2334 Learning & Development courses

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

Duration 3 Days 18 CPD hours This course is intended for Students receive comprehensive Microsoft Dynamics exam preparation, becoming familiarized with the Dynamics CRM customization and configuration tools. Aspirants also learn to leverage the platform tools to create custom objects, automate tasks, modify user interface, and perform other such customizations. Overview Configure the Dynamics CRM settingsConfigure different entities and fieldsImplement entity relationships, custom actions, workflows, and dialogsIdentify scenarios for utilizing multiple forms, and design considerations for chartsSet default share views and public views, and configure and manage dashboardsIdentify role-based business processesIdentify and manage business requirements and teams This course explains everything you need to know about customizing and configuring the Dynamics CRM 365 system in accordance with a company?s specific requirements. Introduction to Customization and Configuring Dynamics CRM Talent and Course Introduction Module Overview CRM Overview What is Dynamics Customization and Configuration? CRM Architecture Customization Methodology Module review Obtaining a Dynamics CRM Trial TEST YOUR KNOWLEDGE MODULE 1' Manage Microsoft Dynamics CRM Online Subscriptions Module Overview Configuring CRM Overview of CRM Security User Administration Mailboxes Teams CRM Security Model Module Overview Purpose of the CCRM Security Model Privileges Access Levels Security Roles Hierarchy Security Hierarchy Types Module review Introduction to Solutions Module Overview Solutions Overview Solution Detail Creating and Working with Solutions Working with Solution Assets Exporting Solutions Importing Solutions Module review Entity and Field Customization Module Overview Types Entities Entity Ownership Entity Properties System vs Custom Entities Custom Entities and Security Roles Overview of Fields Field Properties Module review Additional Field Customization Module Overview Creating Fields to Meet Client Needs Calculated Fields Rollup Fields CRM Option Sets Alternate Keys Field Level Security State and Status Reason Transitions Module Review Configure mobile devices Module Overview Types of Relationships How and where they are created Many to Many Relationships Hierarchical Data Entity Mapping Connection and Connection Roles Module Review Customizing Forms Module Overview Form types Qualities of a good form Building a Form Specialized Form Components Access Teams and Sub Grids Working with Navigation Additional Form Types Multiple Forms Form customizations and Mobile Clients Module Review Business Rules Module Overview Business Rules Business Rule Scope Trigger Rules Condition and Actions Else Conditions and Actions Occur When Conditions Are True Module review Views and Visualizations Module Overview Using Views View Customization System View Types Quick Find Customization Charts Customizing Dashboard Themes Module Review Introduction to Processes Module Overview Processes and Automation Workflow Basics Module review Business Process Flows What are CRM Business Process Flows Enabling Business Process Flows Steps Stages and Categories Conditional Branching Module Review Bringing it all Together Module Overview Review of Customization Topics Covered Senario Packaging in a Solution Module review

Duration 5 Days 30 CPD hours This course is intended for Administrators Developers Implementers Systems Administrators Overview Understand the PeopleSoft system architecture, application development methodology, and tool set so you can build and customize PeopleSoft applications efficiently to meet your organizations business requirements. Quickly and efficiently create functionality in PeopleSoft applications to take advantage of the unique capabilities of these applications. Gain Hands-On Experience Using PeopleSoft Application Designer Enrolling in this course will also give you hands-on experience with the Application Designer, the PeopleSoft integrated development environment (IDE). Learn to create and modify PeopleSoft definitions, including fields, records, pages and components. By the end of this course, you'll be able to use Application Designer to create and deploy PeopleSoft classic applications and fluid applications This PeopleTools I training introduces the PeopleSoft application development methodology. This 5-day course gives you a general overview of PeopleSoft system architecture, as well as the tool set used to develop new applications or customize existing PeopleSoft applications. Navigating PeopleSoft ApplicationsExplaining the PeopleSoft ArchitectureValidating DataUsing Application Designer to Develop ApplicationsDesigning the ApplicationCreating Record DefinitionsBuilding SQL TablesCreating Page DefinitionsRegistering ComponentsTesting ApplicationsEditing the Portal Registry StructureCreating Menu DefinitionsUnderstanding the Fluid User InterfaceCreating Fluid PagesUsing Delivered CSS Additional course details: Nexus Humans Oracle Peoplesoft PeopleTools I 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 Oracle Peoplesoft PeopleTools I 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 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: Data platform engineers Solutions architects IT professionals Overview In this course, you will learn to: Apply data lake methodologies in planning and designing a data lake Articulate the components and services required for building an AWS data lake Secure a data lake with appropriate permission Ingest, store, and transform data in a data lake Query, analyze, and visualize data within a data lake In this course, you will learn how to build an operational data lake that supports analysis of both structured and unstructured data. You will learn the components and functionality of the services involved in creating a data lake. You will use AWS Lake Formation to build a data lake, AWS Glue to build a data catalog, and Amazon Athena to analyze data. The course lectures and labs further your learning with the exploration of several common data lake architectures. Module 1: Introduction to data lakes Describe the value of data lakes Compare data lakes and data warehouses Describe the components of a data lake Recognize common architectures built on data lakes Module 2: Data ingestion, cataloging, and preparation Describe the relationship between data lake storage and data ingestion Describe AWS Glue crawlers and how they are used to create a data catalog Identify data formatting, partitioning, and compression for efficient storage and query Lab 1: Set up a simple data lake Module 3: Data processing and analytics Recognize how data processing applies to a data lake Use AWS Glue to process data within a data lake Describe how to use Amazon Athena to analyze data in a data lake Module 4: Building a data lake with AWS Lake Formation Describe the features and benefits of AWS Lake Formation Use AWS Lake Formation to create a data lake Understand the AWS Lake Formation security model Lab 2: Build a data lake using AWS Lake Formation Module 5: Additional Lake Formation configurations Automate AWS Lake Formation using blueprints and workflows Apply security and access controls to AWS Lake Formation Match records with AWS Lake Formation FindMatches Visualize data with Amazon QuickSight Lab 3: Automate data lake creation using AWS Lake Formation blueprints Lab 4: Data visualization using Amazon QuickSight Module 6: Architecture and course review Post course knowledge check Architecture review Course review Additional course details: Nexus Humans Building Data Lakes 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 Building Data Lakes 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 3 Days 18 CPD hours Additional course details: Nexus Humans Enterprise Operator 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 Enterprise Operator 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 3.75 Days 22.5 CPD hours This course is intended for New systems administrators Overview When you complete this course, you will be able to:Customize your application, including page layouts, fields, tabs, and business processes in Lightning Experience.Learn how security settings created in Salesforce Classic are applied in Lightning.Maintain and import clean data in Lightning.Use Lightning features to create high-value reports and dashboards.Understand how workflow automation complies with Lightning This course is the core training that ensures your success with Salesforce Lightning. It?s a must for new administrators, and we recommend completing this course before starting a Salesforce deployment or when taking over an existing deployment. Getting Around the App Data Model and Navigation Lightning Experience Help & Training Getting Your Organization Ready for Users Setting Up the Company Profile Configuring the User Interface Setting Up Activities and Calendars Configuring Search Settings Setting Up Chatter Groups Mobile Access with Salesforce1 Setting Up & Managing Users Managing User Profiles Managing Users Setting Up Chatter Free Users and Invites Troubleshooting Login Issues Security & Data Access Restricting Logins Determining Object Access Setting Up Record Access Creating a Role Hierarchy Dealing with Record Access Exceptions Managing Field-level Security Object Customizations Administering Standard Fields Creating New Custom Fields Creating Selection Fields: Picklists and Lookups Creating Formula Fields Working with Page Layouts Working with Record Types and Business Processes Maintaining Data Quality Managing Data Import Wizards Data Loader Data.com Mass Transfer Backing Up Data Mass Delete and Recycle Bin Reports & Dashboards Running and Modifying Reports Creating New Reports with the Report Builder Working with Report Filters Summarizing with Formulas and Visual Summaries Printing, Exporting, and Emailing Reports Building Dashboards Automation Email Templates Workflow Rules Process Builder Lead Automation Managing the Support Process Managing and Resolving Cases Customizing a Support Process Automating Support Understanding the Salesforce Console for Service Collaborating in the Service Cloud Analyzing Support Data Additional course details: Nexus Humans Salesforce Administration Essentials for New Admins in Lightning Experience (ADX201) 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 Salesforce Administration Essentials for New Admins in Lightning Experience (ADX201) 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 in an intermediate and beyond-level course is geared for experienced Python developers looking to delve into the exciting field of Natural Language Processing. It is ideally suited for roles such as data analysts, data scientists, machine learning engineers, or anyone working with text data and seeking to extract valuable insights from it. If you're in a role where you're tasked with analyzing customer sentiment, building chatbots, or dealing with large volumes of text data, this course will provide you with practical, hands on skills that you can apply right away. Overview This course combines engaging instructor-led presentations and useful demonstrations with valuable hands-on labs and engaging group activities. Throughout the course you'll: Master the fundamentals of Natural Language Processing (NLP) and understand how it can help in making sense of text data for valuable insights. Develop the ability to transform raw text into a structured format that machines can understand and analyze. Discover how to collect data from the web and navigate through semi-structured data, opening up a wealth of data sources for your projects. Learn how to implement sentiment analysis and topic modeling to extract meaning from text data and identify trends. Gain proficiency in applying machine learning and deep learning techniques to text data for tasks such as classification and prediction. Learn to analyze text sentiment, train emotion detectors, and interpret the results, providing a way to gauge public opinion or understand customer feedback. The Hands-on Natural Language Processing (NLP) Boot Camp is an immersive, three-day course that serves as your guide to building machines that can read and interpret human language. NLP is a unique interdisciplinary field, blending computational linguistics with artificial intelligence to help machines understand, interpret, and generate human language. In an increasingly data-driven world, NLP skills provide a competitive edge, enabling the development of sophisticated projects such as voice assistants, text analyzers, chatbots, and so much more. Our comprehensive curriculum covers a broad spectrum of NLP topics. Beginning with an introduction to NLP and feature extraction, the course moves to the hands-on development of text classifiers, exploration of web scraping and APIs, before delving into topic modeling, vector representations, text manipulation, and sentiment analysis. Half of your time is dedicated to hands-on labs, where you'll experience the practical application of your knowledge, from creating pipelines and text classifiers to web scraping and analyzing sentiment. These labs serve as a microcosm of real-world scenarios, equipping you with the skills to efficiently process and analyze text data. Time permitting, you?ll also explore modern tools like Python libraries, the OpenAI GPT-3 API, and TensorFlow, using them in a series of engaging exercises. By the end of the course, you'll have a well-rounded understanding of NLP, and will leave equipped with the practical skills and insights that you can immediately put to use, helping your organization gain valuable insights from text data, streamline business processes, and improve user interactions with automated text-based systems. You?ll be able to process and analyze text data effectively, implement advanced text representations, apply machine learning algorithms for text data, and build simple chatbots. Launch into the Universe of Natural Language Processing The journey begins: Unravel the layers of NLP Navigating through the history of NLP Merging paths: Text Analytics and NLP Decoding language: Word Sense Disambiguation and Sentence Boundary Detection First steps towards an NLP Project Unleashing the Power of Feature Extraction Dive into the vast ocean of Data Types Purification process: Cleaning Text Data Excavating knowledge: Extracting features from Texts Drawing connections: Finding Text Similarity through Feature Extraction Engineer Your Text Classifier The new era of Machine Learning and Supervised Learning Architecting a Text Classifier Constructing efficient workflows: Building Pipelines for NLP Projects Ensuring continuity: Saving and Loading Models Master the Art of Web Scraping and API Usage Stepping into the digital world: Introduction to Web Scraping and APIs The great heist: Collecting Data by Scraping Web Pages Navigating through the maze of Semi-Structured Data Unearth Hidden Themes with Topic Modeling Embark on the path of Topic Discovery Decoding algorithms: Understanding Topic-Modeling Algorithms Dialing the right numbers: Key Input Parameters for LSA Topic Modeling Tackling complexity with Hierarchical Dirichlet Process (HDP) Delving Deep into Vector Representations The Geometry of Language: Introduction to Vectors in NLP Text Manipulation: Generation and Summarization Playing the creator: Generating Text with Markov Chains Distilling knowledge: Understanding Text Summarization and Key Input Parameters for TextRank Peering into the future: Recent Developments in Text Generation and Summarization Solving real-world problems: Addressing Challenges in Extractive Summarization Riding the Wave of Sentiment Analysis Unveiling emotions: Introduction to Sentiment Analysis Tools Demystifying the Textblob library Preparing the canvas: Understanding Data for Sentiment Analysis Training your own emotion detectors: Building Sentiment Models Optional: Capstone Project Apply the skills learned throughout the course. Define the problem and gather the data. Conduct exploratory data analysis for text data. Carry out preprocessing and feature extraction. Select and train a model. ? Evaluate the model and interpret the results. Bonus Chapter: Generative AI and NLP Introduction to Generative AI and its role in NLP. Overview of Generative Pretrained Transformer (GPT) models. Using GPT models for text generation and completion. Applying GPT models for improving autocomplete features. Use cases of GPT in question answering systems and chatbots. Bonus Chapter: Advanced Applications of NLP with GPT Fine-tuning GPT models for specific NLP tasks. Using GPT for sentiment analysis and text classification. Role of GPT in Named Entity Recognition (NER). Application of GPT in developing advanced chatbots. Ethics and limitations of GPT and generative AI technologies.

R&D work is often carried out in entrepreneurial companies with the aim of developing solutions to scientific or technological problems for a wide range of customers. Projects can include longer term 'frontiers of science' research, medium term product development/manufacturing or more immediate troubleshooting or contract research assignments. In all these contexts, the ability to create innovative solutions in a timely and cost-effective manner is the essence of successful R&D. Whilst R&D groups typically excel in technical expertise, those involved often recognise that there is scope for improving the way that projects are managed. The aim of this training programme is to address this need whilst ensuring that the creative, entrepreneurial spirit that is fundamental to good R&D continues to flourish. MODULE 1: Creating the foundations for success Off-line video tutorials and exercises Total time ~ 1 - 1.5 hours Video 1: Making the most of project management in R&D Characterising R&D projects Applying project management to R&D work Exploiting the potential of project management in R&D Video 2: Promoting success in R&D project management Modelling successful project management Evaluating performance and promoting success The role and skills of the project manager/leader MODULE 2: Initiating and defining R&D projects Live interactive sessions (via Zoom): Session 1: 10:00 - 12:00 Session 2: 14:00 - 15:30 Session 1: Selecting and initiating projects Recognising worthwhile opportunities; initiating projects Identifying stakeholders and their goals Characterising and engaging stakeholders Session 2: Defining goals and agreeing deliverables Establishing the full scope of the project Clarifying and prioritising project deliverables Defining and agreeing deliverable specifications MODULE 3: Planning R&D projects Live interactive sessions (via Zoom): Session 1: 10:00 - 12:00 Session 2: 14:00 - 15:30 Session 1: Identifying and organising activities Creating effective plans; avoiding planning pitfalls Identifying tasks and assigning responsibilities Sequencing tasks and estimating durations Session 2: Developing the timeline and resource plan Identifying the 'critical path'; creating a resource plan Dealing with estimating uncertainty Accelerating the programme MODULE 4: Leadership and teamwork in R&D projects Off-line video tutorials and exercises Total time ~ 1 - 1.5 hours Video 1: Working effectively in project teams Building teamwork in contemporary organisations Recognising each other's skills; building synergy Building good working relationships; handling conflict Video 2: The role of the R&D project team leader Building teamwork: the role of leadership Creating an effective team culture Delegating work and motivating team members MODULE 5: Managing uncertainty in R&D projects Live interactive sessions (via Zoom): Session 1: 10:00 - 12:00 Session 2: 14:00 - 15:30 Session 1: Characterising uncertainty; identifying risks Exploring uncertainty; applying risk management Focusing the risk management process Identifying and defining risk events Session 2: Managing and controlling risks to the project Evaluating risk events Selecting between risk strategies; setting contingencies Updating and controlling exposure to risk MODULE 6: Implementing and controlling R&D projects Live interactive sessions (via Zoom): Session 1: 10:00 - 12:00 Session 2: 14:00 - 15:30 Session 1: Initiating assignments and managing changes Creating a pro-active implementation and control culture Establishing effective implementation and control procedures Assigning work and managing changes Session 2: Monitoring, managing and developing performance Adopting meaningful monitoring techniques Responding to problems; building performance Managing and controlling multiple project assignments

Self-understanding is a prerequisite for leading and managing others responsibly and honourably. The field of Neuro Linguistic Programming has helped us to gain a better insight into how we all think and behave. Upon completion of this course participants will be able to: Gain an insight into the purpose and functions of the unconscious mind Develop flexibility to increase their for behaviours in different circumstances Appreciate how different people experience the world Create and set effective goals and direction Understand the NLP Model of Communication Adapt their communication style to maximise effectiveness Influence and persuade others by connecting with people Understand how empowerment can make life easier Appreciate how creativity works Learn creativity techniques to tap into the power of the team 1 Self-awareness Autopilot - your unconscious mind Developing flexibility How identify, values & beliefs shape our behaviour Models of the world 2 Creating direction Describing present and desired state Designing your direction Making it happen Self-mastery 3 Communication The NLP Model of Communication Insights to the way people think Understanding representation systems Reframing the way people think about negative experiences Using metaphor 4 Influence and persuasion Building trust Connecting with people Purpose intention and outcomes The difference empowerment makes 5 Creativity and innovation Hindrances to creativity and innovation Your natural state of creativity Getting unblocked Creativity techniques 6 Action plan Course summary and presentation of action plans