1735 Mode courses in Cardiff delivered Live Online

Duration 4 Days 24 CPD hours This course is intended for The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview Overview of data science and machine learning at scale Overview of the Hadoop ecosystem Working with HDFS data and Hive tables using Hue Introduction to Cloudera Data Science Workbench Overview of Apache Spark 2 Reading and writing data Inspecting data quality Cleansing and transforming data Summarizing and grouping data Combining, splitting, and reshaping data Exploring data Configuring, monitoring, and troubleshooting Spark applications Overview of machine learning in Spark MLlib Extracting, transforming, and selecting features Building and evaluating regression models Building and evaluating classification models Building and evaluating clustering models Cross-validating models and tuning hyperparameters Building machine learning pipelines Deploying machine learning models Spark, Spark SQL, and Spark MLlib PySpark and sparklyr Cloudera Data Science Workbench (CDSW) Hue This workshop covers data science and machine learning workflows at scale using Apache Spark 2 and other key components of the Hadoop ecosystem. The workshop emphasizes the use of data science and machine learning methods to address real-world business challenges. Using scenarios and datasets from a fictional technology company, students discover insights to support critical business decisions and develop data products to transform the business. The material is presented through a sequence of brief lectures, interactive demonstrations, extensive hands-on exercises, and discussions. The Apache Spark demonstrations and exercises are conducted in Python (with PySpark) and R (with sparklyr) using the Cloudera Data Science Workbench (CDSW) environment. The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview of data science and machine learning at scaleOverview of the Hadoop ecosystemWorking with HDFS data and Hive tables using HueIntroduction to Cloudera Data Science WorkbenchOverview of Apache Spark 2Reading and writing dataInspecting data qualityCleansing and transforming dataSummarizing and grouping dataCombining, splitting, and reshaping dataExploring dataConfiguring, monitoring, and troubleshooting Spark applicationsOverview of machine learning in Spark MLlibExtracting, transforming, and selecting featuresBuilding and evauating regression modelsBuilding and evaluating classification modelsBuilding and evaluating clustering modelsCross-validating models and tuning hyperparametersBuilding machine learning pipelinesDeploying machine learning models Additional course details: Nexus Humans Cloudera Data Scientist Training 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 Cloudera Data Scientist Training 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 class is intended for experienced developers who are responsible for managing big data transformations including: Extracting, loading, transforming, cleaning, and validating data. Designing pipelines and architectures for data processing. Creating and maintaining machine learning and statistical models. Querying datasets, visualizing query results and creating reports Overview Design and build data processing systems on Google Cloud Platform. Leverage unstructured data using Spark and ML APIs on Cloud Dataproc. Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow. Derive business insights from extremely large datasets using Google BigQuery. Train, evaluate and predict using machine learning models using TensorFlow and Cloud ML. Enable instant insights from streaming data Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hand-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data. Introduction to Data Engineering Explore the role of a data engineer. Analyze data engineering challenges. Intro to BigQuery. Data Lakes and Data Warehouses. Demo: Federated Queries with BigQuery. Transactional Databases vs Data Warehouses. Website Demo: Finding PII in your dataset with DLP API. Partner effectively with other data teams. Manage data access and governance. Build production-ready pipelines. Review GCP customer case study. Lab: Analyzing Data with BigQuery. Building a Data Lake Introduction to Data Lakes. Data Storage and ETL options on GCP. Building a Data Lake using Cloud Storage. Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions. Securing Cloud Storage. Storing All Sorts of Data Types. Video Demo: Running federated queries on Parquet and ORC files in BigQuery. Cloud SQL as a relational Data Lake. Lab: Loading Taxi Data into Cloud SQL. Building a Data Warehouse The modern data warehouse. Intro to BigQuery. Demo: Query TB+ of data in seconds. Getting Started. Loading Data. Video Demo: Querying Cloud SQL from BigQuery. Lab: Loading Data into BigQuery. Exploring Schemas. Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA. Schema Design. Nested and Repeated Fields. Demo: Nested and repeated fields in BigQuery. Lab: Working with JSON and Array data in BigQuery. Optimizing with Partitioning and Clustering. Demo: Partitioned and Clustered Tables in BigQuery. Preview: Transforming Batch and Streaming Data. Introduction to Building Batch Data Pipelines EL, ELT, ETL. Quality considerations. How to carry out operations in BigQuery. Demo: ELT to improve data quality in BigQuery. Shortcomings. ETL to solve data quality issues. Executing Spark on Cloud Dataproc The Hadoop ecosystem. Running Hadoop on Cloud Dataproc. GCS instead of HDFS. Optimizing Dataproc. Lab: Running Apache Spark jobs on Cloud Dataproc. Serverless Data Processing with Cloud Dataflow Cloud Dataflow. Why customers value Dataflow. Dataflow Pipelines. Lab: A Simple Dataflow Pipeline (Python/Java). Lab: MapReduce in Dataflow (Python/Java). Lab: Side Inputs (Python/Java). Dataflow Templates. Dataflow SQL. Manage Data Pipelines with Cloud Data Fusion and Cloud Composer Building Batch Data Pipelines visually with Cloud Data Fusion. Components. UI Overview. Building a Pipeline. Exploring Data using Wrangler. Lab: Building and executing a pipeline graph in Cloud Data Fusion. Orchestrating work between GCP services with Cloud Composer. Apache Airflow Environment. DAGs and Operators. Workflow Scheduling. Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery. Monitoring and Logging. Lab: An Introduction to Cloud Composer. Introduction to Processing Streaming Data Processing Streaming Data. Serverless Messaging with Cloud Pub/Sub Cloud Pub/Sub. Lab: Publish Streaming Data into Pub/Sub. Cloud Dataflow Streaming Features Cloud Dataflow Streaming Features. Lab: Streaming Data Pipelines. High-Throughput BigQuery and Bigtable Streaming Features BigQuery Streaming Features. Lab: Streaming Analytics and Dashboards. Cloud Bigtable. Lab: Streaming Data Pipelines into Bigtable. Advanced BigQuery Functionality and Performance Analytic Window Functions. Using With Clauses. GIS Functions. Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz. Performance Considerations. Lab: Optimizing your BigQuery Queries for Performance. Optional Lab: Creating Date-Partitioned Tables in BigQuery. Introduction to Analytics and AI What is AI?. From Ad-hoc Data Analysis to Data Driven Decisions. Options for ML models on GCP. Prebuilt ML model APIs for Unstructured Data Unstructured Data is Hard. ML APIs for Enriching Data. Lab: Using the Natural Language API to Classify Unstructured Text. Big Data Analytics with Cloud AI Platform Notebooks What's a Notebook. BigQuery Magic and Ties to Pandas. Lab: BigQuery in Jupyter Labs on AI Platform. Production ML Pipelines with Kubeflow Ways to do ML on GCP. Kubeflow. AI Hub. Lab: Running AI models on Kubeflow. Custom Model building with SQL in BigQuery ML BigQuery ML for Quick Model Building. Demo: Train a model with BigQuery ML to predict NYC taxi fares. Supported Models. Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML. Lab Option 2: Movie Recommendations in BigQuery ML. Custom Model building with Cloud AutoML Why Auto ML? Auto ML Vision. Auto ML NLP. Auto ML Tables.

Duration 2 Days 12 CPD hours This course is intended for Data Modelers Participants will learn the full scope of the metadata modeling process, from initial project creation, to publishing a dynamic cube, and enabling end users to easily author reports and analyze data. Introduction to IBM Cognos Dynamic Cubes Define and differentiate Dynamic Cubes Dynamic Cubes characteristics Examine Dynamic Cube requirements Examine Dynamic Cube components Examine high level architecture IBM Cognos Dynamic Query Review Dimensional Data Structures Dynamic Cubes caching Create & Design a Dynamic Cube Explore the IBM Cognos Cube Designer Review the cube development process Examine the Automatic Cube Generation Manual development overview Create dimensions Model the cube Best practice for effective modeling Deploy & Configure a Dynamic Cube Deploy a cube Explore the Estimate Hardware Requirements Identify cube management tasks Examine Query Service administration Explore Dynamic Cube properties Schedule cube actions Use the DCAdmin comment line tool Advanced Dynamic Cube Modelling Examine advanced modeling concepts Explore modeling caveats Calculated measures and members Model Relative Time Explore the Current Period property Define period aggregation rules for measures Advanced Features of Cube Designer Examine multilingual support Examine ragged hierarchies and padding members Define Parent-Child Dimensions Refresh Metadata Import Framework Manager packages Filter measures and dimensions Optimize Performance with Aggregates Identify aggregates and aggregate tables In-memory aggregates Use Aggregate Advisor to identify aggregates User defined in-memory aggregates Optimize In-Memory Aggregates automatically Aggregate Advisor recommendations Monitor Dynamic Cube performance Model aggregates (automatically vs manually) Use Slicers to define aggregation partitions Define Security Overview of Dynamic Cube security Identify security filters The Security process - Three steps Examine security scope Identify scope rules Identify roles Capabilities and access permissions Cube security deep dive Model a Virtual Cube Explore virtual cubes Create the virtual cube Explore virtual cube objects Examine virtual measures and calculated members Currency conversion using virtual cubes Security on virtual cubes Introduction to IBM Cognos Analytics Define IBM Cognos Analytics Redefined Business Intelligence Self-service Navigate to content in IBM Cognos Analytics Interact with the user interface Model data with IBM Cognos Analytics IBM Cognos Analytics components Create reports Perform self-service with analysis and Dashboards IBM Cognos Analytics architecture (high level) IBM Cognos Analytics security Package / data source relationship Create Data modules Upload files Additional course details: Nexus Humans B6063 IBM Cognos Cube Designer - Design Dynamic Cubes (v11.0) 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 B6063 IBM Cognos Cube Designer - Design Dynamic Cubes (v11.0) 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 3 Days 18 CPD hours This course is intended for This course is intended for intermediate to advanced Business Analysts who are looking to improve their skills for eliciting, analyzing, documenting, validating, and communicating requirements. Overview Obtain a thorough understanding of the core responsibilities of the business analyst Understand the main professional associations and standards supporting business analysts in the industry Discuss and explore the components of each of the domains/knowledge areas that comprise the work of business analysis Recognize the importance of properly defining the business need prior to engaging in requirements activities Formulate a strong understanding of the concepts that comprise strategy analysis Obtain experience with identifying and analyzing stakeholders Decipher between project and product scope and successfully use models to communicate scope Thoroughly understand and identify the various requirements categories and be able to recognize requirements of various types Explore business rules analysis Understand the benefits of process modeling and the common modeling language of BPMN Discuss process models and how the techniques can capture details about the as-is/to-be environment Learn how to properly prepare and conduct interviews Explore the components of use cases Learn what it means to package requirements Obtain hands-on experience with a number of business analysis techniques and gain hands-on experience eliciting, defining, and writing requirements. This course provides students a clear understanding of all the facets of the business analysis role, including a thorough walkthrough of the various domain/knowledge areas that comprise the business analysis profession. Students are provided an opportunity to try their hand at several business analysis techniques to assist with improving their skills in stakeholder identification, scope definition, and analyzing, documenting, and modeling requirements. Introduction to Business Analysis What is business analysis Benefits and challenges of business analysis Project success factors A Closer Look at the Business Analyst Role Definition of a business analyst Responsibilities of a business analyst Importance of communication/collaboration BA role vs. PM role Project roles involved in requirements IIBA/PMI and the goals of a professional association Purpose for having a BA standard IIBA?s BABOK© Guide and PMI?s Practice Guide in Business Analysis Business analysis beyond project work Business analysis core concepts Business analysis perspectives IIBA and PMI certifications for business analysts Workshop: Choose Your Project Supporting the Project Portfolio (Enterprise Analysis) Define Strategy Analysis When to perform Strategy Analysis Components of Strategy Analysis Defining the business need Envisioning the Product and Project Defining business requirements The importance of stakeholders Stakeholder identification Tips for analyzing stakeholders Techniques for managing stakeholder lists Discussion: Who is involved in strategy analysis? Workshops: Define the Business Need, Write Business Requirements, and Identify Stakeholders Understanding and Defining Solution Scope Defining solution scope Techniques for defining solution scope Applying the brainstorming technique Project scope vs. Product scope Finding solution boundaries The Context Diagram Actors and key information Workshop: Draw a Context Diagram Understanding Requirements What is a requirement? Requirement types Business, Stakeholder, Solution, and Transition requirements Assumptions and constraints Business rules Taxonomy of business rules Decision tables How to write simple calculations Requirements vs. business rules Document requirements Workshop ? Document Requirements Elicitation and Process Modeling Why do we model processes? What is Business Process Management? Using a modeling notation ?As Is? vs. ?To Be? modeling Why use BPMN? Basic BPM notation Business Process Modeling ? A case study Business Process Realignment ?As Is? vs. ?To Be? activity diagrams Workshop: Create a Business Process Model Planning & Eliciting Requirements Interviewing ? what and why? Preparing for an effective interview Types of questions to ask Sequencing questions Active listening techniques Planning for elicitation Conducting the interview Establishing rapport Active Listening Feedback techniques Types of elicitation techniques Workshops: Planning for Elicitation and Conducting an Elicitation Session Use Case & User Story Analysis What is an Actor? Types of Actors How to ?find? Use Cases? Diagramming Use Cases Tips on naming Use Cases Explaining scenarios The use case template Components of a use case Scenario examples Best practices for writing Use Cases Scenarios and flows Alternate and exception flows Exercises: Drawing a Use Case Diagram, Writing the Main Success Scenario, and Writing Alternate and Exception Scenarios Analyzing & Documenting Requirements Requirements and Use Cases Non-Functional requirements User Interface Requirements UI Data Table Reporting requirements Data requirements Data accessibility requirements Characteristics of good requirements The business requirements document (BRD) BRD vs. Functional Requirements Specification Preparing the requirements package Requirements traceability Workshops: Develop a User Interface, Analyzing Requirements, and Tracing requirements Additional Resources Useful books and links on writing effective requirements

Duration 3 Days 18 CPD hours Overview The goal of this course is to enable technical students new to Cassandra to begin working with Cassandra in an optimal manner. Throughout the course students will learn to: Understand the Big Data needs that C* addresses Be familiar with the operation and structure of C* Be able to install and set up a C* database Use the C* tools, including cqlsh, nodetool, and ccm (Cassandra Cluster Manager) Be familiar with the C* architecture, and how a C* cluster is structured Understand how data is distributed and replicated in a C* cluster Understand core C* data modeling concepts, and use them to create well-structured data models Be familiar with the C* eventual consistency model and use it intelligently Be familiar with consistency mechanisms such as read repair and hinted handoff Understand and use CQL to create tables and query for data Know and use the CQL data types (numerical, textual, uuid, etc.) Be familiar with the various kinds of primary keys available (simple, compound, and composite primary keys) Be familiar with the C* write and read paths Understand C* deletion and compaction The Cassandra (C*) database is a massively scalable NoSQL database that provides high availability and fault tolerance, as well as linear scalability when adding new nodes to a cluster. It has many powerful capabilities, such as tunable and eventual consistency, that allow it to meet the needs of modern applications, but also introduce a new paradigm for data modeling that many organizations do not have the expertise to use in the best way.Introduction to Cassandra is a hands-on course designed to teach attendees the basics of how to create good data models with Cassandra. This technical course has a focus on the practical aspects of working with C*, and introduces essential concepts needed to understand Cassandra, including enough coverage of internal architecture to make good decisions. It is hands-on, with labs that provide experience in core functionality. Students will also explore CQL (Cassandra Query Language), as well as some of the ?anti-patterns? that lead to non-optimal C* data models and be ready to work on production systems involving Cassandra. Session 1: Cassandra Overview Why We Need Cassandra - Big Data Challenges vs RDBMS High level Cassandra Overview Cassandra Features Optional: Basic Cassandra Installation and Configuration Session 2: Cassandra Architecture and CQL Overview Cassandra Architecture Overview Cassandra Clusters and Rings Nodes and Virtual Nodes Data Replication in Cassandra Introduction to CQL Defining Tables with a Single Primary Key Using cqlsh for Interactive Querying Selecting and Inserting/Upserting Data with CQL Data Replication and Distribution Basic Data Types (including uuid, timeuuid) Session 3: Data Modeling and CQL Core Concepts Defining a Compound Primary Key CQL for Compound Primary Keys Partition Keys and Data Distribution Clustering Columns Overview of Internal Data Organization Overview of Other Querying Capabilities ORDER BY, CLUSTERING ORDER BY, UPDATE , DELETE, ALLOW FILTERING Batch Queries Data Modeling Guidelines Denormalization Data Modeling Workflow Data Modeling Principles Primary Key Considerations Composite Partition Keys Defining with CQL Data Distribution with Composite Partition Key Overview of Internal Data Organization Session 4: Additional CQL Capabilities Indexing Primary/Partition Keys and Pagination with token() Secondary Indexes and Usage Guidelines Cassandra collections Collection Structure and Uses Defining and Querying Collections (set, list, and map) Materialized View Overview Usage Guidelines Session 5: Data Consistency In Cassandra Overview of Consistency in Cassandra CAP Theorem Eventual (Tunable) Consistency in C* - ONE, QUORUM, ALL Choosing CL ONE Choosing CL QUORUM Achieving Immediate Consistency Overview of Other Consistency Levels Supportive Consistency Mechanisms Writing / Hinted Handoff Read Repair Nodetool repair Session 6: Internal Mechanisms Ring Details Partitioners Gossip Protocol Snitches Write Path Overview / Commit Log Memtables and SSTables Write Failure Unavailable Nodes and Node Failure Requirements for Write Operations Read Path Overview Read Mechanism Replication and Caching Deletion/Compaction Overview Delete Mechanism Tombstones and Compaction Session 7: Working with IntelliJ Configuring JDBC Data Source for Cassandra Reading Schema Information Querying and Editing Tables. Additional course details: Nexus Humans Introduction to Cassandra (TTDS6776) 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 Introduction to Cassandra (TTDS6776) 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 3 Days 18 CPD hours This course is intended for Attendee roles might include: Software Developers/Programmers Data Scientists Machine Learning Engineers AI Researchers User Interface (UI) and User Experience (UX) Designers Technical Product Managers Technical Team Leads Overview Working in an interactive learning environment, led by our engaging AI expert you'll: Develop a strong foundational understanding of generative AI techniques and their applications in software development. Gain hands-on experience working with popular generative AI models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer models. Master the use of leading AI libraries and frameworks, such as TensorFlow, Keras, and Hugging Face Transformers, for implementing generative AI models. Acquire the skills to design, train, optimize, and evaluate custom generative AI models tailored to specific software development tasks. Learn to fine-tune pre-trained generative AI models for targeted applications and deploy them effectively in various environments, including cloud-based services and on-premises servers. Understand and address the ethical, legal, and safety considerations of using generative AI, including mitigating biases and ensuring responsible AI-generated content. Prompt Engineering offers coders and software developers a competitive edge by empowering them to develop more effective and efficient AI-driven solutions in their projects. By harnessing the capabilities of cutting-edge AI models like GPT-4, coders can automate repetitive tasks, enhance natural language understanding, and even generate code suggestions, boosting productivity and creativity. In addition, mastering prompt engineering can contribute to improved job security, as professionals with these in-demand skills are highly sought after in the rapidly evolving tech landscape. Quick Start to Prompt Engineering for Coders and Software Developers is a one day course designed to get you quickly up and running with the prompting skills required to out AI to work for you in your development efforts. Guided by our AI expert, you?ll explore key topics such as text preprocessing, data cleansing, GPT-4 tokenization, input formatting, prompt design, and optimization, as well as ethical considerations in prompt engineering. In the hands-on labs you?ll explore tasks such as formatting inputs for GPT-4, designing and optimizing prompts for business applications, and implementing multi-turn conversations with AI. You?ll work with innovative tools like the OpenAI API, OpenAI Codex, and OpenAI Playground, enhancing your learning experience while preparing you for integrating prompt engineering into your professional toolkit. By the end of this immersive course, you?ll have the skills necessary to effectively use prompt engineering in your software development projects. You'll be able to design, optimize, and test prompts for various business tasks, integrate GPT-4 with other software platforms, and address ethical concerns in AI deployment. Generative AI represents an exhilarating frontier in artificial intelligence, specializing in the creation of new data instances, imitation of real data, and content generation. Its remarkable capabilities facilitate automated content creation, enriched user experiences, and groundbreaking solutions across diverse industries, ultimately fueling efficiency and transcending technological limits. By harnessing the power of generative AI, developers can craft dynamic content, produce code and documentation, refine user interfaces, and devise customized recommendations, empowering them to construct highly efficient and custom solutions for a wide range of applications. Designed for experienced programmers, Turbocharge Your Code! Generative AI Boot Camp for Developers is a three-day workshop-style course that teaches you the latest skills and tools required to master generative AI models, transforming the way you approach software development. In today's fast-paced technological landscape, generative AI has emerged as a game-changer, with leading companies like NVIDIA, OpenAI, and Google leveraging its capabilities to push the boundaries of innovation. By learning how to harness the power of generative models such as GANs, VAEs, and Transformer models, you will be able to generate code, documentation, and tests, enhance user interfaces, and create dynamic content that adapts to user needs. Our comprehensive curriculum covers everything from the fundamentals of generative AI to advanced techniques and ethical considerations, including hands-on labs where you will develop and deploy custom models using state-of-the-art AI tools and libraries like TensorFlow and Hugging Face Transformers. Throughout the course you'll focus on practical application and collaboration, building confidence with personalized guidance and real-time feedback from our expert live instructor. Upon completion, you will be equipped with the knowledge and experience necessary to develop and implement innovative generative AI models across various industries, improving existing products, creating new applications, and gaining highly-valuable skills in the rapidly advancing field of AI. Additional course details: Nexus Humans Turbocharge Your Code! Generative AI Boot Camp for Developers (TTAI2305) 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 Turbocharge Your Code! Generative AI Boot Camp for Developers (TTAI2305) 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 designed for data scientists with experience of Python who need to learn how to apply their data science and machine learning skills on Azure Databricks. Overview After completing this course, you will be able to: Provision an Azure Databricks workspace and cluster Use Azure Databricks to train a machine learning model Use MLflow to track experiments and manage machine learning models Integrate Azure Databricks with Azure Machine Learning Azure Databricks is a cloud-scale platform for data analytics and machine learning. In this course, students will learn how to use Azure Databricks to explore, prepare, and model data; and integrate Databricks machine learning processes with Azure Machine Learning. Introduction to Azure Databricks Getting Started with Azure Databricks Working with Data in Azure Databricks Training and Evaluating Machine Learning Models Preparing Data for Machine Learning Training a Machine Learning Model Managing Experiments and Models Using MLflow to Track Experiments Managing Models Managing Experiments and Models Using MLflow to Track Experiments Managing Models Integrating Azure Databricks and Azure Machine Learning Tracking Experiments with Azure Machine Learning Deploying Models

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.

Duration 5 Days 30 CPD hours This course is intended for Technology Consultants Overview Enables you to implement and maintain SAP BW powered by SAP HANA.Outlines advantages of the new SAP 'In-Memory' database foundation for SAP BW.Explains HANA specific features as well as optimized existing as well as new data models and functions of SAP BW.Introduces the new SAP architecture framework of LSA++ and provides guidelines on how to convert your existing EDW-architecture into this new concept.Explains native HANA modeling and how both, HANA information views as well as BW InfoProviders, can complement each other in so-called 'mixed scenarios'. In this course, students gain the knowledge to enable themselves to implement and maintain SAP BW powered by SAP HANA. The course explains HANA specific features as well as optimized existing as well as new data models and functions of SAP BW. Getting Started SAP HANA Basics SAP BW Basics SAP HANA BW Modeling Tools SAP HANA Native Modeling Migration to SAP BW on HANA Migration Strategies and Tools SAP BW Migration Cockpit BW on HANA Data Management Multi-Temperature Data Management Optimizing BW Data Management based on Dynamic Tiering and Non- Active Concept Optimizing BW Data Management based on Nearline-Storage Optimization Areas in BW on HANA HANA-Optimized Data Modeling: InfoCubes HANA-Optimized Data Modeling: Advanced DataStore-Objects HANA-Optimized Data Modeling: CompositeProviders HANA-Optimized Data Staging HANA-Optimized Analytic Manager HANA-Optimized Analysis Processes Modeling with SAPïs Reference Architecture LSA++ Review of Layered Scalable Architecture (LSA) From LSA to LSA++: Streamline Core Layers LSA++: Open Operational Data Store Layer (Open ODS Views) LSA++: Agile Data Mart & BW Workspace Layer Hybrid Modeling in Mixed Scenarios Generation of HANA-Views of BW InfoProviders Mixed Scenarios SAP HANA Live HANA-Optimized BI Content Data Provisioning into SAP HANA and SAP BW Overview of Data Provisioning Methods to SAP HANA SLT Replication into SAP HANA SLT Replication into SAP BW Operational Data Provisioning into SAP BW DB-Connect and Open Hub Destination Additional Enhancements of BW 7.40 InfoObject Enhancements Authorization Enhancements Process Chain Monitoring HANA-Optimized BW Search HANA-Optimized BW Planning Obsolete BW Object Types Mobile Enablement Enhancements SAP HANA vs. BW Accelerator Summary & Outlook New Transactions and BW on HANA Roadmap Additional course details: Nexus Humans BW362 SAP BW on SAP HANA 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 BW362 SAP BW on SAP HANA 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 3 Days 18 CPD hours This course is intended for This course is designed for Service Provider networking professionals in job roles such as: Network administrators Network architects Network designers Network engineers Network managers Network Operations Center (NOC) personnel Network supervisors Overview After taking this course, you should be able to: Use NETCONF and RESTCONF programmability protocols on Cisco devices Describe and use tools to validate YANG data models on Cisco devices Describe and configure model-driven telemetry on Cisco devices Describe and configure network traffic automation with Cisco XTC Describe and use network automation tools that utilize SSH Automate service provider network configuration with Cisco NSO Describe how to automate virtualized resources with Cisco ESC Describe how to automate service provider WAN with Cisco WAE This course prepares you to implement and support automation solutions in a Service Provider network infrastructure, using network programmability principles, protocols, tools, and mechanisms. Through a combination of lessons and hands-on labs, you will learn to deploy, configure, monitor, and operate Service Provider network environments using modern data models. These models allow you to represent operational data and new network management protocols in order to administer hundreds or thousands of devices in a single operation, replacing traditional, time-consuming, error prone, device-by-device Command Line Interface (CLI) management. The course also introduces powerful automation solutions that can streamline network operations. This course also prepares you for the CCNP-Service Provider concentration exam 300-535. Implementing Network Device Programmability Interfaces with NETCONF and RESTCONF Implement NETCONF Protocol Implement RESTCONF Protocol Implementing Model-Driven Programmability with YANG YANG Data Models YANG Tools YANG Development Kit Implementing Model-Driven Telemetry Implementing Model-Driven Telemetry with gRPC Implementing Model-Driven Telemetry with gNMI Automating Service Provider Network Traffic with Cisco XTC Cisco XTC Fundamentals Configure Cisco XTC Automating Networks with Tools That Utilize SSH Implement Device Configurations with Python Netmiko Library Implement Device Configurations with Ansible Playbooks Orchestrating Network Services with Cisco NSO Cisco NSO Fundamentals Cisco NSO Device Manager Cisco NSO Services Implement Device Configurations with Python Automating Virtualized Resources with Cisco Elastic Services Controller Cisco ESC Architecture Cisco ESC Resource Management Automating the WAN with Cisco WAE Describe the Cisco WAE Components Additional course details: Nexus Humans Cisco Implementing Automation for Cisco Service Provider Solutions v1.0 (SPAUI) 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 Cisco Implementing Automation for Cisco Service Provider Solutions v1.0 (SPAUI) 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.