75 AI (Artificial Intelligence) courses

Learn to efficiently use Midjourney, an AI image-generation tool sweeping the globe. Generate images with artificial intelligence (AI) in a revolutionary way using text prompts or simple drawing tools. So, join this course and let's get your first idea on the screen!

This course starts with the basics of data science and gradually moves towards explaining the concepts of machine learning and various data science algorithms.

Are you fascinated with Netflix and YouTube recommendations and how they accurately recommend content that you would like to watch? Are you looking for a practical course that will teach you how to build intelligent recommendation systems? This course will show you how to build accurate recommendation systems in Python using real-world examples.

This ChatGPT for Marketing and Productivity with AI Tools course is your guide to using AI to boost your marketing results. Boost your marketing skills and productivity to the next level with our comprehensive ChatGPT for Marketing and Productivity with AI Tools course. Dive deep into the world of Artificial Intelligence (AI), its applications, and how it can revolutionise the way you work. This course is meticulously designed to empower marketing professionals, content creators, entrepreneurs, and anyone intrigued by the power of AI.  It's a blend of theoretical understanding, practical exposure, and foresight into the future of AI, particularly in the field of marketing and productivity. In Section 01, we unpack the 'AI Marketing Playbook'. Starting with an introduction to OpenAI's ChatGPT, its possibilities, and its limitations, you'll gain a fundamental understanding of AI capabilities. Following this, delve into practical aspects of using ChatGPT, from generating innovative ideas and content to cross-posting queries and simplifying complex information. Our experts will also guide you on how to leverage AI for business problem-solving and developing methodologies, wrapping up with insights on the future of ChatGPT. In Section 02 get teaching on how to use ChatGPT and other AI tools for effective marketing. Learn to work with Autonomous AI Agents and a variety of AI tools such as Durable, Eightify, Genei, and Ellicit, to name a few. By the end of this section, you'll be equipped with the skills to carry out high-quality research, build AI-based websites, determine research credibility, and clone voices. You'll also get an interesting perspective on the future of AI. Finally, Section 03 is all about enhancing your productivity with ChatGPT and AI tools. From meta-search sites to speech-to-text services, AI design tools, content improvement techniques, and more, this section aims to streamline your work processes. Learn to use tools like Microsoft Bing Search, Google Bard, Speechify, and Adobe for audio enhancements. Wrap up this course with an exploration of generative AI and a glance into the future of this exciting field. Whether you're a beginner or an experienced professional, this course promises to expand your horizons and make you proficient in harnessing AI's power for marketing and productivity. Unleash the potential of AI and transform your work efficiency with this ChatGPT for Marketing and Productivity with AI Tools course. Enrol today and start your AI journey with us! Learning Outcomes Upon completion of the ChatGPT for Marketing course, you will be able to: * Understand the fundamentals of OpenAI's ChatGPT and its capabilities. * Generate and qualify ideas effectively using ChatGPT. * Learn to apply ChatGPT for solving specific business problems. * Develop skills to connect with various Autonomous AI Agents. * Learn to use AI tools for enhanced research and content creation. * Understand how to determine research credibility using AI. * Gain proficiency in utilising AI for website creation and voice cloning. * Develop skills to leverage AI tools for improved productivity. * Understand the future scope of generative AI in marketing. * Master the use of various AI design and content improvement tools. Who is this course for? This ChatGPT for Marketing course is ideal for: * Marketing professionals seeking to leverage AI in their strategies. * Content creators interested in AI-powered idea generation and curation. * Business owners looking to integrate AI into their operational processes. * Individuals interested in exploring AI applications in marketing and productivity. * Any tech enthusiast keen on understanding and applying AI tools. Career Path Our ChatGPT for Marketing course will help you to pursue a range of career paths, such as: * AI Marketing Specialist: £45,000 - £70,000 * Content Strategist: £35,000 - £55,000 * Business Intelligence Analyst: £40,000 - £65,000 * Productivity Consultant: £45,000 - £75,000 * AI Research Analyst: £50,000 - £80,000 * AI Application Developer: £55,000 - £90,000 * Digital Transformation Consultant: £60,000 - £100,000 * AI Solutions Architect: £65,000 - £110,000 PREREQUISITES This Photoshop Training for Beginners does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Photoshop Training for Beginners was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. CERTIFICATION After studying the course materials of the Photoshop Training for Beginners there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. COURSE CURRICULUM Section 01: The AI Marketing Playbook Unit 01: Start an Account with ChatGPT 00:02:00 Unit 02: What the Company OpenAI Say About Itself 00:02:00 Unit 03: What OpenAI Say About The Limitations of the Chatbot 00:02:00 Unit 04: Chatbot Prompt Examples Given By Open AI 00:02:00 Unit 05: Will Chat GPT Be a Paid Application 00:01:00 Unit 06: Chat GPT Idea Generation 00:02:00 Unit 07: Chat GPT - Idea Qualification and Accuracy 00:03:00 Unit 08: ChatGPT - Accuracy and Citations 00:02:00 Unit 09: Chat GPT - Creating HTML Instances 00:01:00 Unit 10: Chat GPT - How to Solve Specific Business Problems 00:02:00 Unit 11: Chat GPT - Statistical Verification of Information 00:01:00 Unit 12: Chat GPT - Rewrite Content for Different Contexts 00:02:00 Unit 13: ChatGPT - Content Checked With AI 00:02:00 Unit 14: ChatGPT - Simplifying Information 00:01:00 Unit 15: ChatGPT - How to Ask the Chatbot about Context 00:01:00 Unit 16: ChatGPT - How to Cross-Post Queries 00:01:00 Unit 17: ChatGPT - How to Narrow Down the Context of Your Query 00:02:00 Unit 18: ChatGPT - How to Solve a Business Process 00:02:00 Unit 19: ChatGPT - Developing a Methodology From Experts 00:01:00 Unit 20: The Future of ChatGPT 00:01:00 Section 02: How to Use ChatGPT and AI for Marketing Unit 01: Autonous AI Agents 00:01:00 Unit 02: Connecting to Open AI 00:01:00 Unit 03: Getting an OpenAI Key 00:01:00 Unit 04: Agent GPT - Autonomous AI 00:02:00 Unit 05: GoalGPT - Autonomous Agents 00:01:00 Unit 06: Cognosis - Autonomous AI 00:02:00 Unit 07: Aomni - Autonomous Agent 00:01:00 Unit 08: Durable - Build a Website with AI 00:01:00 Unit 09: Eightify Summaries 00:02:00 Unit 10: Genei - Do Higher Quality Research with AI 00:01:00 Unit 11: Ellicit - Do Higher Quality Research with AI 00:01:00 Unit 12: Inciteful - Do Higher Quality Research with AI 00:02:00 Unit 13: SciteAI Determine the Credibility of Your Research 00:01:00 Unit 14: Eleven Labs - Voice Cloning 00:02:00 Unit 15: AgentGPT - Wrap Up and Return 00:01:00 Unit 16: Cognosys - Wrap Up and Return 00:01:00 Unit 17: Aomni - Wrap Up and Return 00:01:00 Unit 18: Goal GPT - Wrap Up and Return 00:01:00 Unit 19: Uploading Research Reports to Summarization Applications 00:01:00 Unit 20: Perspective on The Future of AI 00:01:00 Section 03: Productivity with AI Tools Unit 01: Meta Search Sites 00:02:00 Unit 02: SMMRY for Summarzing 00:01:00 Unit 03: ChatGPT Plugins Waitlist 00:01:00 Unit 04: Using Microsoft Bing Search 00:02:00 Unit 05: Using Google Bard 00:01:00 Unit 06: Microsoft Word Speech To Text 00:01:00 Unit 07: Transcribe Audio in Microsoft Word 00:02:00 Unit 08: Speechify 00:02:00 Unit 09: Exact Image Creation 00:01:00 Unit 10: AI Design Tools 00:02:00 Unit 11: Learn How to Prompt 00:01:00 Unit 12: Content Improvement 00:01:00 Unit 13: Idea Generation 00:01:00 Unit 14: Audio Enhancement with Adobe 00:02:00 Unit 15: Clean up Audio With Cleaanvoice 00:01:00 Unit 16: Notion-AI 00:01:00 Unit 17: Pictory 00:01:00 Unit 18: Lex 00:01:00 Unit 19: ChatPDF 00:01:00 Unit 20: Conclusion and the Future of Generatie AI - Searchie 00:01:00

This course covers the basic concepts of machine learning (ML) that are crucial for getting started on the journey of becoming a skilled ML developer. You will become familiar with different algorithms and networks, such as supervised, unsupervised, neural networks, Convolutional Neural Network (CNN), and Super-Resolution Convolutional Neural Network (SRCNN), needed to develop effective ML solutions.

Dive into the revolutionary world of AI image generation with our Mastering Dall-E course. From understanding diffusion models to harnessing Dall-E for creative and commercial use, this course offers a comprehensive guide to mastering this cutting-edge technology.

Duration 5 Days 30 CPD hours This course is intended for The skills covered in this course converge on four areas-software development, IT operations, applied math and statistics, and business analysis. Target students for this course should be looking to build upon their knowledge of the data science process so that they can apply AI systems, particularly machine learning models, to business problems. So, the target student is likely a data science practitioner, software developer, or business analyst looking to expand their knowledge of machine learning algorithms and how they can help create intelligent decisionmaking products that bring value to the business. A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming. This course is also designed to assist students in preparing for the CertNexus Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification Overview In this course, you will develop AI solutions for business problems. You will: Solve a given business problem using AI and ML. Prepare data for use in machine learning. Train, evaluate, and tune a machine learning model. Build linear regression models. Build forecasting models. Build classification models using logistic regression and k -nearest neighbor. Build clustering models. Build classification and regression models using decision trees and random forests. Build classification and regression models using support-vector machines (SVMs). Build artificial neural networks for deep learning. Put machine learning models into operation using automated processes. Maintain machine learning pipelines and models while they are in production Artificial intelligence (AI) and machine learning (ML) have become essential parts of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, all while following a methodical workflow for developing data-driven solutions. SOLVING BUSINESS PROBLEMS USING AI AND ML * Topic A: Identify AI and ML Solutions for Business Problems * Topic B: Formulate a Machine Learning Problem * Topic C: Select Approaches to Machine Learning PREPARING DATA * Topic A: Collect Data * Topic B: Transform Data * Topic C: Engineer Features * Topic D: Work with Unstructured Data TRAINING, EVALUATING, AND TUNING A MACHINE LEARNING MODEL * Topic A: Train a Machine Learning Model * Topic B: Evaluate and Tune a Machine Learning Model BUILDING LINEAR REGRESSION MODELS * Topic A: Build Regression Models Using Linear Algebra * Topic B: Build Regularized Linear Regression Models * Topic C: Build Iterative Linear Regression Models BUILDING FORECASTING MODELS * Topic A: Build Univariate Time Series Models * Topic B: Build Multivariate Time Series Models BUILDING CLASSIFICATION MODELS USING LOGISTIC REGRESSION AND K-NEAREST NEIGHBOR * Topic A: Train Binary Classification Models Using Logistic Regression * Topic B: Train Binary Classification Models Using k-Nearest Neighbor * Topic C: Train Multi-Class Classification Models * Topic D: Evaluate Classification Models * Topic E: Tune Classification Models BUILDING CLUSTERING MODELS * Topic A: Build k-Means Clustering Models * Topic B: Build Hierarchical Clustering Models BUILDING DECISION TREES AND RANDOM FORESTS * Topic A: Build Decision Tree Models * Topic B: Build Random Forest Models BUILDING SUPPORT-VECTOR MACHINES * Topic A: Build SVM Models for Classification * Topic B: Build SVM Models for Regression BUILDING ARTIFICIAL NEURAL NETWORKS * Topic A: Build Multi-Layer Perceptrons (MLP) * Topic B: Build Convolutional Neural Networks (CNN) * Topic C: Build Recurrent Neural Networks (RNN) OPERATIONALIZING MACHINE LEARNING MODELS * Topic A: Deploy Machine Learning Models * Topic B: Automate the Machine Learning Process with MLOps * Topic C: Integrate Models into Machine Learning Systems MAINTAINING MACHINE LEARNING OPERATIONS * Topic A: Secure Machine Learning Pipelines * Topic B: Maintain Models in Production

Master Bing AI from scratch! Dive into practical applications—analyze website links, plan vacations, write CVs, and more, with our beginner-friendly tutorial.

Thinking about learning more about the data you are using in your job and how to present this? The BCS Foundation Award in Data Visualisation teaches how data is used to make decisions in an organisation and the importance of presenting accurate data in a way that enables decision making to happen.

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