14196 AI courses delivered Online

This course provides basic introductory guidance to FinTech. You will be using an easy programming language R to learn some basic statistics in money management. You will also understand how to time the stock market and build tradeable factor-based algorithms from scratch. This course provides some of the most basic rules of thumb and intuition that every successful trader should know.

Learn everything you need to know to be fully competent with Window OS. This syllabus takes you around the basics and then on another deep dive into all the elements. Discover things you never knew and speed up your experience using Windows OS. Module 1: Introduction to Windows OS • Understanding the Windows operating system • Navigating the Windows interface • Customizing system settings and preferences Module 2: File Management • Managing files and folders in Windows Explorer • Copying, moving, and renaming files • Using the Recycle Bin and data recovery Module 3: Windows Built-in Apps • Using Microsoft Edge for web browsing • Effective web searching using search engines • Email management with Windows Mail • Calendar and task management with Windows Calendar Module 4: Software Installation and Updates • Installing and updating software applications • Managing and uninstalling programs • Windows Store and app installations Module 5: Microsoft Office Basics • Introduction to Microsoft Office suite • Using Microsoft Word for document creation • Basic spreadsheet management with Microsoft Excel Module 6: Microsoft Office Intermediate Skills • Advanced features in Microsoft Word • Creating and formatting spreadsheets in Microsoft Excel • Creating dynamic presentations with PowerPoint Module 7: Multimedia and Graphics • Basic image editing with Paint • Using Windows Photo app for photo management • Creating graphics with Paint 3D Module 8: Productivity and Collaboration • Using OneDrive for cloud-based storage and collaboration • Working with Windows Sticky Notes and To-Do • Collaborative editing with Microsoft Office Online Module 9: Troubleshooting and Maintenance • Identifying and resolving common Windows issues • Using Task Manager for performance monitoring • Maintenance tasks for Windows OS Module 10: Windows Security and Privacy • Overview of Windows security features • Online safety and privacy best practices • Protecting personal data and devices Module 11: Advanced Windows Features • Customizing the Windows Start Menu and Taskbar • Using Cortana for voice commands and search • Virtual desktops and advanced multitasking Module 12: Using AI and Chat GPT • Introduction to AI and Chat GPT technology • Exploring AI-powered features in Windows • Using Chat GPT for productivity and assistance Module 13: Browsing and Search Engines • Effective use of web browsers • Utilizing search engines for research • Online safety and privacy while browsing Module 14: Cybersecurity • Understanding cybersecurity threats • Protecting against malware and phishing attacks • Secure online practices and password management Module 15: Software Installation and Factory Reset • Installing and updating software applications • Factory resetting a Windows device • Data backup and recovery during resets Module 16: Final Projects and Assessment • Culminating projects showcasing Windows OS skills • Practical exams assessing Windows software knowledge and skills • Preparing for industry-recognized certifications (optional) Please note that the duration and depth of each module can vary depending on the level of expertise required and the specific needs of the learners. Additionally, it's important to adapt the curriculum to the learners' proficiency levels, whether they are A Level/GCSE students or adult learners with different experience levels.

ChatGPT for Marketing and Productivity with AI Tools Course Overview: This course provides an in-depth exploration of ChatGPT and other AI tools in the context of marketing and productivity. Designed for individuals keen on integrating AI into their business strategies, it covers essential techniques and applications to enhance marketing efforts and streamline work processes. Learners will gain insights into leveraging AI for targeted campaigns, content creation, and automation, while also learning how to increase personal and team productivity using AI tools. By the end of the course, learners will have a clear understanding of how to apply AI-driven solutions to achieve measurable results in marketing and productivity. Course Description: In this course, learners will explore the dynamic field of AI-powered marketing and productivity tools. Key topics include the AI Marketing Playbook, which introduces learners to the fundamentals of using AI in marketing, followed by strategies for utilising ChatGPT and other AI tools for content creation, social media campaigns, and customer engagement. Additionally, learners will discover various AI tools designed to optimise productivity, including project management, data analysis, and communication tools. This course provides a comprehensive approach, equipping learners with the knowledge to harness AI’s capabilities in improving both marketing efforts and workplace efficiency. ChatGPT for Marketing and Productivity with AI Tools Curriculum: Module 01: The AI Marketing Playbook Module 02: How to Use ChatGPT and AI for Marketing Module 03: Productivity with AI Tools (See full curriculum) Who is this course for? Individuals seeking to enhance their marketing efforts with AI. Professionals aiming to boost their productivity using AI-driven tools. Beginners with an interest in AI technologies and marketing. Business owners looking to streamline marketing and productivity. Career Path: Digital Marketing Specialist Marketing Automation Expert AI Solutions Specialist Productivity Consultant Marketing Manager

Data-Informed Decision Making in Projects: On-Demand Project management professionals constantly need to make project decisions that could be decisive for the outcome of their projects but often do not have sufficient information available to confidently make decisions. As a result, projects are increasingly falling short of delivering on their promises, requiring, more than ever, a data-informed approach to decision-making in the area of project delivery and management. The rapid growth of data comes with various challenges though, which consequently needs consideration of various critical factors for a successful implementation of a data-informed decision-making process in organizations and projects. What You Will Learn At the end of this program, you will be able to: Describe and understand the relevant methods and techniques to identify, acquire, and analyze relevant data points for decision making in projects Articulate analytical questions to focus on the real problems Identify potential shortfalls and gaps in project decision-making and apply actions to mitigate them Introduction to Data-Informed Decision Making The different types of decisions in projects Data-informed decision-making framework Shortcomings with traditional decision-making models Understanding the value of data for project delivery Issues in project management and how data can help solve them The DIKW Pyramid (Data, information, knowledge, wisdom) Types of data in projects Applying Data Analytics Understanding Data Analytics Levels of Data Analytics Data-Informed vs. Data-Driven Challenges and How to Address Them Project data availability and collection Data quality Behavioral blockers and bias Skills and Techniques Data literacy and data fluency Communicating for informed decision-making Monitoring and evaluating project decisions Implementing Data-Informed Decision Making Decision-making strategy and governance Project data culture Continuously improving decision quality Future Outlook for Decision-Making in Projects Data and AI Digital Decisioning

Data-Informed Decision Making in Projects: On-Demand Project management professionals constantly need to make project decisions that could be decisive for the outcome of their projects but often do not have sufficient information available to confidently make decisions. As a result, projects are increasingly falling short of delivering on their promises, requiring, more than ever, a data-informed approach to decision-making in the area of project delivery and management. The rapid growth of data comes with various challenges though, which consequently needs consideration of various critical factors for a successful implementation of a data-informed decision-making process in organizations and projects. What You Will Learn At the end of this program, you will be able to: Describe and understand the relevant methods and techniques to identify, acquire, and analyze relevant data points for decision making in projects Articulate analytical questions to focus on the real problems Identify potential shortfalls and gaps in project decision-making and apply actions to mitigate them Introduction to Data-Informed Decision Making The different types of decisions in projects Data-informed decision-making framework Shortcomings with traditional decision-making models Understanding the value of data for project delivery Issues in project management and how data can help solve them The DIKW Pyramid (Data, information, knowledge, wisdom) Types of data in projects Applying Data Analytics Understanding Data Analytics Levels of Data Analytics Data-Informed vs. Data-Driven Challenges and How to Address Them Project data availability and collection Data quality Behavioral blockers and bias Skills and Techniques Data literacy and data fluency Communicating for informed decision-making Monitoring and evaluating project decisions Implementing Data-Informed Decision Making Decision-making strategy and governance Project data culture Continuously improving decision quality Future Outlook for Decision-Making in Projects Data and AI Digital Decisioning

🔬 Unlock the Secrets of Chemistry with Chemically Speaking Two! 🌐 Ready to take your understanding of chemistry to the next level? Welcome to Chemically Speaking Two, where the world of molecules and reactions comes alive in a dynamic online learning experience!   🚀 What's Inside Chemically Speaking Two? 🧪 Advanced Concepts, Simplified: Dive into the intricacies of organic chemistry, chemical kinetics, thermodynamics, and more. Our expert instructors break down complex topics into digestible lessons, making even the most challenging concepts accessible. 🌎 Real-World Applications: Explore how chemistry shapes the world around us. From pharmaceuticals to environmental science, learn how chemical principles impact everyday life and industries worldwide. 🎓 Interactive Learning: Say goodbye to boring lectures! Engage in interactive simulations, virtual labs, and quizzes that reinforce your understanding and make learning enjoyable. 🤝 Community Support: Join a vibrant community of learners passionate about chemistry. Connect with fellow students, share insights, and collaborate on projects to enhance your learning journey. 📆 Flexible Learning: Life is busy, and we get it. Chemically Speaking Two offers a flexible schedule, allowing you to learn at your own pace. Access course materials 24/7 from anywhere in the world.   🎁 Exclusive Bonuses: 📚 Comprehensive Study Materials: Access downloadable resources, study guides, and reference materials to reinforce your knowledge. 🤖 AI-Powered Tutoring: Benefit from AI-assisted tutoring sessions to get personalized help and guidance tailored to your progress. 🏆 Certification: Earn a prestigious certification upon course completion, validating your expertise in advanced chemistry.   🔥 Limited-Time Offer: Enroll now and enjoy a special discount! Don't miss the chance to elevate your understanding of chemistry with Chemically Speaking Two. 🌐 Join the Chemistry Revolution! Enroll Today! 🌐 Course Curriculum Chemically Speaking Two Intro Video Chemically Speaking Two Intro Video 00:00 Dual Diagnosis Dual Diagnosis 00:00 Integrative Holistic Model Integrative Holistic Model 00:00 Needs Underlying Issues Needs Underlying Issues 00:00 Grief Cycle Anger Grief Cycle Anger 00:00 Drink Drug Driving Drink Drug Driving 00:00 Cycle Of Addiction Cycle Of Addiction 00:00 Optimal Health Optimal Health 00:00 Stages Of Change Brief Intervention Stages Of Change Brief Intervention 00:00

Duration 3 Days 18 CPD hours This course is intended for Senior Consultants (both functional and technical) that aspire to be Solution Architects, or current Solution Architects that are new to the role. The Solution Architect is responsible for the successful design, implementation, deployment and adoption of an overall solution. The Solution Architect ensures that the solution meets the customer?s needs now and in the future. In this course, students will learn about decisions a Solution Architect makes during an implementation, covering security, integrations, Power Apps architecture, Power Automate architecture, and more. This course is designed to give you an introduction to the Solution Architect role. Becoming a Solution Architect/Getting to know your customer Define a Solution Architect Role of a Solution Architect on projects Project Methodology Getting to know your customer Group exercise - Getting to know your customer Conceptualizing the design from requirements How to lead the requirement collection effort Using fit gap analysis Pillars of good architecture Blueprinting the solution architecture Group exercise - Design from requirements Project governance and working as a team Solution Architect's role in project governance Techniques for keeping a project on track Scenarios that could cause a project to fail Group exercise - Project governance and working as a team Power Platform Architecture Key Power Platform architecture components Understand how platform design and limits influence solution architectures Updates and feature releases Understand how to communicate how the platform meets customer needs Data Modeling Data model influences Data model strategy Data types Data relationships Group exercise - Data modeling Analytics and artificial intelligence Planning and evaluating requirements Operational reporting Power BI Enterprise BI Pre-built insights and custom AI Power Apps Architecture Discuss options for apps and how to choose where to start Discuss app composition options Using components as part of your app architecture Considerations for including Portals as an app in your architecture Group exercise - Power Apps Architecture topics Application Lifecycle Management (ALM) Microsoft vision and Solution Architect's role in ALM Environment strategies Defning a solution structure for your deliverable Power Automate Architecture Discuss options for automation and custom logic Review considerations for using triggers and common actions Explore using Business Process Flows (BPF) to guide users through business processes Group Exercise - Evaluate scenarios for Power Automate usage Security Modeling Solution Architect's role in security modeling Discovery and learning your client's environment Controlling access to environments and resources Controlling access to CDS Data Group Exercise - Security Modeling Integration Solution Architects role in Integrations What is an integration and why do we need it Platform features that enable integration CDS Event Publishing Scenarios for group discussion Dynamics 365 Applications Architecture Solution Architect's role when deploying Dynamics 365 apps Architecture Considerations for primary apps Group Exercise - App specific working groups evaluate requirements Power Virtual Agents architecture Introduction Chatbot options Chatbot concepts Best practices Integrate chatbots Power Virtual Agents in Microsoft Teams Robotic Process Automation Introduction Power Automate Desktop Recording and editing tasks Running desktop flows Process advisor Testing and Go Live Solution Architect's role with testing and go live Planning for testing Planning for go live

Duration 4 Days 24 CPD hours This course is intended for This class is intended for experienced developers who are responsible for managing big data transformations including: Extracting, loading, transforming, cleaning, and validating data. Designing pipelines and architectures for data processing. Creating and maintaining machine learning and statistical models. Querying datasets, visualizing query results and creating reports Overview Design and build data processing systems on Google Cloud Platform. Leverage unstructured data using Spark and ML APIs on Cloud Dataproc. Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow. Derive business insights from extremely large datasets using Google BigQuery. Train, evaluate and predict using machine learning models using TensorFlow and Cloud ML. Enable instant insights from streaming data Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hand-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data. Introduction to Data Engineering Explore the role of a data engineer. Analyze data engineering challenges. Intro to BigQuery. Data Lakes and Data Warehouses. Demo: Federated Queries with BigQuery. Transactional Databases vs Data Warehouses. Website Demo: Finding PII in your dataset with DLP API. Partner effectively with other data teams. Manage data access and governance. Build production-ready pipelines. Review GCP customer case study. Lab: Analyzing Data with BigQuery. Building a Data Lake Introduction to Data Lakes. Data Storage and ETL options on GCP. Building a Data Lake using Cloud Storage. Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions. Securing Cloud Storage. Storing All Sorts of Data Types. Video Demo: Running federated queries on Parquet and ORC files in BigQuery. Cloud SQL as a relational Data Lake. Lab: Loading Taxi Data into Cloud SQL. Building a Data Warehouse The modern data warehouse. Intro to BigQuery. Demo: Query TB+ of data in seconds. Getting Started. Loading Data. Video Demo: Querying Cloud SQL from BigQuery. Lab: Loading Data into BigQuery. Exploring Schemas. Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA. Schema Design. Nested and Repeated Fields. Demo: Nested and repeated fields in BigQuery. Lab: Working with JSON and Array data in BigQuery. Optimizing with Partitioning and Clustering. Demo: Partitioned and Clustered Tables in BigQuery. Preview: Transforming Batch and Streaming Data. Introduction to Building Batch Data Pipelines EL, ELT, ETL. Quality considerations. How to carry out operations in BigQuery. Demo: ELT to improve data quality in BigQuery. Shortcomings. ETL to solve data quality issues. Executing Spark on Cloud Dataproc The Hadoop ecosystem. Running Hadoop on Cloud Dataproc. GCS instead of HDFS. Optimizing Dataproc. Lab: Running Apache Spark jobs on Cloud Dataproc. Serverless Data Processing with Cloud Dataflow Cloud Dataflow. Why customers value Dataflow. Dataflow Pipelines. Lab: A Simple Dataflow Pipeline (Python/Java). Lab: MapReduce in Dataflow (Python/Java). Lab: Side Inputs (Python/Java). Dataflow Templates. Dataflow SQL. Manage Data Pipelines with Cloud Data Fusion and Cloud Composer Building Batch Data Pipelines visually with Cloud Data Fusion. Components. UI Overview. Building a Pipeline. Exploring Data using Wrangler. Lab: Building and executing a pipeline graph in Cloud Data Fusion. Orchestrating work between GCP services with Cloud Composer. Apache Airflow Environment. DAGs and Operators. Workflow Scheduling. Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery. Monitoring and Logging. Lab: An Introduction to Cloud Composer. Introduction to Processing Streaming Data Processing Streaming Data. Serverless Messaging with Cloud Pub/Sub Cloud Pub/Sub. Lab: Publish Streaming Data into Pub/Sub. Cloud Dataflow Streaming Features Cloud Dataflow Streaming Features. Lab: Streaming Data Pipelines. High-Throughput BigQuery and Bigtable Streaming Features BigQuery Streaming Features. Lab: Streaming Analytics and Dashboards. Cloud Bigtable. Lab: Streaming Data Pipelines into Bigtable. Advanced BigQuery Functionality and Performance Analytic Window Functions. Using With Clauses. GIS Functions. Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz. Performance Considerations. Lab: Optimizing your BigQuery Queries for Performance. Optional Lab: Creating Date-Partitioned Tables in BigQuery. Introduction to Analytics and AI What is AI?. From Ad-hoc Data Analysis to Data Driven Decisions. Options for ML models on GCP. Prebuilt ML model APIs for Unstructured Data Unstructured Data is Hard. ML APIs for Enriching Data. Lab: Using the Natural Language API to Classify Unstructured Text. Big Data Analytics with Cloud AI Platform Notebooks What's a Notebook. BigQuery Magic and Ties to Pandas. Lab: BigQuery in Jupyter Labs on AI Platform. Production ML Pipelines with Kubeflow Ways to do ML on GCP. Kubeflow. AI Hub. Lab: Running AI models on Kubeflow. Custom Model building with SQL in BigQuery ML BigQuery ML for Quick Model Building. Demo: Train a model with BigQuery ML to predict NYC taxi fares. Supported Models. Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML. Lab Option 2: Movie Recommendations in BigQuery ML. Custom Model building with Cloud AutoML Why Auto ML? Auto ML Vision. Auto ML NLP. Auto ML Tables.

Prepare for success with the Microsoft PL-900 Certification Course, covering the fundamentals of Power Platform, including Power BI, Power Apps, Power Automate, Power Virtual Agents, and related topics such as Dataverse, AI Builder, Connectors, Dynamics 365, Teams, Security, and Administration. Suitable for beginners with no prerequisites.

This course will teach you how to link ChatGPT's API directly into your applications and solutions. Learn to integrate the API into Power Apps. Build any business application you want using Power Apps, which can now incorporate ChatGPT. Extend ChatGPT to any platform, including React, Webflow, Zapier, Excel, and so on.