8644 Art courses delivered Online

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.

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.

Do you hear yourself saying the same thing over and over again? Do you want to bring some new skills to your role? If you have been in a sales role for some time it is easy to fall into a comfortable pattern. This workshop will help you incorporate advanced techniques drawn from NLP, behavioural science and social psychology into your existing skills. This course will help you: Use the consultative sales process to achieve more cross-sales Employ advanced rapport-building skills Assess the buying preferences of a customer Articulate the link between customer goals and needs Identify your customer's needs and wants Use advanced questioning techniques to gather information Resist the temptation to tell when it would be better to ask Identify communication preferences Given various scenarios, present a product to the explicit need of a customer Appreciate the impact of the language used during this stage of the sale and decide what language is appropriate with a variety of customers Handle objections positively Close the sale or gain commitment to further action 1 Introduction Aims and objectives of the training Personal introductions and objectives Self-assessment of existing sales skills Overview of content 2 Understanding yourself and your customers Personal communication style and what this means in a sales situation Wants versus needs What motivates people to buy Using social media tools such as LinkedIn Managing your portfolio to maximise sales Preparing to sell 3 The sales process Overview of the consultative sales process Review personal strengths and weaknesses as a salesperson Habits of top-performing sales people Common pitfalls Articulate sales goals 4 Building rapport 11 decisions that customers make in the first 9 seconds Spotting buyer communication preferences Building rapport with a wide variety of customers Dealing with emotions Keeping control 5 Questioning and listening Assumptions and how they trip us up Structured questioning Looking for cross-sales Honing your listening skills Identifying buyers' motivation Using summaries to move the customer forward 6 Presenting products and services to customers Choosing the right time to present Using features, advantages and benefits Tailoring your presentation of products and services to match buyer preferences and motivations 7 Gaining commitment When to close Dealing with difficult customers 5 things to avoid when handling a customer objection 8 Managing your business The link between service and sales Using customer surveys Winning back lost business 9 Putting it all together Skills practice Personal learning summary and action plans

Developing the high performing team takes time and effort. But above all, it requires an understanding of the dynamics of high performing teams. This programme helps managers and leaders understand what high performing teams do and how they do it. It focuses on enabling managers to see their teams from different perspectives, allowing them to adapt their styles to maximise team outputs. A core theme is the need for managers of teams to 'hold up the mirror' to themselves and to see themselves as a leader of people, to reflect on how others see them and to modify their style accordingly. This programme will help managers / team leaders: Analyse the constituents of a 'high performing' team Apply essential influencing techniques Use a range of communication techniques to support effective teamwork Create and articulate team vision Generate common values Assess team effectiveness and take/recommend the appropriate actions Make more efficient use of team time Understand and agree on techniques to manage conflict Define and implement team meeting protocols that will facilitate team effectiveness Use the Prime Focus model to create the environment and framework for a high performing team Draft your team strategy to take them to the next level Day 1 1 Welcome and introduction Participants are welcomed to the programme and invited to share their personal objectives and people challenges Participants are given an action plan template to complete throughout the workshop 2 Your team The concept of 'positive intention' The difference between a team and a high performing team Assess your team effectiveness What is your 'interference'? 3 The team environment Setting the scene Building rapport Active listening Team goals and role profiling 4 Your style Tuckman model of team stages - how do you manage each stage? Team standards and goals Your team vision 5 Effective team meetings Influencing in team meetings How to make them interesting and relevant The pure role of the chair Day 2 1 Effective communication techniques Giving and receiving feedback Your communication style How to adapt, pace and lead to build rapport The Mehrabian theory of communication 2 How to manage conflict What is conflict? What is your default conflict approach? Tools and tips for managing conflict Practice sessions 3 Team skills Undertake a team skills analysis Types of team member Motivating team members Reframing situations 4 Setting your strategy Seeing the bigger picture The Prime Focus Model Your strategy for success Articulating your strategy Action plans revisited

Agility has become a prized business attribute. Although Agile methods were once most associated with software development, they are now applied in a host of different areas. Agile continues to find new applications because it is primarily an attitude. This programme delivers a solid grounding in both the Agile mindset and Agile methods. It covers three methods, illustrates the benefits of each and shows how they can be integrated. It includes practical techniques as well as background knowledge. By the end of the session, participants will be able to: Apply Agile concepts to self-manage their work Understand the roles people take on in Agile teams Use a variety of techniques to help deliver customer satisfaction Focus on delivering against priorities Employ a range of estimating techniques 1 Introduction Overview of the programme Review of participants' needs and objectives 2 The basics of Agile What makes Agile different Agile Manifesto and Principles Using feedback to deliver what is needed 3 Agile teams Multi-disciplinary teams Team size and empowerment Agile values 4 Agile at the team level - Scrum Scrum roles Scrum 'events' Scrum 'artifacts' 5 Agile for teams juggling multiple demands - Kanban Taking control of the work Improving throughput Dealing with bottlenecks 6 Agile in projects - AgilePM The phases of an Agile project Managing change requests Delivering on time 7 Estimating T-shirt / Pebble sizing Yesterday's weather Planning poker 8 Pick 'n' mix - some useful techniques The daily stand-up User stories Retrospectives Work-in-process limits Burndown charts Minimum viable product A / B testing 9 Review and action planning Identify actions to be implemented individually Conclusion

Software comes in a variety of guises - application software, firmware, middleware, system software. Increasingly, however, it doesn't necessarily present that way, especially as the boundaries between software, data and source code are becoming more and more blurred. And as software becomes more complex and more difficult to disentangle, so it becomes harder to manage and to value. But as it becomes more integral to every aspect of a business, so it is ever more important to keep on top of the technical, legal and commercial issues that arise, issues such as: To address these issues, organisations need a process for evaluating their current situation from all perspectives and for identifying the key actions they need to take to ensure holistic management of their software. This very practical programme will help set your organisation on the right path. Note: this is an indicative agenda, to be used as a starting point for a conversation between client and consultant, depending on the organisation's specific situation and requirements. This programme is designed to give you a deeper understanding of: The technical, legal and commercial risks associated with software development, procurement, use and commercial exploitation The most appropriate processes and responsibilities for managing those risks Note: this is an indicative agenda, to be used as a starting point for a conversation between client and consultant, depending on the organisation's specific situation and requirements. 1 Software business model What is the software business model? What options exist? Has the software business model been thoroughly reviewed to ensure its viability? This means fully understanding the market opportunity, the business environment and customer and end-user expectations. 2 Technology What are the technologies? How has the technology selection been validated considering the competitiveness, structure, and potential for future innovation? 3 UI and UX What is the UI and UX? How to best articulate this? Has the user interface and user experience been studied from both a subjective and objective view to give insight into customer behaviour? 4 Legal framework / commercial aspects Has the necessary legal framework or commercial aspects that may impact upon use or operation of the software been understood and risks identified and mitigated? 5 Software development What is the software development process? Are both the business management and development team's processes resilient in order to improve the company's capability and the maturity of the software? 6 Software quality What is quality? What are the metrics around software quality? What is the maturity level, based around a qualitative and quantitative assessment? 7 Intellectual property associated What IP should be considered when it comes to software? Does the company understand both the intellectual property risks and potential opportunities associated with this software? 8 Security What does software security mean in this context? How is it being addressed? 9 An holistic approach Review of roles and responsibilities to ensure appropriate management and protection

Learn the basics of Data Science, combining a supported #CISCO Skills for All online course with practical learning and a project to help consolidate the learning.

Learn the skills and mindset to become a business partner, align your expertise with the organisational goals, drive growth, and shape the future of your company. Course overview Duration: 1 day (6.5 hours) This is a highly interactive and practical course which will help you to understand the role of Business Partnering and the key skills needed to be successful in the role. The course is designed specifically to give you an opportunity to explore the role and test a range of techniques to improve your strategic working as well as your influencing and relationship building skills. This course is aimed at individuals who work cross-functionally and with other people to achieve business results and is particularly helpful for those who engage frequently with senior managers and executives. Objectives  By the end of the course you will be able to: Describe the role of Business Partner and the benefit to the business Develop stronger engagement with all internal and external stakeholders Influence without authority Build positive relationships Handle robust conversations Be forward thinking, not reactive Act as a change agent Communicate with confidence and credibility Content Understanding the role of the Business Partner What is Business Partnering from an individual, departmental and business view Using a psychometric test to look at where your strengths are and how you can use them effectively in the role Developing Relationships Creating a powerful first impression. Communication skills Understanding different communication styles Being more proactive in developing key relationships Stakeholder engagement Presenting ideas in a confident and articulate way Understanding and developing trust Connecting with people Networking Influencing and Negotiating Influencing and persuading others Dealing with conflict and difficult conversations Becoming a trusted advisor/Business Partner Advanced questioning skills and techniques to get to the root of a problem Strategic Thinking Develop the mind-set and strategic capability to play a more proactive leading role in the business

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