31 Forest courses in Nottingham delivered Live Online

Duration 4 Days 24 CPD hours This course is intended for This four-day course is intended for Windows Server Hybrid Administrators who have experience working with Windows Server and want to extend the capabilities of their on-premises environments by combining on-premises and hybrid technologies. Windows Server Hybrid Administrators implement and manage on-premises and hybrid solutions such as identity, management, compute, networking, and storage in a Windows Server hybrid environment. This course teaches IT Professionals how to manage core Windows Server workloads and services using on-premises, hybrid, and cloud technologies. The course teaches IT Professionals how to implement and manage on-premises and hybrid solutions such as identity, management, compute, networking, and storage in a Windows Server hybrid environment. Prerequisites Experience with managing Windows Server operating system and Windows Server workloads in on-premises scenarios, including AD DS, DNS, DFS, Hyper-V, and File and Storage Services Experience with common Windows Server management tools (implied in the first prerequisite). Basic knowledge of core Microsoft compute, storage, networking, and virtualization technologies (implied in the first prerequisite). Experience and an understanding of core networking technologies such as IP addressing, name resolution, and Dynamic Host Configuration Protocol (DHCP) Experience working with and an understanding of Microsoft Hyper-V and basic server virtualization concepts Basic experience with implementing and managing IaaS services in Microsoft Azure Basic knowledge of Azure Active Directory Experience working hands-on with Windows client operating systems such as Windows 10 or Windows 11 Basic experience with Windows PowerShell 1 - Introduction to AD DS Define AD DS Define users, groups, and computers Define AD DS forests and domains Define OUs Manage objects and their properties in AD DS 2 - Manage AD DS domain controllers and FSMO roles Deploy AD DS domain controllers Maintain AD DS domain controllers Manage the AD DS Global Catalog role Manage AD DS operations masters Manage AD DS schema 3 - Implement Group Policy Objects Define GPOs Implement GPO scope and inheritance Define domain-based GPOs Create and configure a domain-based GPO Define GPO storage Define administrative templates 4 - Manage advanced features of AD DS Create trust relationships Implement ESAE forests Monitor and troubleshoot AD DS Create custom AD DS partitions 5 - Implement hybrid identity with Windows Server Select a Microsoft Entra integration model Plan for Microsoft Entra integration Prepare on-premises Active Directory for directory synchronization Install and configure directory synchronization with Microsoft Entra Connect Implement Seamless Single Sign-On Enable Microsoft Entra login in for Windows VM in Azure Describe Microsoft Entra Domain Services Implement and configure Microsoft Entra Domain Services Manage Windows Server 2019 in a Microsoft Entra Domain Services environment Create and configure a Microsoft Entra Domain Services instance Join a Windows Server VM to a managed domain 6 - Deploy and manage Azure IaaS Active Directory domain controllers in Azure Select an option to implement directory and identity services using Active Directory Domain Services in Azure Deploy and configure Active Directory Domain Services domain controllers in Azure VMs Install a replica Active Directory domain controller in an Azure VM Install a new Active Directory forest on an Azure VNet 7 - Perform Windows Server secure administration Define least privilege administration Implement delegated privileges Use privileged access workstations Use jump servers 8 - Describe Windows Server administration tools Explore Windows Admin Center Use Server Manager List Remote Server Administration Tools Use Windows PowerShell Use Windows PowerShell to remotely administer a server 9 - Perform post-installation configuration of Windows Server List the available post-installation configuration tools Configure Server Core using Sconfig Use DSC to configure Windows Server Perform post-installation configuration with Windows Admin Center Configure a server with answer files 10 - Just Enough Administration in Windows Server Explain the concept of Just Enough Administration (JEA) Define role capabilities for a JEA endpoint Create a session configuration file to register a JEA endpoint Describe how JEA endpoints work to limit access to a PowerShell session Create and connect to a JEA endpoint Demonstration: Connect to a JEA endpoint 11 - Administer and manage Windows Server IaaS Virtual Machine remotely Select the appropriate remote administration tool Manage Windows Virtual Machines with Azure Bastion Create an Azure Bastion host Configure just-in-time administration 12 - Manage hybrid workloads with Azure Arc Describe Azure Arc Onboard Windows Server instances Connect hybrid machines to Azure from the Azure portal Use Azure Arc to manage Windows Server instances Restrict access with RBAC 13 - Configure and manage Hyper-V Define Hyper-V Define Hyper-V Manager Configure Hyper-V hosts using best practices Configure Hyper-V networking Assess advanced Hyper-V networking features Define nested virtualization 14 - Configure and manage Hyper-V virtual machines List the virtual machine configuration versions List the virtual machine generation versions List available VHD formats and types Create and configure VMs Determine storage options for VMs Define shared VHDs and VHD Sets Implement guest clusters using shared VHDX 15 - Secure Hyper-V workloads Define guarded fabric Define the Host Guardian Service Explore TPM-trusted attestation Define KPS Determine key features of shielded VMs Compare encryption-supported and shielded VMs in a guarded fabric Implement a shielded VM 16 - Run containers on Windows Server Define containers List the differences between containers and VMs Define Windows Server and Hyper-V containers and isolation modes Explore Docker Prepare a Windows Server 2019 host for container deployment Security, Storage, and Networking with Windows containers 17 - Orchestrate containers on Windows Server using Kubernetes Define orchestration Define Kubernetes Deploy Kubernetes resources Create a Kubernetes cluster on Windows Define Azure Arc Connect an Azure Arc-enabled Kubernetes cluster to Azure Arc 18 - Plan and deploy Windows Server IaaS Virtual Machines Describe Azure compute Describe Virtual Machine storage Deploy Azure Virtual Machines Create a windows Virtual Machine using the portal Create a windows Virtual Machine using Azure CLI Deploy Azure Virtual Machines using templates Describe additional management optimization options 19 - Customize Windows Server IaaS Virtual Machine images Create a generalized image Create a new Virtual Machine from a managed image Create a managed image of a generalized virtual machine in Azure Create a Virtual Machine from a managed image Implement Azure Image Builder Create a windows Virtual Machine using Azure Image Builder template Create a Windows Virtual Machine with Azure Image Builder using PowerShell 20 - Automate the configuration of Windows Server IaaS Virtual Machines Describe Azure Automation Implement Azure Automation with DSC Remediate noncompliant servers Describe Custom Script Extensions Configure a Virtual Machine by using DSC 21 - Deploy and manage DHCP Use DHCP to simplify IP configuration Install and configure the DHCP role Configure DHCP options Configure DHCP scopes Select DHCP high availability options Implement DHCP Failover 22 - Implement Windows Server DNS Explore the DNS architecture Work with DNS zones and records Install and configure the DNS role Implement DNS forwarding 23 - Implement IP Address Management Define IP Address Management Deploy IP Address Management Administer IP Address Management Configure IP Address Management options Manage DNS zones with IP Address Management Manage DHCP servers with IP Address Management Use IP Address Management to manage IP addressing 24 - Implement remote access Examine the remote access options in Windows Server Select and set up VPNs Use NPS to create and enforce network access policies Plan and implement NPS Deploy a PKI for remote access Use WAP as a reverse web proxy 25 - Implement hybrid network infrastructure Describe Azure network topologies Implement Azure VPN options Create a route-based VPN gateway using the Azure portal Implement Azure ExpressRoute Configure Azure Virtual WAN Implement DNS in hybrid environments 26 - Implement DNS for Windows Server IaaS VMs Understand Azure DNS Implement Azure DNS Create an Azure DNS zone and record using the Azure portal Implement DNS with Azure IaaS virtual machines Implement split-horizon DNS in Azure Troubleshoot DNS 27 - Implement Windows Server IaaS VM IP addressing and routing Implement a virtual network Implement IaaS VM IP addressing Assign and manage IP addresses Configure a private IP address for a virtual machine using the Azure portal Create a virtual machine with a static public IP address using the Azure portal Implement IaaS virtual machine IP routing Implement IPv6 for Windows Server IaaS virtual machines 28 - Manage Windows Server file servers Define the Windows Server file system List the benefits and uses of File Server Resource Manager Define SMB and its security considerations Configure SMB protocol Define Volume Shadow Copy Service 29 - Implement Storage Spaces and Storage Spaces Direct Define the Storage Spaces architecture and its components List the functionalities, benefits, and use cases of Storage Spaces Implement Storage Spaces List the functionalities, components, benefits, and use cases of Storage Spaces Direct Implement Storage Spaces Direct 30 - Implement Windows Server Data Deduplication Define the architecture, components, and functionality of Data Deduplication Define the use cases and interoperability of Data Deduplication Implement Data Deduplication Manage and maintain Data Deduplication 31 - Implement Windows Server iSCSI List the functionalities, components, and use cases of iSCSI List the considerations for implementing iSCSI Implement iSCSI Configure high availability for iSCSI 32 - Implement Windows Server Storage Replica List the functionalities and components of Storage Replica Examine the prerequisites for implementing Storage Replica Implement Storage Replica by using Windows Admin Center Implement Storage Replica by using Windows PowerShell 33 - Implement a hybrid file server infrastructure Describe Azure File services Configure Azure Files Configure connectivity to Azure Files Describe Azure File Sync Implement Azure File Sync Deploy Azure File Sync Deploy Azure File Sync 2 Manage cloud tiering Migrate from DFSR to Azure File Sync

The HoardingUK National Hoarding Conference is back in 2024! This year we're looking at what IS working. Our expert panel will include housing, environmental health, fire service, social care and other relevant professionals. Attend to hear how we've jointly overcome hurdles to deliver a successful, integrated, cost-effective, time managed programme.

Duration 3 Days 18 CPD hours This course is intended for Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary. Overview By the end of this course, you will be able to build your own marketing reporting and interactive dashboard solutions. The course starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation.As you make your way through the course, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding sections, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices. Data Preparation and Cleaning Data Models and Structured Data pandas Data Manipulation Data Exploration and Visualization Identifying the Right Attributes Generating Targeted Insights Visualizing Data Unsupervised Learning: Customer Segmentation Customer Segmentation Methods Similarity and Data Standardization k-means Clustering Choosing the Best Segmentation Approach Choosing the Number of Clusters Different Methods of Clustering Evaluating Clustering Predicting Customer Revenue Using Linear Regression Understanding Regression Feature Engineering for Regression Performing and Interpreting Linear Regression Other Regression Techniques and Tools for Evaluation Evaluating the Accuracy of a Regression Model Using Regularization for Feature Selection Tree-Based Regression Models Supervised Learning: Predicting Customer Churn Classification Problems Understanding Logistic Regression Creating a Data Science Pipeline Fine-Tuning Classification Algorithms Support Vector Machine Decision Trees Random Forest Preprocessing Data for Machine Learning Models Model Evaluation Performance Metrics Modeling Customer Choice Understanding Multiclass Classification Class Imbalanced Data Additional course details: Nexus Humans Data Science for Marketing Analytics 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 Data Science for Marketing Analytics 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.

This offering is for an initial session exploring your enneagram map report , which is included. The test I use is from Aephoria, and it will give you your tritype (your strategies for thinking, feeling and doing) as well as your instinctual variant, which is helpful information about where you tend to focus your attention. Once you have your map, we will unpack the information together, and if you wish, go on to explore it over a series of sessions, using creative tools and practices to find its meaning and wisdom for you in your life.

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

Duration 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. Python for Data Science ? Using Modules ? Listing Methods in a Module ? Creating Your Own Modules ? List Comprehension ? Dictionary Comprehension ? String Comprehension ? Python 2 vs Python 3 ? Sets (Python 3+) ? Python Idioms ? Python Data Science ?Ecosystem? ? NumPy ? NumPy Arrays ? NumPy Idioms ? pandas ? Data Wrangling with pandas' DataFrame ? SciPy ? Scikit-learn ? SciPy or scikit-learn? ? Matplotlib ? Python vs R ? Python on Apache Spark ? Python Dev Tools and REPLs ? Anaconda ? IPython ? Visual Studio Code ? Jupyter ? Jupyter Basic Commands ? Summary Applied Data Science ? What is Data Science? ? Data Science Ecosystem ? Data Mining vs. Data Science ? Business Analytics vs. Data Science ? Data Science, Machine Learning, AI? ? Who is a Data Scientist? ? Data Science Skill Sets Venn Diagram ? Data Scientists at Work ? Examples of Data Science Projects ? An Example of a Data Product ? Applied Data Science at Google ? Data Science Gotchas ? Summary Data Analytics Life-cycle Phases ? Big Data Analytics Pipeline ? Data Discovery Phase ? Data Harvesting Phase ? Data Priming Phase ? Data Logistics and Data Governance ? Exploratory Data Analysis ? Model Planning Phase ? Model Building Phase ? Communicating the Results ? Production Roll-out ? Summary Repairing and Normalizing Data ? Repairing and Normalizing Data ? Dealing with the Missing Data ? Sample Data Set ? Getting Info on Null Data ? Dropping a Column ? Interpolating Missing Data in pandas ? Replacing the Missing Values with the Mean Value ? Scaling (Normalizing) the Data ? Data Preprocessing with scikit-learn ? Scaling with the scale() Function ? The MinMaxScaler Object ? Summary Descriptive Statistics Computing Features in Python ? Descriptive Statistics ? Non-uniformity of a Probability Distribution ? Using NumPy for Calculating Descriptive Statistics Measures ? Finding Min and Max in NumPy ? Using pandas for Calculating Descriptive Statistics Measures ? Correlation ? Regression and Correlation ? Covariance ? Getting Pairwise Correlation and Covariance Measures ? Finding Min and Max in pandas DataFrame ? Summary Data Aggregation and Grouping ? Data Aggregation and Grouping ? Sample Data Set ? The pandas.core.groupby.SeriesGroupBy Object ? Grouping by Two or More Columns ? Emulating the SQL's WHERE Clause ? The Pivot Tables ? Cross-Tabulation ? Summary Data Visualization with matplotlib ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary Data Science and ML Algorithms in scikit-learn ? Data Science, Machine Learning, AI? ? Types of Machine Learning ? Terminology: Features and Observations ? Continuous and Categorical Features (Variables) ? Terminology: Axis ? The scikit-learn Package ? scikit-learn Estimators ? Models, Estimators, and Predictors ? Common Distance Metrics ? The Euclidean Metric ? The LIBSVM format ? Scaling of the Features ? The Curse of Dimensionality ? Supervised vs Unsupervised Machine Learning ? Supervised Machine Learning Algorithms ? Unsupervised Machine Learning Algorithms ? Choose the Right Algorithm ? Life-cycles of Machine Learning Development ? Data Split for Training and Test Data Sets ? Data Splitting in scikit-learn ? Hands-on Exercise ? Classification Examples ? Classifying with k-Nearest Neighbors (SL) ? k-Nearest Neighbors Algorithm ? k-Nearest Neighbors Algorithm ? The Error Rate ? Hands-on Exercise ? Dimensionality Reduction ? The Advantages of Dimensionality Reduction ? Principal component analysis (PCA) ? Hands-on Exercise ? Data Blending ? Decision Trees (SL) ? Decision Tree Terminology ? Decision Tree Classification in Context of Information Theory ? Information Entropy Defined ? The Shannon Entropy Formula ? The Simplified Decision Tree Algorithm ? Using Decision Trees ? Random Forests ? SVM ? Naive Bayes Classifier (SL) ? Naive Bayesian Probabilistic Model in a Nutshell ? Bayes Formula ? Classification of Documents with Naive Bayes ? Unsupervised Learning Type: Clustering ? Clustering Examples ? k-Means Clustering (UL) ? k-Means Clustering in a Nutshell ? k-Means Characteristics ? Regression Analysis ? Simple Linear Regression Model ? Linear vs Non-Linear Regression ? Linear Regression Illustration ? Major Underlying Assumptions for Regression Analysis ? Least-Squares Method (LSM) ? Locally Weighted Linear Regression ? Regression Models in Excel ? Multiple Regression Analysis ? Logistic Regression ? Regression vs Classification ? Time-Series Analysis ? Decomposing Time-Series ? Summary Lab Exercises Lab 1 - Learning the Lab Environment Lab 2 - Using Jupyter Notebook Lab 3 - Repairing and Normalizing Data Lab 4 - Computing Descriptive Statistics Lab 5 - Data Grouping and Aggregation Lab 6 - Data Visualization with matplotlib Lab 7 - Data Splitting Lab 8 - k-Nearest Neighbors Algorithm Lab 9 - The k-means Algorithm Lab 10 - The Random Forest Algorithm

Microsoft Active Directory course description A thorough understanding of this system is essential for anyone managing enterprise MS networks. Essential theory is complimented with a high level of hands on practice allowing delegates to observe the idiosyncrasies of Active Directory and Group Policy at first hand. Delegates learn the fundamental theory of AD and progress onto building a multi-domain network in the classroom. The course includes troubleshooting methods, and essential maintenance procedures. This course is designed to teach you the skills needed for day to day management of these technologies. What will you learn Install AD on multiple PCs. Use the tools to create and manage objects. Create appropriate group policies to restrict selected user's desktops and network access. Install DNS to support Active Directory without loosing Internet Connectivity. Maintain and troubleshoot AD problems Backup Active Directory. Microsoft Active Directory course details Who will benefit: Technical staff working with AD based networks. Prerequisites: Supporting Windows server. Duration 3 days Microsoft Active Directory course contents Introduction to Active Directory Network authentication methods, Active Directory defined, AD naming conventions, network management with AD, AD structures: Domains, Organisational Units, Forests & Trees, Sites, The Global Catalogue. Windows 2003 new features, installing AD. Hands on Installing an AD network. Windows overview Management methods Server management tools, installing the additional tools, Terminal Server: Administration mode, Administrator accounts in AD, Local Security Policy. Hands on Install the management tools, Management using Terminal Services. Creating & Managing Objects (a quick look) AD management tools, AD users and computers, Creating & managing OUs, User Accounts and groups, controlling access to AD objects, moving objects, Publishing resources, locating objects in AD, delegating authority. Hands on Creating a control OU structure and delegating authority. Introduction to Group Policies What are Group Policies? Where Group Policy data is stored, security, Group Policy flow. Hands on Implementing Group Policies Working with Group Policies Local security templates, administrative templates, scripts, folder redirection, software deployment. Hands on Scripts, redirecting the start menu, creating a secure, robust desktop environment. Implementing DNS DNS basics, troubleshooting, implementing DNS zones. Hands on Building a unified DNS solution. Maintaining and managing the AD database AD support tools, database internal structure, replication, replication tools, Single Operations Masters, tools for maintenance, maintenance techniques, Backing up AD, Directory Services restore mode, NTDSUtil, Authoritative & non-authoritative restoration, rebuilding. Hands on NTDSUtil.

This course will create insight about carbon,carbon emission, Green House Gases ( GHG's) and the voluntary carbon market. It will enable learners understand the concept of climate change, as well as nature based solutions to mitigate climate change

If you would like to spend some time exploring in imaginal forest together, and listen to what the creatures of the forest have to say to you, and to younger parts of you, then this offering is MADE for you!

Transform your relationship with nature through the Wild Finca Online Rewilding Retreat. Over the course of two weeks, embark on a journey designed to deepen your understanding of the natural world, inspire personal growth, and provide practical steps for integrating rewilding practices into your daily life. Be among the first to experience this unique and innovative retreat. With limited spots available, don’t miss the opportunity to embrace a harmonious lifestyle with nature. Begin your journey towards a more connected existence today.