function; Naive Bayes Common Data Distributions Percentiles and Moments A Crash Course in matplotlib Advanced Visualization with Seaborn Covariance and Correlation Conditional Probability Naive Bayes: Concepts Bayes? Theorem Naive Bayes Spam Classifier with Naive Bayes Predictive Models Linear Regression Polynomial Regression Multiple Regression, and Predicting Car Prices Logistic Regression Logistic Regression Machine Learning with Python Supervised vs. Unsupervised Learning, and Train/Test Using Train/Test to Prevent Overfitting Understanding a Confusion Matrix Measuring Classifiers (Precision, Recall, F1, AUC, ROC) K-Means Clustering K-Means: Clustering People Based on Age and Income Measuring Entropy LINUX: Installing GraphViz Decision Trees: Concepts Decision Trees: Predicting Hiring Decisions Ensemble Learning Support Vector Machines (SVM) Overview Using SVM to Cluster People using scikit-learn Recommender Systems User-Based Collaborative Filtering Item-Based Collaborative Filtering Finding Similar Movie Better Accuracy for Similar Movies Recommending movies to People Improving your recommendations KNN and PCA K-Nearest-Neighbors: Concepts Using KNN to Predict a Rating for a Movie Dimensionality Reduction; Principal Component Analysis (PCA) PCA with the Iris Data Set Reinforcement Learning Reinforcement Learning with Q-Learning and Gym Dealing with Real-World Data Bias / Variance Tradeoff K-Fold Cross-Validation Data Cleaning and Normalization Cleaning Web Log Data Normalizing Numerical Data Detecting Outliers Feature Engineering and the Curse of Dimensionality Imputation Techniques for Missing Data Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE Binning, Transforming, Encoding, Scaling, and Shuffling Experimental Design / ML in the Real World Deploying Models to Real-Time Systems A/B Testing Concepts T-Tests and P-Values Hands-on With T-Tests Determining How Long to Run an Experiment A/B Test Gotchas Capstone Project Group Project & Presentation or Review Deep Learning and Neural Networks Deep Learning Prerequisites The History of Artificial Neural Networks Deep Learning in the TensorFlow Playground Deep Learning Details Introducing TensorFlow Using TensorFlow Introducing Keras Using Keras to Predict Political Affiliations Convolutional Neural Networks (CNN?s) Using CNN?s for Handwriting Recognition Recurrent Neural Networks (RNN?s) Using an RNN for Sentiment Analysis Transfer Learning Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters Deep Learning Regularization with Dropout and Early Stopping The Ethics of Deep Learning Learning More about Deep Learning Additional course details: Nexus Humans Machine Learning Essentials with Python (TTML5506-P) 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 Machine Learning Essentials with Python (TTML5506-P) 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 1 Days 6 CPD hours This course is intended for This course is intended for: Developers responsible for developing Deep Learning applications Developers who want to understand concepts behind Deep Learning and how to implement a Deep Learning solution on AWS Overview This course is designed to teach you how to: Define machine learning (ML) and deep learning Identify the concepts in a deep learning ecosystem Use Amazon SageMaker and the MXNet programming framework for deep learning workloads Fit AWS solutions for deep learning deployments In this course, you?ll learn about AWS?s deep learning solutions, including scenarios where deep learning makes sense and how deep learning works. You?ll learn how to run deep learning models on the cloud using Amazon SageMaker and the MXNet framework. You?ll also learn to deploy your deep learning models using services like AWS Lambda while designing intelligent systems on AWS. Module 1: Machine learning overview A brief history of AI, ML, and DL The business importance of ML Common challenges in ML Different types of ML problems and tasks AI on AWS Module 2: Introduction to deep learning Introduction to DL The DL concepts A summary of how to train DL models on AWS Introduction to Amazon SageMaker Hands-on lab: Spinning up an Amazon SageMaker notebook instance and running a multi-layer perceptron neural network model Module 3: Introduction to Apache MXNet The motivation for and benefits of using MXNet and Gluon Important terms and APIs used in MXNet Convolutional neural networks (CNN) architecture Hands-on lab: Training a CNN on a CIFAR-10 dataset Module 4: ML and DL architectures on AWS AWS services for deploying DL models (AWS Lambda, AWS IoT Greengrass, Amazon ECS, AWS Elastic Beanstalk) Introduction to AWS AI services that are based on DL (Amazon Polly, Amazon Lex, Amazon Rekognition) Hands-on lab: Deploying a trained model for prediction on AWS Lambda Additional course details: Nexus Humans Deep Learning 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 Deep Learning 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.