851 Art courses in Uppermill delivered Live Online

Duration 5 Days 30 CPD hours This course is intended for This course is designed for API developers. Overview Configure services in Cloud Manager for an on-premises installation of API Connect Create a catalog and Developer Portal Create consumer and provider organizations Create, test, and publish SOAP, REST, and GraphQL APIs Create message processing policies that transform API requests and responses Authorize client API requests with security definitions Enforce an OAuth flow with an OAuth 2.0 API security provider Perform advanced testing of APIs by using the Test tab and the Local Test Environment Define products and plans in API Manager Stage, publish, version, migrate, deprecate, and retire products and APIs Manage member roles and permissions in the Developer Portal Create an application and subscribe to a plan Review API analytics in the Developer Portal Review analytics dashboards and visualizations in API Manager Customize the Developer Portal This course teaches you how to configure a newly built API Connect 10 environment. You are taught how to configure a catalog with the gateway, portal, and analytics services and set up the environment for API development. You then define API interfaces according to the OpenAPI specification. You build SOAP and REST based APIs along with a GraphQL API. You assemble message processing policies and define client authorization schemes, such as OAuth 2.0, in the API definition. You verify the proper sequencing of policies in the assembly tester and further test your APIs in the new Test tab and Local Test Environment. After building and testing your APIs, you publish them and make them available on the Developer Portal. You manage all aspects of the provider organization in the API Manager user interface to create, publish, version, and retire API artifacts such as products, plans and APIs themselves. You also learn how to manage consumer organizations who use the APIs that are made available on the Developer Portal. You learn how to add members to the consumer organization that provides access to the APIs on the Developer Portal. You learn how the layout of the Developer Portal can be customized. Finally, you call the APIs on the secure gateway and you view the graphs and metrics of API usage. Course Outline Introduction to IBM API Connect V10 Exercise: Reviewing the API Connect development and runtime environments Managing catalogs and organizations Exercise: Managing catalogs and consumer organizations Defining APIs in API Manager Exercise: Defining an API that calls an existing SOAP service Defining a REST API in API Manager Exercise: Defining a REST API from a target service Assembling message processing policies Exercise: Assembling message processing policies Declaring client authorization requirements Creating an OAuth 2.0 provider Exercise: Implementing OAuth 2.0 security Testing and debugging APIs Exercise: Introduction to the Test tab Creating and testing a GraphQL API Exercise: Creating and testing a GraphQL API Testing an API in the Local Test Environment Exercise: Testing an API in the Local Test Environment Publishing and managing products and APIs Exercise: Define and publish an API product The product lifecycle Exercise: Subscribing and testing APIs in the Developer Portal Exercise: Managing and approving API Products Subscribing and testing APIs in the Developer Portal API Analytics Exercise: Calling an API on the gateway and monitoring API usage Customizing the Developer Portal Exercise: Customizing the Developer Portal Additional course details: Nexus Humans WD515G Create, Secure, and Publish APIs with IBM API Connect 10 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 WD515G Create, Secure, and Publish APIs with IBM API Connect 10 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 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.

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 work on the ideas introduced in Level 1: Understanding & Dealing with Everyday Racism The Six Stages Framework

Marks4sure is a premium provider of Real and Valid Exam dumps of All IT certifications. Pass your certification exam easily with pdf dumps in 2024

Does your voice abandon you when you present? Do you find you become much less articulate and much less yourself when you are under the spotlight? Have you experienced brain freeze where you "umm" all the way through your presentation? Maybe you blush or your voice wavers. Presenting is a big source of pain for many of us. I've been there. For years, I hated presenting. I went all weird under the spot light and the best I could hope for was just to grit my teeth, force myself to do it and hope for the best. Mostly it was okay, pretty good, a couple of times it was downright traumatic! But sadly, because of my intense dislike of presenting I never felt that i did myself justice. What if I told you that I now relish the opportunity to express myself and my thinking? The bigger the audience, the better! I've done a complete 180 turnaround on giving presentations and I've been reflecting on why. In my next masterclass, I breakdown my 5 top insights that have transformed the way I think about giving presentations. And when you feel good about doing something, your performance improves dramatically.  Whether it's presenting to real people in a real room, or presenting in your living room to a ton of people with their cameras off, my top tips and tools will help you find your natural and confident voice so that you can do yourself justice and maybe even start to enjoy the opportunity.

Get Assignment help and Writing Services Online by University Experts.

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