163 Courses delivered Online

Duration 1 Days 6 CPD hours This course is intended for To gain the most from attending this course you should possess the following incoming skills: Basic knowledge of programming concepts and syntax in Python. Familiarity with common data formats such as CSV, JSON, and XML. Experience using command-line interfaces and basic text editing tools. Understanding of basic machine learning concepts and algorithms. Overview Working in an interactive learning environment, led by our engaging expert, you will: Gain a solid understanding of prompt engineering concepts and their applications in software development and AI-driven solutions. Master the techniques for preprocessing and cleaning text data to ensure high-quality inputs for AI models like GPT-4. Develop expertise in GPT-4 tokenization, input formatting, and controlling model behavior for various tasks and requirements. Acquire the ability to design, optimize, and test prompts effectively, catering to diverse business applications and use cases. Learn advanced prompt engineering techniques, such as conditional text generation and multi-turn conversations, to create more sophisticated AI solutions. Practice creating prompts to generate, run, and test code in a chosen programming language using GPT-4 and OpenAI Codex. Understand the ethical implications and best practices in responsible AI deployment, ensuring fair and unbiased AI applications in software development. 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. Introduction to Prompt Engineering Overview of prompt engineering and its importance in AI applications Major applications of prompt engineering in business Common challenges faced in prompt engineering Overview of GPT-4 and its role in prompt engineering Key terminology and concepts in prompt engineering Getting Things Ready: Text Preprocessing and Data Cleansing Importance of data preprocessing in prompt engineering Techniques for text cleaning and normalization Tokenization and n-grams Stop word removal and stemming Regular expressions and pattern matching GPT-4 Tokenization and Input Formatting GPT-4 tokenization and its role in prompt engineering Understanding and formatting GPT-4 inputs Context windows and token limits Controlling response length and quality Techniques for handling out-of-vocabulary tokens Prompt Design and Optimization Master the skills to design, optimize, and test prompts for various business tasks. Designing effective prompts for different tasks Techniques for prompt optimization GPT-4 system and user parameters for controlling behavior Importance of prompt testing and iteration Best practices for prompt engineering in business applications Advanced Techniques and Tools in Prompt Engineering Learn advanced techniques and tools for prompt engineering and their integration in business applications. Conditional text generation with GPT-4 Techniques for handling multi-turn conversations Overview of tools for prompt engineering: OpenAI API, OpenAI Codex, and OpenAI Playground Integration of GPT-4 with other software platforms and tools Monitoring and maintaining prompt performance Code Generation and Testing with Prompt Engineering Develop the skills to generate, integrate, and test AI-generated code effectively, enhancing productivity and creativity in software development projects. Introduction to code generation with AI models like GPT-4 Designing prompts for code generation across programming languages Techniques for specifying requirements and constraints in prompts Generating and interpreting code snippets using AI-driven solutions Integrating generated code into existing projects and codebases Best practices for testing and validating AI-generated code Ethics and Responsible AI Understand the ethical implications of prompt engineering and the importance of responsible AI deployment in business. Ethical considerations in prompt engineering Bias in AI systems and its impact on prompt engineering Techniques to minimize bias and ensure fairness Best practices for responsible AI deployment in business applications Monitoring and addressing ethical concerns in prompt engineering

Duration 2 Days 12 CPD hours This course is intended for This course is intended for software testers, architects, engineers, or other related roles, who wish to apply AI to software testing practices within their enterprise. While there are no specific pre-requisites for this course, it would be helpful is the attendee has familiarity with basic scripting (Python preferred) and be comfortable with working from the command line (for courses that add the optional hands-on labs). Attendees without basic scripting skills can follow along with the hands-on labs or demos. Overview This course introduces AI and related technologies from a practical applied software testing perspective. Through engaging lecture and demonstrations presented by our expert facilitator, students will explore: Exploring AI Introduction to Machine Learning Introduction to Deep Learning Introduction to Data Science Artificial Intelligence (AI) in Software Testing Implementing AI in Test Automation Innovative AI Test Automation Tools for the Future Implementing AI in Software Testing / AI in Test Automation is an introductory-level course for attendees new to AI, Machine Learning or Deep Learning who wish to automate software testing tasks leveraging AI. The course explores the essentials of AI, ML and DL and how the integrate into IT business operations and initiatives. Then the course moves to specifics about the skills, techniques and tools used to apply AI to common software testing requirements. Exploring AI AI-Initiatives The Priority: Excellence AI- Intelligence Types The Machine Learning Types The Quality Learning Initiative The Inception in Academics AI - Importance & Applications The Re-visit Learning Re-visited via AI Teaching in the world of AI Exploring AI for Self-Development AI In Academics Beyond Academics Introduction to Machine Learning What is Machine Learning? Why Machine Learning? Examples - Algorithms behind Machine Learning Introduction to Deep Learning What is Deep Learning? Why Deep Learning? Example - Deep Learning Vs Machine Learning Introduction to Data Science What is Data Science? Why Data Science? Examples - Use Cases of Data Science Artificial Intelligence (AI) in Software Testing What is AI in Software Testing? The Role of AI Testing Why do we Need AI in Software Testing? Pros and Cons of AI in Software Testing Applications of AI in Software Testing Is it time for Testers or QA Teams to worry about AI? Automated Testing with Artificial Intelligence Implementing AI in Test Automation Training the AI Bots Challenges with AI-powered Applications Examples - Real World use cases using Artificial Intelligence Demo - Facial Emotion Detection Using Artificial Intelligence Demo - Text Analysis API Using Artificial Intelligence Demo - EYE SPY Mobile App Using Artificial Intelligence Innovative AI Test Automation Tools for the Future Tools used for Implementing AI in Automation Testing What is NEXT? AI Test Automation Demo using Testim

Duration 2 Days 12 CPD hours This course is intended for Attending students should be new to Jira (this is NOT for experienced users), and are required to have a background in basic Enterprise application development Overview This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Working in a hands-on learning environment guided by our expert Jira practitioner, students will explore: Getting started with JIRA Using JIRA for Business Projects Using JIRA for Agile Projects Issue Management Field Management Screen Management Workflows and Business Process Searching and Filtering Tracking issues is a critical component of any project management strategy. JIRA provides a web based single repository for creating, tracking and reporting on feature requests, bugs reported, or managing workflow. JumpStart to Jira for End Users is a two-day, lab-intensive course for participants new to Jira, that provides them with a hands-on Jira instance to ?learn by doing?. This course provides essential understanding in the practical use of the Jira in an Agile context, with an emphasis on Best Practices and practical job-ready skills. Getting started with JIRA JIRA Overview Core concepts Terminology Infrastructure Users and Groups JIRA roles Using JIRA for Business Projects Overview of Projects Project types Project screens Tasks and task management Project Management Process Management Using JIRA for Agile Projects Overview of Agile with JIRA (very brief) Kanban overview Running a project with Kanban Configuring agile screen resolving an issue Issue Management Overview of Issues Working with issues Issue cloning Time tracking Issues and comments Tasks and subtasks Field Management Overview of Fields Built-in fields Custom fields Searching Configuring JIRA for fields Screen Management Overview of JIRA screens Working with screens Using screen tabs Issue type screens associating an issue type screen a with a project Customizing JIRA screens Workflow and Business Process Overview of Workflow Mapping business processes Managing workflows Authoring a workflow Updating an existing workflow Workflow schemes Applying a workflow to a project Searching and Reporting Overview of Searching Search screens Basic search Advanced search with JQL Working with search results Reports Dashboards Filters Gadgets Charts Summary and Best Practices Looking back at the ?Big Picture? JIRA Administration Overview Where JIRA fits into the Agile perspective JIRA End-User best practices

Technology and innovation and its profound impact on financial operations Technology adoption indeed comes with risks, particularly around data security and privacy. As CFOs, we must ensure robust cybersecurity measures and adhere to strict data protection regulations. It requires ongoing investment in secure systems, staff training, and proactive monitoring to mitigate risks and protect sensitive financial information. Change management is also crucial. The adoption of new technologies requires proper planning, training, and cultural adjustments. As CFOs, we need to foster a culture that embraces innovation and continuous learning. Clear communication about the benefits and objectives of technology adoption is essential to gain buy-in and drive successful implementation. Fantastic insights! Now, let’s discuss the future. What emerging technologies do you foresee shaping the future of finance functions? One area that holds immense potential is blockchain technology. Its decentralised and transparent nature has the potential to streamline financial transactions, enhance auditability, and revolutionize supply chain finance. We’re closely monitoring blockchain’s development and exploring pilot projects to leverage its benefits. I agree, blockchain is a game-changer. Additionally, as the internet of things (IoT) expands, we anticipate new opportunities and challenges. CFOs will need to adapt to the influx of real-time data from interconnected devices, leveraging this information to optimize financial processes, enhance risk management, and improve operational efficiencies. Before we wrap up, any final thoughts or advice for our CFO audience? Embrace technology and view it as an opportunity rather than a threat. Invest in understanding the technological landscape and its implications for finance. Stay curious, adapt, and be open to change. Technology will continue to evolve, and as CFOs, we must evolve with it. Absolutely. Collaboration is key. Engage with IT teams, industry peers, and external experts to stay informed about the latest technological advancements. By fostering partnerships and sharing knowledge, we can collectively navigate the ever-changing technology landscape and drive innovation within our organisations. https://www.fdcapital.co.uk/podcast/technology-and-innovation-and-its-profound-impact-on-financial-operations/ Tags Online Events Things To Do Online Online Conferences Online Science & Tech Conferences #technology #innovation #financial #impact #operations

The Fintech Frontier: Why FDs Need to Know About Fintech,” the podcast where we delve into the world of financial technology There are numerous areas where fintech can make a significant impact. For example, payment processing and reconciliation can be streamlined through digital payment solutions and automated tools. Data analytics and artificial intelligence can enhance financial forecasting, risk management, and fraud detection. Blockchain technology can revolutionize supply chain finance and streamline processes involving multiple parties. By understanding the capabilities of these fintech solutions, FDs can identify areas for improvement and select the right technologies to optimise their financial operations. Additionally, fintech can greatly enhance financial reporting and analysis. Advanced data analytics tools can extract meaningful insights from vast amounts of financial data, enabling FDs to make data-driven decisions and identify trends and patterns. Automation of repetitive tasks, such as data entry and reconciliation, reduces the risk of errors and frees up valuable time for FDs to focus on strategic initiatives. The adoption of cloud-based financial management systems also provides flexibility, scalability, and real-time access to financial data, empowering FDs to make informed decisions on the go. With the rapid pace of fintech advancements, how can FDs stay up to date and navigate the evolving fintech landscape? Continuous learning and engagement with the fintech community are key. Attend industry conferences, participate in webinars and workshops, and engage with fintech startups and established players. Networking with professionals in the field, joining fintech-focused associations, and following relevant publications and blogs can help FDs stay abreast of the latest fintech developments. Embracing a mindset of curiosity and adaptability is crucial in navigating the ever-changing fintech landscape. I would also encourage FDs to foster partnerships and collaborations with fintech companies. Engage in conversations with fintech providers to understand their solutions and explore potential synergies. By forging strategic partnerships, FDs can gain access to cutting-edge technologies and co-create innovative solutions tailored to their organisation’s unique needs. As we conclude, do you have any final thoughts or advice for our FD audience regarding fintech? Embrace fintech as an opportunity, not a threat. Seek to understand its potential and how it can align with your organisation’s goals and strategies. Be open to experimentation and pilot projects to test the viability of fintech solutions. Remember that fintech is a tool to enhance and optimize financial processes, and as FDs, we have a crucial role in driving its effective implementation. https://www.fdcapital.co.uk/podcast/the-fintech-frontier-why-fds-need-to-know-about-fintech/ Tags Online Events Things To Do Online Online Conferences Online Business Conferences #event #fintech #knowledge #fds #frontier

An M&A specialist is a senior CFO with a proven track record of overseeing and implementing mergers and acquisitions. An M&A specialist is a senior CFO with a proven track record of overseeing and implementing mergers and acquisitions. They may be hired on a full-time basis and carry a full CFO workload or can be recruited on a part-time or interim basis to focus on a specific merger or acquisition. This flexibility is ideal for start-ups and SMEs who don’t have the budget to recruit a full external team to oversee an M&A or to hire a full-time CFO. The CFO is a link between both companies engaged in the M&A, acting as the eyes and ears for both the board and CEO. Their financial skills enable them to identify potential M&A opportunities and incorporate risk management into their strategy to get the most value out of their deal. Most companies evolve their approach to mergers and acquisitions organically, especially those who rely on an M&A specialist instead of having a dedicated team that works solely on M&A. The CFO is responsible for considering any potential acquisitions, crunching the numbers involved, and ensuring due diligence. They’ll be responsible for determining the value of a potential M&A and presenting it to the company’s board and leadership team to determine whether to make the purchase. An M&A specialist is responsible for gathering the data – including both positive and negative factors – to present an objective look at the other organisation and the potential value the acquisition could bring. CFOs will spend most of their time getting to grips with the numbers involved, long before presenting the M&A proposal to the board. This exercise also requires them to have real-time insight into their own company’s performance, value, and finances to paint a wider picture. An M&A specialist will take the critical steps of ensuring that the numbers presented to them are correct. CFOs who don’t specialise in M&As will still have the skill set required to oversee the process but may lack the efficiency and unique insight of an M&A specialist. Companies that are exploring the option of a merger or being acquired by another company may also decide to recruit an M&A specialist to prepare their accounting. The organisation will want to ensure they present the correct numbers to get the correct valuation and prevent any delays further in the process if incorrect numbers pop up. Getting on top of the data early can enable CFOs to plan accordingly. Most will want to provide extra time within their strategy for any potential hiccups along the way. Spending more time on the data early on can speed up the process while still ensuring due diligence is met. Visit our website to learn more https://www.fdcapital.co.uk/mergers-and-acquistions-specialist/ Tags Online Events Things To Do Online Online Networking Online Business Networking #finance #insights #cfo #mergers #acquisitions

Duration 5 Days 30 CPD hours This course is intended for This intermediate and beyond level course is geared for experienced technical professionals in various roles, such as developers, data analysts, data engineers, software engineers, and machine learning engineers who want to leverage Scala and Spark to tackle complex data challenges and develop scalable, high-performance applications across diverse domains. Practical programming experience is required to participate in the hands-on labs. Overview Working in a hands-on learning environment led by our expert instructor you'll: Develop a basic understanding of Scala and Apache Spark fundamentals, enabling you to confidently create scalable and high-performance applications. Learn how to process large datasets efficiently, helping you handle complex data challenges and make data-driven decisions. Gain hands-on experience with real-time data streaming, allowing you to manage and analyze data as it flows into your applications. Acquire practical knowledge of machine learning algorithms using Spark MLlib, empowering you to create intelligent applications and uncover hidden insights. Master graph processing with GraphX, enabling you to analyze and visualize complex relationships in your data. Discover generative AI technologies using GPT with Spark and Scala, opening up new possibilities for automating content generation and enhancing data analysis. Embark on a journey to master the world of big data with our immersive course on Scala and Spark! Mastering Scala with Apache Spark for the Modern Data Enterprise is a five day hands on course designed to provide you with the essential skills and tools to tackle complex data projects using Scala programming language and Apache Spark, a high-performance data processing engine. Mastering these technologies will enable you to perform a wide range of tasks, from data wrangling and analytics to machine learning and artificial intelligence, across various industries and applications.Guided by our expert instructor, you?ll explore the fundamentals of Scala programming and Apache Spark while gaining valuable hands-on experience with Spark programming, RDDs, DataFrames, Spark SQL, and data sources. You?ll also explore Spark Streaming, performance optimization techniques, and the integration of popular external libraries, tools, and cloud platforms like AWS, Azure, and GCP. Machine learning enthusiasts will delve into Spark MLlib, covering basics of machine learning algorithms, data preparation, feature extraction, and various techniques such as regression, classification, clustering, and recommendation systems. Introduction to Scala Brief history and motivation Differences between Scala and Java Basic Scala syntax and constructs Scala's functional programming features Introduction to Apache Spark Overview and history Spark components and architecture Spark ecosystem Comparing Spark with other big data frameworks Basics of Spark Programming SparkContext and SparkSession Resilient Distributed Datasets (RDDs) Transformations and Actions Working with DataFrames Spark SQL and Data Sources Spark SQL library and its advantages Structured and semi-structured data sources Reading and writing data in various formats (CSV, JSON, Parquet, Avro, etc.) Data manipulation using SQL queries Basic RDD Operations Creating and manipulating RDDs Common transformations and actions on RDDs Working with key-value data Basic DataFrame and Dataset Operations Creating and manipulating DataFrames and Datasets Column operations and functions Filtering, sorting, and aggregating data Introduction to Spark Streaming Overview of Spark Streaming Discretized Stream (DStream) operations Windowed operations and stateful processing Performance Optimization Basics Best practices for efficient Spark code Broadcast variables and accumulators Monitoring Spark applications Integrating External Libraries and Tools, Spark Streaming Using popular external libraries, such as Hadoop and HBase Integrating with cloud platforms: AWS, Azure, GCP Connecting to data storage systems: HDFS, S3, Cassandra, etc. Introduction to Machine Learning Basics Overview of machine learning Supervised and unsupervised learning Common algorithms and use cases Introduction to Spark MLlib Overview of Spark MLlib MLlib's algorithms and utilities Data preparation and feature extraction Linear Regression and Classification Linear regression algorithm Logistic regression for classification Model evaluation and performance metrics Clustering Algorithms Overview of clustering algorithms K-means clustering Model evaluation and performance metrics Collaborative Filtering and Recommendation Systems Overview of recommendation systems Collaborative filtering techniques Implementing recommendations with Spark MLlib Introduction to Graph Processing Overview of graph processing Use cases and applications of graph processing Graph representations and operations Introduction to Spark GraphX Overview of GraphX Creating and transforming graphs Graph algorithms in GraphX Big Data Innovation! Using GPT and Generative AI Technologies with Spark and Scala Overview of generative AI technologies Integrating GPT with Spark and Scala Practical applications and use cases Bonus Topics / Time Permitting Introduction to Spark NLP Overview of Spark NLP Preprocessing text data Text classification and sentiment analysis Putting It All Together Work on a capstone project that integrates multiple aspects of the course, including data processing, machine learning, graph processing, and generative AI technologies.

These events are designed to work on the ideas introduced in Level 1: Understanding & Dealing with Everyday Racism The Six Stages Framework

These events are designed to introduce the BOOK & basic ideas behind Understanding & Dealing with Everyday Racism The Six Stages Framework

These events are designed to introduce the BOOK & basic ideas behind Understanding & Dealing with Everyday Racism The Six Stages Framework