Advanced Prompt Engineering for Business Users

In this Automating Prompt Engineering with Python Training, you will use the latest and most powerful python and LangChain tools to unlock sustainable business value and get the most value out of artificial intelligence.

5-day, Automating, Prompt Engineering, Python, cutting-edge, artificial intelligence, latest, powerful, LangChain, automate, agents, creative ambition, controlled responsibility, sustainable, business value, Training Delivery Methods, In-Person, Online, Training Information, Benefits, best practice, large language models, framework, techniques, prompt templates, chains, vector embeddings, automated AI tools, public data, autonomous, practical, real-time, competitor analysis, dynamic marketing, insightful, recruitment, industries, Training Prerequisites, write, run, basic Python scripts, principles, libraries, API calls, Working knowledge, writing prompts, chat models, various, AI outputs, AI-generated, conversations, hallucinations, Training Outline, Accordion, drop down header, Introduction, Why, required, advanced, overview, tools, LLM, search, retrievers, Querying, deep dive, In-depth, Hands-on exercises, injection, sequencing, Day 3, Agents, Automation, Configuring, Monitoring, debugging, readiness, production, Day 4, theory, templates, Creating, effective, Injecting, dynamic data, Storing, outputs, vector databases, scaling, Day 5, Commercial, Use Cases, Assessment, Real-world, Hands-on workshop, solving, sample, challenges, Developing, end-to-end, Presentations, review, Capstone assessment, new, future applications, 5-dagars, Automatisering, Prompt Engineering, Python, banbrytande, artificiell intelligens, senaste, kraftfull, LangChain, automatisera, agenter, kreativ ambition, kontrollerat ansvar, hållbar, affärsvärde, Utbildningsleveransmetoder, Personligen, Online, Utbildningsinformation, Fördelar, bästa praxis, stora språkmodeller, ramverk, tekniker, prompt-mallar, kedjor, vektorembeddingar, automatiserade AI-verktyg, offentliga data, autonom, praktisk, realtid, konkurrentanalys, dynamisk marknadsföring, insiktsfull, rekrytering, industrier, Förkunskapskrav, skriva, köra, grundläggande Python-skript, principer, bibliotek, API-anrop, Arbetskunskap, skriva prompts, chattmodeller, olika, AI-utdata, AI-genererade, konversationer, hallucinationer, Utbildningsöversikt, Dragspel, fäll ner rubrik, Introduktion, Varför, nödvändig, avancerad, översikt, verktyg, LLM, sökning, retrievers, Frågeformulering, djupdykning, I detalj, Hands-on övningar, injektion, sekvensering, Dag 3, Agenter, Automation, Konfigurering, Övervakning, felsökning, beredskap, produktion, Dag 4, teori, mallar, Skapande, effektiv, Injicering, dynamiska data, Lagring, utdata, vektordatabaser, skalning, Dag 5, Kommersiell, Användningsfall, Bedömning, Verkliga världen, Hands-on workshop, lösa, exempel, utmaningar, Utveckling, ände-till-ände, Presentationer, granskning, Slutexamination, ny, framtida applikationer, Automating: Autamating, Automting, Automatng Prompt Engineering: Promt Engineering, Prompt Engeneering, Promt Enginering, Python: Pyhton, Pithon, Pythn, Cutting-edge: Cuttin-edge, Cuting-edge, Cuttng-edge, Artificial Intelligence: Artifical Intelligence, Artifitial Intelligence, Artificial Inteligence, LangChain: LangChian, LangChan, LangChainn, Automate: Autmate, Automat, Automte, Agents: Agants, Agnets, Agentz, Creative Ambition: Cretive Ambition, Creative Ambtion, Creatve Ambition, Controlled Responsibility: Controled Responsibility, Controled Responsiblity, Controlled Responsibilty, Sustainable: Sustainble, Sustanable, Sustaineble, Business Value: Buisness Value, Business Valu, Busines Value, Training Delivery Methods: Trainig Delivery Methods, Training Delvery Methods, Training Delevery Methods, In-Person: In-Persn, In-Peson, InPersn, Online: Onlin, On-line, Onlene, Training Information: Trainig Information, Training Informtion, Trainin Information, Benefits: Benifits, Benfits, Beneffits, Best Practice: Best Practise, Best Practie, Bes Practice, Large Language Models: Lrge Language Models, Large Languag Models, Large Langage Models, Framework: Framwork, Frameowrk, Frameork, Techniques: Techniqes, Tehniques, Tecniques, Prompt Templates: Promt Templates, Prompt Tempates, Prompt Templtes, Chains: Chanis, Chians, Chainz, Vector Embeddings: Vctor Embeddings, Vector Embddings, Vectr Embeddings, Automated AI Tools: Automted AI Tools, Automated AI Tols, Automated A Tools Public Data: Pubic Data, Public Dta, Pulbic Data, Autonomous: Autonmous, Autonomus, Atonomous, Practical: Practial, Practcal, Pratical, Real-time: Realtime, Reel-time, Rael-time, Competitor Analysis: Compettior Analysis, Competior Analysis, Competitor Analisis, Dynamic Marketing: Dinamic Marketing, Dyamic Marketing, Dyanmic Marketing, Basic Python Scripts: Basic Pyton Scripts, Basci Python Sctipts, Basic Pythn Scripts, Principles: Pinciples, Princples, Principlez, Libraries: Librairies, Librarys, Libaries, API Calls: AP Calls, AAPI Calls, API Calles, Working Knowledge: Workin Knowledge, Working Knowlege, Woriking Knowledge, Writing Prompts: Writting Prompts, Writin Prompts, Writing Prmopts, Chat Models: Chet Models, Chat Modles, Chat Mmodels, Various: Varous, Verious, Varios, AI Outputs: A Outputs, AI Ouputs, AI Outpts AI-generated: AIgenereted, AI-genrated, AI-genereted, Conversations: Coversations, Conversatons, Conversations Hallucinations: Halucinations, Halluciantions, Hallucintions, Training Outline: Trainig Outline, Training Outlin, Trainning Outline, Accordion: Acordion, Acordeon, Accordian, Drop Down Header: Drop Dwn Header, Drop Down Hedder, Dropp Down Header, Introduction: Introducton, Introdction, Introdcution, Why: Whi, Wai, Whyy, Required: Requried, Requred, Reqiured, Advanced: Adavanced, Advaced, Advansed, Overview: Overveiw, Oerview, Overviw, Tools: Toos, Tollz, Tols, LLM: LML, LMM, LLMs, Search: Searhc, Serach, Saerch, Retrievers: Retreivers, Retrievrs, Retriverz, Querying: Queering, Querrying, Queryng, Deep Dive: Depp Dive, Deep Dve, Deeep Dive, In-depth: In-deph, Indepth, In-Depth, Hands-on Exercises: Hands on Exersises, Hands-on Exercizes, Hands-om Exercises, Injection: Injction, Injecton, Injetion, Sequencing: Seqencing, Sequenceing, Sequecning, Agents: Agnts, Agens, Aggents, Automation: Autmation, Automtion, Automattion, Configuring: Configurin, Confguring, Configurring, Monitoring: Monitering, Monitring, Monitorng, Debugging: Debuggin, Deugging, Debgging, Readiness: Rediness, Readines, Readyness Production: Prodution, Produktion, Producton, Theory: Theroy, Thoery, Theorie, Templates: Tempates, Templats, Templaets, Creating: Creatin, Creatng, Cretating, Effective: Efective, Effctive, Affective, Injecting: Injectin, Inecting, Injecing, Dynamic Data: Dyanmic Data, Dinamic Data, Dynamic Dta, Storing: Storeing, Storin, Stroring, Outputs: Ouputs, Outputz, Outpts, Vector Databases: Vctor Databases, Vector Datbases, Vector Databses, Scaling: Scalling, Scaing, Scaeling, Commercial: Comercial, Commecial, Commerial, Use Cases: Usee Cases, Us Cases, Use Caes, Assessment: Assement, Assesment, Asssesment, Real-world: Realword, Reel-world, Reall-world, Hands-on Workshop: Hands on Workshp, Hands-on Workshopp, Handson Workshop, Solving: Solvin, Solvng, Sollving, Sample: Sampl, Sampel, Sammple, Challenges: Challanges, Chalenges, Challengs, Developing: Develping, Developin, Develloping, End-to-end: End to end, End-toEnd, End-2-end, Presentations: Presntations, Presenttions, Presentationz,

This hands-on Introduction to Artificial Intelligence course will introduce you to the key concepts in modern AI and discuss how they apply to you and your business.

Introduction to AI,Key concepts in modern AI,Hands-on AI,Challenges of AI adoption,Artificial Intelligence Training,Confidence in AI,Artificial Intelligence for businesses,Machine learning,Uniform decision-making,Deep learning,Interpret images,AI limitations,AI risks,Module 1,What is Artificial Intelligence?,AI terms,AI technologies,AI constraints,AI limitations,Define intelligence,Module 2,Machines that learn,Machine learning,Benefits of AI,AI best practices,AI effectiveness,Module 3,Image recognition,Neural networks,Teaching neural networks,Classify complex images,Module 4,Chatbots,Generative AI,AI applications,Generative AI potential,Module 5,Protecting AI Systems,AI security,AI risks,Protecting your business,Protecting your privacy,Module 6,AI society and ethics,Biases in AI,Ethical use of AI,AI impact on society,Business context of AI,Module 7,The future of AI,AI threat on humanity,Future of work,AI in business future, Introduktion till AI,Nyckelkoncept inom modern AI,Hands-on AI,Utmaningar med AI-adoption,Artificiell Intelligensutbildning,Självförtroende i AI,Artificiell intelligens för företag,Maskininlärning,Uniformt beslutsfattande,Djupinlärning,Tolkning av bilder,AI begränsningar,AI risker,Modul 1,Vad är artificiell intelligens?,AI-termer,AI-teknologier,AI-begränsningar,AI-begränsningar,Definiera intelligens,Modul 2,Maskiner som lär sig,Maskininlärning,Fördelar med AI,AI bästa praxis,AI effektivitet,Modul 3,Bildigenkänning,Neurala nätverk,Undervisa neurala nätverk,Klassificera komplexa bilder,Modul 4,Chattbots,Generativ AI,AI-applikationer,Generativ AI potential,Modul 5,Att skydda AI-system,AI-säkerhet,AI risker,Skydda ditt företag,Skydda din integritet,Modul 6,AI, samhälle och etik,Bias inom AI,Etisk användning av AI,AI:s påverkan på samhället,Affärssammanhang av AI,Modul 7,AI:s framtid,AI-hot mot mänskligheten,Framtiden för arbete,AI i affärsframtid

This AI ML Data Science Python course teaches you how to use Python libraries to build, evaluate, & deploy Machine Learning & Artificial Intelligence models.

Introduction to Data Science, Machine Learning & AI using Python, CHAT GPT, , CHAT , CHAT GPTT, AI ML Data Science Python Course, Data Science, Python, Course, Machine Learning, Artificial Intelligence, AI, ML, Libraries, Build, Evaluate, Deploy, Models, Insights, Lifecycle, Manage, Transform, Visualize, Raw Data, Predictive Models, Opportunities. Data Science Data Science, Data Sciencee, Data Scince, Data Scienze Python Pyhton, Pyton, Pithon Course Cource, Coursee, Coourse Machine Learning Machine Leanring, Machne Learning, Machin Learning Artificial Intelligence Artifical Intelligence, Artificiall Intelligence, Artifitial Intelligence AI A.I, Ai, Ai, A I ML M.L, Ml, m.l Libraries Libaries, Libarys, Librarys Build Bild, Buildd, Buil Evaluate Evalute, Evalute, Evalute Deploy Deply, Deplooy, Deployy Models Model, Modells, Modles Insights Insigths, Insightts, Insight Lifecycle Life Cycle, Life-cycle, Lifecyle Manage Manag, Managge, Managa Transform Tranform, Transorm, Transfrom Visualize Visualise, Vizualize, Vizualise Raw Data Raw Datta, Raw Dta, Raw Dataa Predictive Models Predective Models, Predictive Model, Predictive Modells Opportunities Oppurtunities, Oppertunities, Opportunitis Data Science, Python, Kurs, Maskininlärning, Artificiell Intelligens, AI, ML, Bibliotek, Bygga, Utvärdera, Implementera, Modeller, Insikter, Livscykel, Hantera, Transformera, Visualisera, Rådata, Prediktiva modeller, Möjligheter. Data Science Data Science, Data Sciencee, Data Scince, Data Scienze Python Pyhton, Pyton, Pithon Kurs Cource, Coursee, Coourse Maskininlärning Machine Leanring, Machne Learning, Machin Learning Artificiell Intelligens Artifical Intelligence, Artificiall Intelligence, Artifitial Intelligence AI A.I, Ai, Ai, A I ML M.L, Ml, m.l Bibliotek Libaries, Libarys, Librarys Bygga Bild, Buildd, Buil Utvärdera Evalute, Evalute, Evalute Implementera Deply, Deplooy, Deployy Modeller Model, Modells, Modles Insikter Insigths, Insightts, Insight Livscykel Life Cycle, Life-cycle, Lifecyle Hantera Manag, Managge, Managa Transformera Tranform, Transorm, Transfrom Visualisera Visualise, Vizualize, Vizualise Rådata Raw Datta, Raw Dta, Raw Dataa Prediktiva modeller Predective Models, Predictive Model, Predictive Modells Möjligheter Oppurtunities, Oppertunities, Opportunitis

Join our hands-on 2-day course to build practical skills in NLP and Generative AI training to develop advanced AI models like ChatGPT from OpenAI.

2-day course,Building Practical Skills in NLP and Generative AI,Generative AI,ChatGPT,OpenAI,Natural Language Processing (NLP),advanced architectures,Generative AI,modules,hands-on labs,Skills in NLP and Generative AI Training Delivery Methods,In-Person,Online,Training Information,Understanding of NLP and Generative AI,Applying Advanced AI Techniques,GRUs,LSTMs,Transformer architectures,Transitioning from traditional NLP to modern methods,Bag-of-Words,word embeddings,advanced neural networks,Lack of practical AI experience,Training Prerequisites,Python programming,machine learning,deep learning,Training Outline,Day 1,Understanding NLP and Sequential Data Processing,Introduction to Natural Language Processing (NLP),Overview of NLP,Traditional NLP Techniques,Bag-of-Words (BoW),Lab 1,Text Classification,Moving to Word Embeddings,Word2Vec,GloVe,Applying NLP,Sentiment Analysis,Lab 2,Restaurant Review Sentiment Analysis,Sequential Data in NLP,Recurrent Neural Networks (RNNs),Gated Recurrent Units (GRUs),Lab 3,Text Generation,Day 2,Deep Dive into Advanced Neural Networks and Generative AI,Long Short-Term Memory Networks (LSTM),Lab 4,Autoencoders,Sequence-to-Sequence Models (Seq2Seq),Lab 5,Machine Translation,Attention Mechanisms,Transformers,BERT,GPT,Lab 6,Language Models (LLMs),Deep and Wide Architectures in AI,future trends,generative AI,2-day coruse,Bulding Practical Skills in NLP and Generative AI,Gnerative AI,ChatGPT,OpneAI,Natural Language Procesing (NLP),advnced architectures,Genrative AI,moduels,hands-on lbas,Skills in NLP and Generative AI Training Delivery Mthods,In-Person,Oline,Training Informatin,Understanding of NLP and Generative AI,Applying Advanced AI Techniqes,GRUs,LSTMs,Trasnformer architectures,Transitioning from traditional NLP to mdoern methods,Bag-of-Wrds,word embeddins,advanced neural ntworks,Lack of practical AI experiene,Training Prerequisits,Python programmng,machine learnin,deep learnig,Training Otline,Day 1,Understanding NLP and Sequential Data Procesing,Introduction to Natural Language Procesing (NLP),Overview of NPL,Traditional NLP Techinques,Bag-of-Words (BoW),Lab 1,Text Classifcation,Moving to Word Embeddngs,Word2Vec,Glve,Applying NPL,Sentiment Analisis,Lab 2,Restaurant Review Sentiment Analisis,Sequential Data in NPL,Recurrent Neural Networks (RNNs),Gated Recurrent Units (GRUs),Lab 3,Text Generatin,Day 2,Deep Dive into Advanced Neural Networks and Generative AI,Long Short-Term Memory Networks (LSTM),Lab 4,Autencoders,Sequence-to-Sequence Models (Seq2Seq),Lab 5,Machine Traslation,Attention Mechanisms,Tranformers,BERT,GPT,Lab 6,Language Models (LLMs),Deep and Wide Architectures in AI,future trend,generative AI,2-dagars kurs,Att bygga praktiska färdigheter i NLP och generativ AI,Generativ AI,ChatGPT,OpenAI,Natural Language Processing (NLP),avancerade arkitekturer,Generativ AI,moduler,hands-on labb,Färdigheter i NLP och generativ AI-utbildningens leveransmetoder,På plats,Online,Utbildningsinformation,Förståelse av NLP och generativ AI,Tillämpning av avancerade AI-tekniker,GRUer,LSTMer,Transformer-arkitekturer,Övergång från traditionell NLP till moderna metoder,Bag-of-Words,ord inbäddningar,avancerade neurala nätverk,Brist på praktisk AI-erfarenhet,Utbildningsförutsättningar,Python programmering,maskinlärning,djupinlärning,Utbildningsschema,Dag 1,Förståelse för NLP och sekventiell datahantering,Introduktion till Natural Language Processing (NLP),Översikt över NLP,Traditionella NLP-tekniker,Bag-of-Words (BoW),Lab 1,Textklassificering,Övergång till ord inbäddningar,Word2Vec,GloVe,Tillämpning av NLP,Känslanalys,Lab 2,Restaurangrecension Känslanalys,Sekventiell data i NLP,Rekurrenta neurala nätverk (RNNs),Gated Recurrent Units (GRUs),Lab 3,Textgenerering,Dag 2,Djupdykning i avancerade neurala nätverk och generativ AI,Lång korttidsminnesnätverk (LSTM),Lab 4,Autoenkodare,Sequence-to-Sequence-modeller (Seq2Seq),Lab 5,Maskinöversättning,Upmärksamhetsmekanismer,Transformatorer,BERT,GPT,Lab 6,Språkmodeller (LLMs),Djupa och breda arkitekturer i AI,framtida trender,generativ AI,ytterligare läsning och studier

This 1-hour ChatGPT for Business Users: On-Demand course will explore Conversational AI and develop your own Prompt Engineer to streamline prompt creation and ensure accurate responses.

OnDemand course, Conversational AI, ChatGPT, related terms, applications, business, personal life, day-to-day activities, office, optimise features, prompts, responses, improve results, Prompt Engineer, streamline prompt creation, accurate responses, familiarise, daily use, valuable tips, utilising effectively, develop skills, detailed prompts, optimal results, Business Users, On-Demand Training Outline, Introduction, comprehensive understanding, practical ways, daily interactions, Getting Started, guide, better results, queries, Engineering, customisation, personalised prompts, best responses, specific business use cases,OnDemand-kurs, Konversationell AI, ChatGPT, relaterade termer, applikationer, affär, personligt liv, vardagliga aktiviteter, kontor, optimera funktioner, uppmaningar, svar, förbättra resultat, Prompt Engineer, effektivisera skapandet av uppmaningar, korrekta svar, bekanta sig, daglig användning, värdefulla tips, utnyttja effektivt, utveckla färdigheter, detaljerade uppmaningar, optimala resultat, Affärsanvändare, On-Demand-utbildningsplan, Introduktion, omfattande förståelse, praktiska sätt, dagliga interaktioner, Komma igång, guide, bättre resultat, förfrågningar, Teknik, anpassning, personliga uppmaningar, bästa svar, specifika affärsfall.

In this Graph Databases for Data Analytics and AI Training course, you will learn how databases for graph data can support data analytics and artificial intelligence in ways not practical with traditional relational databases.

In this Graph Databases for Data Analytics and AI Training course, you will learn how databases for graph data can support data analytics and artificial intelligence in ways not practical with traditional relational databases. You will see practical and profitable use cases and understand the importance of graph data in today’s world. You will master the practical aspects of importing and exporting data for graph databases, and how graph databases are searched and queried. Graph databases are also ideal for the representation of human knowledge, not just simple facts. You will understand how knowledgebase graphs integrate with artificial intelligence systems and learn to use these systems to provide business analyses and insights. Why Graph Databases are Important Objectives: • Understand what graph data is and why it is important • Explore a few or the many practical applications of graph databases Contents: • What are graphs? • How graphs are used to provide structure to data • Business use-cases of graph databases Module 2: Foundations of Graphs and Graph Data Objectives: • Storing graph data in simple files • What is a relational database? • How graph databases differ from traditional relational databases Contents: • How graphs are used to provide structure to data o Graph software • Graph data in files o File format alternatives o Scripts for manipulating and characterising graph data • Diagramming graphs o Diagramming graphs with Python o Using GraphViz software Module 3: Graph Database Platforms Objectives: • Examine commercial graph database systems • Understand the differences among graph database systems Contents: • Options for graph database systems • Introduction to Neo4j • Introduction to GraphDB • Introduction to CogniPy Module 4: Querying and Analysing Graph Data Objectives: • Learn how to query data in a graph database • Understand the differences among graph database query languages Contents: • Using the Neo4j browser • The Cypher® graph query language o Structure of Cypher® queries o Viewing and interpreting query results • Other graph query languages • GraphQL • Gremlin • SPARQL Module 5: Using Graph Database APIs to Build Systems Objectives: • Understand how APIs are used to build a system • Explore some examples using Python Contents: • Using Python to manage and query a Neo4j graph database • How graphs are used to provide structure to data Module 6: Knowledge Graphs Objectives: • Learn how human knowledge can be represented in graph data using semantic nets • Create a simple knowledge graph • Understand ontologies Contents: • Semantic nets and the Resource Description Framework (RDF) o Formats for representing RDF triples • Ontologies o The structure of ontology documents o Tools for creating and editing ontologies o Protege and other Tools • Semantic Nets and Knowledge graphs o Loading knowledge graphs into a graph database o Applying logic-based “reasoners” to knowledge graphs Module 7: Graph Neural Networks Objectives: • Understand the Need for “Hybrid” AI • Applying convolutional neural networks to graph data Contents: • “Hybrid” AI o Creating graph neural networks • Applying convolutional neural networks to graph data o Review the basics of convolutional neural networks o Training the hybrid AI