[{"Course Category": "Business Analytics ", "Course Name": "Introduction to business analytics", "Course Url": "https://courses.analyticsvidhya.com/courses/introduction-to-analytics", "Details": "About Introduction to Business Analytics\nGetting Started with Business Analytics\nWhat is Business Analytics? Why has it become so popular recently? What are some of the popular applications of Business Analytics? And more importantly, how can you get started with learning Business Analytics from scratch?\nWith growth in digitisation, Business Analytics is ubiquitous right now. Organizations are splurging to integrate data science solutions in their daily processes. This is where they need Business Analysts.\nWhy pursue Business Analytics:\nData is ubiquitous! Organizations need people who can use Business Analytics tools and techniques to make sense of this data. \nIt is one of the hottest field in the industry right now There are so many Business Analytics tools and techniques which can be applied to solve business problems. Keep learning, keep growing!\nThe potential of Business Analytics is limitless - spanning across industries, roles and functions\nCourse Curriculum\nWhat is Business Analytics?\nWhat is Business Analytics?What is Business Analytics\nYou just joined an exicting startup!\nMap the Job families\nData Scientist vs. Data Engineer vs. Business Analyst\nQuiz - Map the responsibilities\nSample problems and projects - Business Analytics vs. Data Science\nQuiz: Sample problems and Projects - Business Analysts vs. Data Scientits\nA few more things - Business Analytics vs. Data Science\nCareer in Business Analytics\nKnowing Each other\nSpectrum of Business Analytics\nTerms related to Business Analytics\nManagement Information Systems (MIS)\nDetective Analysis\nBusiness Intelligence\nPredictive Modeling\nArtificial Intelligence and Machine Learning\nWhat kind of problems do Business Analysts work on?\nSkills Required for Business Analytics and Roadmap of Business Analytics Program\nSkills Required in Business Analytics Roles\nDownload the Roadmap for Certified Business Analytics Program (CBAP)\nBonus Section - Logistics of Certified Business Analytics Program from Analytics Vidhya\nCase study: Ezine Publishing\nOverview - Case study - Ezine Publishing\nUnderstanding Business\nQuiz: Identify Focus Categories\nQuiz: Getting Granular with traffic data\nQuiz - Identify effective channel\nQuiz - Maximize Revenue\nQuiz - Target Customers\nQuiz: Traffic Distribution\nQuiz - Advertisements\nWhere to go from here?", "Course Description": "Business analytics is thriving \u2013 and so is its role in forward-thinking organizations around the world. 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Tableau Desktop (and now Tableau Public) have transformed the way we interact with visualizations and tell data stories to our clients, stakeholders, and to non-technical audiences around the world.\nTableau has been recognized as a Leader in the Gartner Magic Quadrant for Analytics and Business Intelligence Platforms for 8 straight years. Here\u2019s Gartner\u2019s most recent ranking in 2020:\nIn this Tableau for Beginners course, you will learn everything you need to get started with this wonderful visualization and business intelligence tool. You\u2019ll be able to build charts like bar charts, line charts (for working with time series data), pie charts, and even get the hang of geospatial analysis using map visualizations in Tableau!\nNote: If you\u2019re looking to build and master dashboards and storyboards in Tableau, make sure you check out the popular \u2018Mastering Tableau from Scratch: Become a Data Visualization Rockstar\u2019 course!\nCourse curriculum\nIntroduction\nWelcome to the Course\nAI&ML Blackbelt Plus Program (Sponsored)\nConcept of Visualization\nWhat is Data Visualization and Why Should we Use it\nHans Rosling - 200 Countries 200 Years 4 Minutes\nUnderstanding the Length and Breadth of Tableau\nNavigating the Tableau Interface Part 1\nNavigating the Tableau Interface Part 2\nGetting Started with Tableau\nConnect to the Data\nData Visualizations\nDifferent Types of Charts in Tableau\nNet Statistics\nNet Statistics Part 2\nLine Chart\nPie Chart\nMap Chart\nScatter Plots\nBONUS: Other Functionalities in Tableau\nFilters\nTrend Line\nWhat's Next?\nWhat's Next?\nCommon Questions Beginners Ask about Tableau\nWhy should you use Tableau?\nTableau, as we mentioned above, is the gold standard in analytics and business intelligence. It is a widely used tool in the industry, both in big firms as well as startups. Tableau helps us create effective, impactful and beautiful dashboards and stories that our clients and stakeholders love.\nThere are a ton of job vacancies for Tableau professionals in the industry so this is a great time to get started.\nTableau Desktop or Tableau Public - which one should I choose?\nTableau Desktop and Tableau Public are two of the many offerings by Tableau. Tableau Desktop is the premium version of Tableau Public. It is used by organizations worldwide for their analytics and business intelligence projects. You will be working with Tableau Desktop in the industry.\nTableau Public, on the other hand, is a free platform to learn Tableau. It was created with the purpose of making the broader audience comfortable with Tableau and how it works. Tableau Public offers almost all the features of Tableau Desktop (except you can\u2019t save your work on your local machine - it will be uploaded to Tableau\u2019s public gallery).\nYou can perform all the work provided in this course in Tableau Public.\nWhat are the different tools under the Tableau umbrella?\nTableau offers a variety of tools catering to different organizations and solutions:\nTableau Desktop\nTableau Public\nTableau Prep\nTableau Server\nTableau Online\nAnd many other tools you can check out on Tableau\u2019s official website.\nWhere can you learn about the intermediate-level of Tableau?\nExcellent question! We recommend taking the \u2018Mastering Tableau from Scratch: Become a Data Visualization Rockstar\u2019 course to deep dive into Tableau. You will learn how to quickly convert your data into actionable insights, create dashboards to impress your clients, and learn Tableau tips, tricks and best practices in your day-to-day role.\nCan I use Tableau for time series forecasting?\nYes, you can. Tableau has an in-built feature for forecasting where you can use the concept of moving average to build forecasts. But keep in mind that this is a crude feature and might not be reliable for your business. Time series is a complex topic and has advanced beyond moving average.\nHowever, Tabelau\u2019s forecast feature does give you a rough idea of what to expect and might work for small businesses or if you\u2019re looking for a quick idea of what to expect from your sales numbers, for example.\nDo you need to know programming to learn Tableau?\nNot at all. Tableau thrives as a drag-and-drop tool (for the most part). That\u2019s the beauty of Tableau to be honest, you can quickly get started and build awesome visualizations without having to get into any coding or programming.\nBut knowing simple Excel formulas, such as IF-ELSE, will help you with feature engineering in Tableau. And that is a critical part of a business intelligence analysts skillset.\nWhat kind of projects can you perform using Tableau?\nYou can take up all sorts of analytics projects next. We suggest heading over to the DataHack platform and picking up any project that catches your interest. Load the dataset into Tableau and get going!", "Course Description": "Tableau is the tool of choice for business intelligence, analytics and data visualization experts. 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This course covers a wide range of Excel formulas, including LookUp Functions!\nYour Pathway to Mastering Microsoft Excel Formulas and Functions\nMicrosoft Excel is the gold standard in data analysis tools. There\u2019s no question about it - industry experts, professionals and evetrans still lean heavily on Excel\u2019s prowess and Swiss Army Knife nature to slice and dice their data.\nWhether you\u2019re looking to perform quick data analysis or an in-depth dissection of your project, Microsoft Excel is the first tool you\u2019ll look at. The incredible array of formulas and functions Excel offers is unparalleled in the industry.\nYou\u2019ll find people at all levels in organizations pouring over Excel spreadsheets, from data analysts and business analysts to C-suite executives and data scientists.\nThe one thing that has separated Excel from all other tools is the incredibly in-depth nature of the formulas and functions it offers. From arithmetic to logical functions, from Date and Time to Text functions, and of course, the marvellous LookUp functions (VLookUp and HLookUp) - you\u2019ll need to have these handy when you\u2019re working on real-world analytics projects.\nWho is the Microsoft Excel: Formulas and Functions Course for?\nThis course is for anyone who:\nWants to learn Microsoft Excel\nWants to brush up their Excel skills, from beginner to intermediate\nIs looking to master Excel formulas and functions from basic to advanced\nWants to get started with data analysis\nIs looking to start their business analytics or data science journey\nCourse curriculum\nIntroduction\nWelcome to the Course!\nInstructor Introduction\nAI&ML Blackbelt Plus Program (Sponsored)\nExcel Formula and Referencing\nGetting Started with Excel Formulas\nReferencing\nExercise\nPaste Special & Format Painter\nExercise\nDateTime and Text Functions\nDateTime Function\nExercise\nLeft,Right,Mid,Len,Concatenate\nFind & Search\nExercise\nReplace & Substitute\nText and Value\nMathematical Functions\nSum, Product, Mod, Sqrt, Fact\nRound, RoundUp, Rounddown, SumIFS\nExercise\nFinancial Function\nPresent Value and Future Value\nNPV and IRR\nFinancial Functions in Excel\nExercise\nLookUp Function\nVlookup and Hlookup\nMatch\nIndex\nExercise\nLogical and Error Function\nAND, OR, NOT, ISERROR, ISNUMBER, ISBLANK, ISTEST\nIF, IFERROR\nExercise\nStatistical Function\nCount, Counta, Countblank, Countifs\nMean, Median, Mode, Std, Rank, Quartiles, Corr\nExercise\nWhat's Next?\nCBAP: Certified Business Analytics Program", "Course Description": "Microsoft Excel is still the tool of choice in the industry when it comes to performing data analysis, thanks to its incredible depth and array of formulas and functions. 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Learn AI-powered text and image generation, use top AI tools, and explore industry applications. Gain practical skills, understand ethical practices, and master prompting techniques.This course is a transformative journey tailored for beginners and delves into AI-powered text and image generation using leading tools like ChatGPT, Microsoft Copilot, and DALL\u00b7E3. Learn practical applications across industries, ethical considerations, and best practices. Whether you're a content creator, business innovator, or AI enthusiast, gain the expertise to harness Generative AI's full potential and drive innovation in your field.Course curriculum Introduction to Generative AI Fundamentals of Generative AI What is Generative AI? How does Generative AI work? Exploring the Potential of Generative AI GenAI Pinnacle Program Hands On: Let\u2019s get generating! Text Generation Using Generative AI An Overview of Text Generation What is ChatGPT? Working with ChatGPT Working with ChatGPT Plus Working with Bing Chat Breaking Bard Sponsored Ad Learning the Art of Prompting Creating a Chatbot Ethics and Best Practices Image Generation Using Generative AI Introduction to Image Generation Exploring the Potential of Image Generation Working with free image generation tools Working with Clipdrop Working with Bing Image Creator Working with Firefly Working with Paid Image Generative Tools Working with paid image generative tools DreamStudio Working with DALLE-2 Working with Midjourney Sponsored Ad Prompting your Way to Art Accomplishing Tasks with Image Generation E for Ethics and Efficiency Who Should Enroll: This course is perfect for beginners with no technical background, professionals looking to enhance their AI skills, students eager to explore AI, and content creators seeking to leverage AI tools. If you're curious about AI's potential and want to stay ahead in your field, this course is for you.", "Course Description": "This course is a transformative journey tailored for beginners and delves into AI-powered text and image generation using leading tools like ChatGPT, Microsoft Copilot, and DALL\u00b7E3. Learn practical applications across industries, ethical considerations, and best practices. 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By the end of the course, you will be proficient in implementing and fine-tuning these techniques to enhance generative AI model performance. You'll learn to apply various prompting methods and build chatbots on enterprise data, equipping you with the skills to improve conversational AI systems in real-world projects.\nWho Should Enroll:\nProfessionals: Individuals looking to deepen their knowledge and apply advanced LLM and prompt engineering techniques to solve complex problems across various domains.\nAspiring Students: Individuals looking to deepen their knowledge and apply advanced LLM and prompt engineering techniques to solve complex problems across various domains.\nCourse curriculum\nHow to build diffferent LLM AppIications?\nIntroduction to Building Different LLM applications\nPrompt Engineering\nRetrieval Augmented Generation\nFinetuning LLMs\nTraining LLMs from Scratch\nQuiz\nGetting Started with Prompt Engineering\nIntroduction to Prompt Engineering\nSet up your machine for Prompt Engineering\nPrompt Engineering with ChatGPT API\nEnabling Conversation with ChatGPT API\nQuiz\nUnderstanding Different Prompt Engineering Techniques\nIntroduction to Understanding Different Prompt Engineering Techniques\nFew Shot Prompting\nOne Shot Prompting\nZero Shot Prompting\nQuiz\nAssignment\nKey Takeaways from the Course\nLearn to effectively build and implement different LLM applications\nUnderstand and apply few-shot, one-shot, and zero-shot prompting\nGain expertise in building chatbots using enterprise data through advanced prompt engineering methods", "Course Description": "This course will provide you with a hands-on understanding of building LLM applications and mastering prompt engineering techniques. By the end of the course, you will be proficient in implementing and fine-tuning these techniques to enhance generative AI model performance. 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You will learn the various components of Midjourney and how to use it to bring your imaginations to real world.\nThis course will provide you with a practical understanding of MidJourney tools. By the end of the course, you will be able to utilize MidJourney effectively and explore alternative tools for your creative projects. You'll learn how to draw inspiration, use MidJourney's features, and understand its applications through engaging lessons.\nWho Should Enroll:\nCreative Professionals: Individuals looking to enhance their creativity and apply MidJourney tools to various artistic and visual projects.\nAspiring Creatives: Those beginning their journey into visual storytelling and digital art, seeking to learn the fundamentals of MidJourney and its alternatives.\nCourse curriculum\nMidJourney\nMidJourney - Storm _ Story\nMidJourney - Inspiration\nMidJourney - How to use\nMidJourney Alternatives\nQuiz\nKey Takeaways from the course\nUnderstand MidJourney Tools: Gain a practical understanding of how to use MidJourney for creative projects.\nFind Inspiration: Learn how to draw inspiration from various sources to fuel your creativity.\nExplore Alternatives: Discover alternative tools to broaden your creative toolkit.", "Course Description": "This course will provide you with a practical understanding of MidJourney tools. By the end of the course, you will be able to utilize MidJourney effectively and explore alternative tools for your creative projects. You'll learn how to draw inspiration, use MidJourney's features, and understand its applications through engaging lessons.", "embedding": [0.015985121950507164, -0.04339538887143135, -0.028600312769412994, -0.04191799834370613, -0.0402655228972435, 0.020105086266994476, 0.014234782196581364, -0.03043478913605213, -0.04988498613238335, -0.010152588598430157, 0.056782398372888565, 0.006747785955667496, -0.04486331343650818, 0.07137463986873627, -0.03296767547726631, -0.10654336959123611, -0.05623523145914078, 0.023587923496961594, -0.04621458798646927, -0.023857101798057556, -0.01686909608542919, 0.012307577766478062, -0.03488484397530556, 0.040592171251773834, -0.04571220651268959, -0.06515834480524063, -0.03305348753929138, 0.019776852801442146, 0.041211605072021484, -0.03440472111105919, -0.03975725173950195, 0.008318619802594185, 0.009020539000630379, 0.013286889530718327, 2.443507355565089e-06, -0.043273117393255234, 0.004199695307761431, -0.05155700445175171, 0.02531289868056774, 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You will start with data ingestion by loading a file into the system, followed by indexing the data for efficient retrieval. Next, you will set up retrieval configurations and use a response synthesizer to combine data into a coherent response. Finally, you will employ a query engine to generate responses. By the end of this course, you will have a solid understanding of these processes and be able to build an RAG system using LlamaIndex code effectively.\nCourse curriculum\nIntroduction to RAG systems\nWelcome to this course\nWhy RAG\nWhat is RAG system\nOverview of RAG Framework\nQuiz\nCourse handouts\nGetting Started with LlamaIndex\nIntroduction to LlamaIndex\nComponents of LlamaIndex\nReading Material: How to get your API Key\nHow to get Open AI Keys - 2 min - Website go through\nBuild Your First RAG system using LlamaIndex\nQuiz\nKey Takeaways from the course\nLearn the steps involved in building a RAG system using Llamaindex.\nHands-On Experience: Engage with exercises designed to reinforce your learning and apply concepts in real-world scenarios.", "Course Description": "This course will guide you\u00a0through building your first Retrieval-Augmented Generation (RAG) system\u00a0using LlamaIndex.\u00a0You will start with data ingestion by loading a file into the system, followed by indexing the data for efficient retrieval. Next, you will set up retrieval configurations and use a response synthesizer to combine data into a coherent response. Finally, you will employ a query engine to generate responses. By the end of this course, you will have a solid understanding of these processes and be able to build an RAG system using LlamaIndex code effectively.", "embedding": [0.03285502269864082, 0.0280617605894804, -0.017828192561864853, -0.005268825218081474, 0.011238384060561657, -0.008703845553100109, -0.02198418602347374, 0.009284986183047295, 0.01893414556980133, -0.03275301307439804, -0.019585544243454933, -0.0028942071367055178, -0.03807516396045685, 0.03523194417357445, 0.0048217615112662315, -0.033717092126607895, 0.04735049605369568, 0.026429664343595505, -0.04680031165480614, -0.012117166072130203, -0.007196558639407158, -0.008000552654266357, 0.011388970538973808, 0.022167790681123734, -0.03194647654891014, 0.005095531232655048, -0.05307993292808533, 0.04856179654598236, -0.017445934936404228, -0.05842115357518196, -0.002231243997812271, 0.01615428924560547, 0.048094868659973145, 0.03856121003627777, 1.9794204035861185e-06, -0.07069611549377441, -0.02037419192492962, 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Learn to deploy SD WebUI, use Automatic WebUI on RunPod GPU environments, and master installation, setup, generation, and customization of SD.\nThis course will give you a practical understanding of Stability.AI tools. By the end of the course, you will be able to deploy and customize SD WebUI, and use the Automatic1111 WebUI on RunPod GPU environments. You'll learn to install, set up, generate, and fine-tune SD WebUI settings, equipping you with the skills to harness Stability.AI's full potential for your projects.\nWho Should Enroll:\nProfessionals: Individuals aiming to enhance their skill set and apply Stability.AI tools/Stable Diffusion in various fields.\nAspiring Students: Those beginning their journey to mastering Generative AI tool deployment and customization, looking to make an impact in the evolving world of Generative AI\nCourse curriculum\nMastering stability.ai and its tools\nIntroduction to Stability\nHow to use Stability.AI tools\nReview of Deployment Options for SD WebUI\nAutomatic1111 WebUI on RunPod GPU environment\nSD WebUI Hands-On - Installation and Setup\nSD WebUI Hands-On - Generation and Settings\nQuiz\nKey Takeaways from the course\nLearn to effectively use and customize SD WebUI and the Automatic1111 WebUI.\nHands-On Experience: Engage in practical exercises to reinforce learning and apply concepts, ensuring you gain the skills to utilize Stability.AI tools confidently", "Course Description": "This course will give you a practical understanding of Stability.AI tools. By the end of the course, you will be able to deploy and customize SD WebUI, and use the Automatic1111 WebUI on RunPod GPU environments. 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This course offers an optimal pathway to delve into the intricacies of natural language processing and model training.\nThis course will help you gain a comprehensive understanding of Large Language Models (LLMs) and develop advanced natural language processing (NLP) applications using the PyTorch framework. With a carefully curated list of resources and exercises, this course is your guide to becoming an expert in LLMs. Master the techniques to build and fine-tune LLMs, and generate human-like text.\nWho Should Enroll: Professionals: Individuals looking to expand their skill set and leverage LLMs across different industries. Aspiring Students: For those setting out on their journey to master language data analysis and leave a mark in the tech world.\nCourse curriculum\nIntroduction\nCourse Objective\nCourse Handouts\nThe Exponential Growth\nThe Evolution of NLP\nThe Evolution of NLP: Symbolic NLP\nThe Evolution of NLP: Statistical NLP\nThe Evolution of NLP: Deep Learning\nThe Evolution of NLP: Deep Learning Era II\nThe Evolution of NLP: Tranformers and Evolution\nQuiz\nWhat are Large Language Models?\nIntroduction to Large Language Model\nWhat is a Large Language Model?\nUnderstanding Foundational Models\nDifferent types of LLMs: Based on Response\nDifferent types of LLMs: Based on Model Architecture\nQuiz\nThe Current State of the Art in LLMs\nThe Current State of the Art in LLMs\nGenerative AI - Glossary\nGenerative AI- Glossary\nYour Feedback Matters!\nYour Feedback Matters!\nKey Takeaways from the course\nLearn Large Language Model techniques and build real-world NLP applications.\nHands-On Experience: Engage with exercises designed to reinforce your learning and apply concepts in real-world scenarios.", "Course Description": "This course will help you gain a comprehensive understanding of Large Language Models (LLMs) and develop advanced natural language processing (NLP) applications using the PyTorch framework. 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Classification is a skill every Data Scientist should be well versed in.\nIn this course, we are solving a real life case study of Dream Housing Finance. The company deals in all home loans. They have a presence across all urban, semi-urban and rural areas. Customers first apply for a home loan after that company validates the customer's eligibility. The company wants to automate the loan eligibility process (real-time) based on customer detail provided while filling online application form.\nBy the end of the course, you will have a solid understanding of Classification problem and Various approaches to solve the probem\nPre-requisites\nThis course assumes that you have familiarity with Python.\nCourse curriculum\nLoan Prediction : Practice Problem\nIntroduction to the Course\nTable of Contents\nProblem Statement\nHypothesis Generation\nExercise 2 | Discussion\nGetting the system ready and loading the data\nUnderstanding the Data\nUnivariate Analysis\nBivariate Analysis\nMissing Value and Outlier Treatment\nEvaluation Metrics for Classification Problems\nModel Building : Part I\nLogistic Regression using stratified k-folds cross validation\nFeature Engineering\nModel Building : Part II\nAI&ML Blackbelt Plus Program (Sponsored)\nFAQs\nThis course is meant for people looking to explore Classification Problems in Python.\nYou should have Python and Jupyter Notebook installed on your system.\nThe course is free of charge.\nWe would highly recommend taking the course in the order in which it has been designed to gain the maximum knowledge from it.", "Course Description": "In this course, we are solving a real life case study of Dream Housing Finance. 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If done correctly, it can have a significant impact on the success and performance of that company.\nIn this course you will be working on the Big Mart Sales Prediction Challenge.\nThe course will equip you with the skills and techniques required to solve regression problems in R. You will be provided with sufficient theory and practice material to hone your predictive modeling skills.\nPre-requisites\nThis course assumes that you have familiarity with R.\nCourse curriculum\nBig Mart Sales\nOverview of the Course\nTable of contents\nProblem Statement\nHypothesis Generation\nLoading Packages and Data\nUnderstanding the Data\nUnivariate Analysis\nBivariate Analysis\nMissing Value Treatment\nFeature Engineering\nEncoding Categorical Variables\nPreProcessing Data\nModel Building\nLinear Regression\nRegularized Linear Regression\nRandom Forest\nXGBoost\nSummary\nAI&ML Blackbelt Plus Program (Sponsored)\nEnroll for free\nFAQs\nThis course is meant for people looking to learn solving regression problems using R.\nYou will need to download and install R and RStudio", "Course Description": "In this course you will be working on the Big Mart Sales Prediction Challenge.The course will equip you with the skills and techniques required to solve regression problems in R. 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Why is sentiment analysis so popular in data science? And how can you perform sentiment analysis? Find the answers to all these questions in this free course on Sentiment Analysis using Python!What is Sentiment Analysis?\nSentiment Analysis or Opinion Mining is a technique used to analyse the emotion in a text. We can extract the attitude or the opinion of a piece of text and get insights on it. \nIn the context of machine learning, you can think of Sentiment Analysis as a Classification problem where the text can either have a positive sentiment, a negative sentiment or a neutral one.\nWhat are the applications of Sentiment Analysis in the industry?\nIn the age of social media, it is extremely common to comment about \na movie you liked or \na book you didn\u2019t like or \na product you bought was not up to the mark.\nTherefore, a lot of companies use sentiment analysis for their products since it provides direct feedback of the customer\u2019s opinion.\nIt is also important to detect and remove hateful content from social media and companies like Twitter, Facebook, etc. extensively use sentiment analysis on a daily basis.\nOn what kind of projects would I implement sentiment analysis?\nThere are a wide variety of projects where you can use Sentiment Analysis. Here are a couple of popular use cases:\nSentiment Analysis can not only be used for customer reviews or product feedback, but in other domains as well.\nAnalyzing the sentiments on social media on the US Elections, for example, gives useful insights on which candidates are favoured by the public and which are not.\nFor other interesting problems involving sentiment/emotion detection, you can visit: https://datahack.analyticsvidhya.com/contest/all/\nWhat is the range of sentiments that can be observed and analysed?\nIn the earlier days of Natural language processing and Sentiment Analysis, the sentiments could hold only 2 or 3 values: Positive or Negative, and Positive, Neutral or Negative.\nHowever, with the advent of deep learning, we can now recognize the subtle emotions in a text as well.\nThis has made tasks like Sarcasm detection, fake news detection etc. popular in research areas of Natural language processing \nCan I add this project to my resume and use it in my Interview?\nSentiment Analysis is one of the most popular applications of Machine Learning and Classification in Natural language processing\nWe also encourage you to take up more diverse datasets and apply sentiment analysis on them.\nSentiment Analysis is also one of the first projects you would learn in your Natural language processing journey and as such is commonly asked in interviews.\nCourse curriculum\nTwitter Sentiment Analysis (Using Python)\nOverview of the Course\nUnderstand the Problem Statement\nTable of Contents\nLoading Libraries and Data\nData Inspection\nData Cleaning\nStory Generation and Visualization from Tweets\nBag-of-Words Features\nTF-IDF Features\nWord2Vec Features\nModeling\nLogistic Regression\nSupport Vector Machine (SVM)\nRandomForest\nXGBoost\nFineTuning XGBoost + Word2Vec\nSummary\nAI&ML Blackbelt Plus Program (Sponsored)", "Course Description": "What is sentiment analysis? Why is sentiment analysis so popular in data science? And how can you perform sentiment analysis? 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Why is web scraping a must-know skill? How can you perform web scraping in Python? This course will cover all these aspects of web scraping and showcase how to perform web scraping using BeautifulSoup and Scrapy.\nBecome Familiar with Web Scraping using Python\nThe need and importance of extracting data from the web is becoming increasingly loud and clear. There is an unprecedented volume of data on the internet right now - and data science projects often need this data to build predictive models.\nThat\u2019s a key reason why data scientists are expected to be familiar with web scraping.\nWe have found web scraping to be a very helpful technique for gathering data from multiple websites. Some websites these days also provide APIs for many different types of data you might want to use, such as Tweets or LinkedIn posts.\nBut there might be occasions when you need to collect data from a website that does not provide a specific API. This is where having the ability to perform web scraping comes in handy. As a data scientist, you can code a simple Python script and extract the data you\u2019re looking for.\nSo knowing how to perform web scraping using Python will help you go a long way towards becoming a resourceful data scientist. Are you ready to take the next step and dive in?\nA note of caution here \u2013 web scraping is subject to a lot of guidelines and rules. Not every website allows the user to scrape content so there are certain legal restrictions at play. Always ensure you read the website\u2019s terms and conditions on web scraping before you attempt to do it.\nIn this course, we will dive into the basics of web scraping using Python. We will understand what web scraping is, the different Python libraries for performing web scraping, and finally we\u2019ll implement web scraping using Python in a real-world project. There\u2019s a lot to unpack here so enroll today and start learning!\nQuestions Beginners have about Web Scraping using Python\nWe\u2019re sure you\u2019ve asked these questions before. Even if you haven\u2019t, you should start learning how these web scraping questions should be answered:\nWhat is web scraping?\nWhy should you learn web scraping?\nWhy Python for web scraping?\nWhat are the different Python libraries for performing web scraping?\nCan I use R for web scraping?\nWhat kind of projects can I take up after learning web scraping?\nAre web scraping concepts asked in data science/machine learning interviews?\nYou\u2019ll learn about these concepts inside the course and we have even provided a high-level overview of these questions after the course curriculum below.\nWho is the Introduction to Web Scraping using Python Course for?\nThis course is for anyone who:\nWants to learn the art of web scraping using Python\nIs looking to collect or gather more data for their data science or machine learning project\nWants to add a new and crucial skill to their existing data science portfolio\nIs curious about Python programming\nWhat do you need to get started with the Introduction to Web Scraping using Python course?\nHere\u2019s what you\u2019ll need:\nA working laptop/desktop with 4 GB RAM\nA working Internet connection\nBasic knowledge of Python. You can take this free Python course if you need a refresher\nThat\u2019s it! You\u2019re all set to perform web scraping on your machine!\nCall to action\nThis is where you seal the deal. Sprinkle this section throughout your page to push prospects to purchase!\nGet started now\nCourse curriculum\nIntroduction to Web Scraping\nWhat is Web Scraping?\nCaution\nPopular Libraries for Web Scraping\nComponents of Web Scraping\nAI&ML Blackbelt Plus Program (Sponsored)\nWeb Scraping: Procedure\nProblem Setup\nStep 1: Crawl\nStep 2: Parse and Transform\nStep 3: Store the Data\nScraping URLs and Email IDs from a Web Page\nSingle Webpage Scraping\nMultiple Webpage Scraping(BeautifulSoup and Regex)\nScrape Images in Python\nScrape Images in Python\nScrape Data on Page Load\nScarpe Data on Page Load\nCommon Questions Beginners Ask about Web Scraping\nHere, we break down the common questions beginners often have on web scraping\nWhat is web scraping?\nWeb scraping is a computer software technique of extracting information from websites. This technique mostly focuses on the transformation of unstructured data (HTML format) on the web into structured data (database or spreadsheet).\nYou can perform web scraping in various ways, including use of Google Docs to almost every programming language.\nWhy should you learn web scraping?\nWeb scraping is incredibly useful when you don\u2019t have enough data with you to train a machine learning model. Web scraping helps us to collect this data from websites (if permitted) and we can then use that to train our model. You can imagine why web scraping is such a prized tool in a data scientist\u2019s arsenal!\nWhy Python for web scraping?\nPython is the most popular tool out there in the world for Web Scraping. Its 2 prominent libraries - BeautifulSoup and Scrapy makes web scraping easy and efficient. Python\u2019s syntax makes understanding of the codes easy. Also python provides many other libraries for web scraping which can be used as per our needs. Eg- lxml, requests etc\nWhat are the different Python libraries for performing web scraping?\nThere are many libraries in Python that help us to scrape the web. The 3 most prominent libraries include:\nBeautifulSoup\nScrapy\nSelenium\nCan I use R for web scraping?\nYou sure can! You can perform web scraping in both Python and R. We are teaching you how to do this using Python in the course but feel free to use R if that\u2019s your language of choice. You can go through this tutorial that walks you through how to master web scraping using an R package called rvest.\nAre web scraping concepts asked in data science/machine learning interviews?\nThis depends a lot on the data science role and the organization you\u2019re interviewing for. Not all organizations require you to know or apply web scraping. But here\u2019s why you should learn it anyway - it will help you expand your skillset and also help you work on your personal projects for data science. There\u2019s a lot to learn and nothing to lose!", "Course Description": "What is web scraping? Why is web scraping a must-know skill? How can you perform web scraping in Python? 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It\u2019s difficult to keep up with the pace of time.  But, technology has developed some powerful methods using which we can \u2018see things\u2019 ahead of time!\nNope, not the time machine, we are talking about the methods of prediction & forecasting. As the name \u2018time series forecasting\u2019 suggests, it involves working on time (years, days, hours, minutes) based data, to derive hidden insights to make informed decision making.\nWhat will you learn from Time Series Forecasting using Python Course?\nThis course is designed for people who want to solve problems related to Time Series Forecasting. By the end of the course, you will learn to apply the following necessary skills and techniques required to solve Time Series problems:\nARIMA Model\nTuning Parameters for ARIMA\nHandling Seasonality using ARIMA\nMoving Average\nMachine Learning for Time Series forecasting\nExponential Smoothing Methods\nFramework to evaluate Time Series Models\nCourse curriculum\nIntroduction to Time Series\nIntroduction to the Course\nIntroduction to Time Series\nComponents of a Time Series\nAI&ML Blackbelt Plus Program (Sponsored)\nUnderstanding Problem Statements and Data Sets\nProblem Statement\nTable of Contents\nHypothesis Generation\nGetting the system ready and loading data\nDataset Structure and Content\nExploration and Preprocessing\nFeature Extraction\nExploratory Analysis\nExercise 1\nModelling Techniques and Evaluation\nSplitting the data into training and validation part\nModeling Techniques\nHolt's Linear trend model on daily time series\nHolt Winter's model on daily time series\nIntroduction to ARIMA model\nParameter tuning for ARIMA model\nSARIMAX model on daily time series\nExercise 2\nImportant Links\nYour Feedback", "Course Description": "This course is designed for people who want to solve problems related to Time Series Forecasting. 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From functions to industries, AI and ML are disrupting how we work and how we function. Get to know all about the different facets of AI and ML in this course.\nWelcome to the World of Artificial Intelligence and Machine Learning!\nThe AI revolution is here - are you prepared to integrate it into your skillset? How can you leverage it in your current role? What are the different facets of AI and ML?\nAnalytics Vidhya\u2019s \u2018Introduction to AI and ML\u2019 course, curated and delivered by experienced instructors with decades of industry experience between them, will help you understand the answers to these pressing questions.\nArtificial Intelligence and Machine Learning have become the centerpiece of strategic decision making for organizations. They are disrupting the way industries and roles function - from sales and marketing to finance and HR, companies are betting big on AI and ML to give them a competitive edge.\nAnd this, of course, directly translates to their hiring. Thousands of vacancies are open as organizations scour the world for AI and ML talent. There hasn\u2019t been a better time to get into this field!\nWhat do I need to start with Introduction to AI & ML course?\nA working Internet connection\nCuriosity about Artificial Intelligence and Machine Learning\nThis is all it takes for you to start your journey in Artificial Intelligence and Machine Learning\nWhat are you waiting for?\nWhat do I need to start with Introduction to AI & ML course?\nCourse curriculum\nIntroduction to AI & ML\nWhat is AI&ML?\nTypes of ML\nWhen to Apply AI&ML\nRecent AI Uprising\nHow the world is Changing?\nBuilding Blocks of AI and ML\nKnowing Each Other\nAI&ML Blackbelt Plus Program (Sponsored)\nCommon Terminologies, Tools and Techniques\nCommon Terminologies\nCommon Data Capturing Types and Tools\nCommon Tools\nCommon Techniques\nCommon Techniques - Part1\nCommon Techniques - Part2\nSkills required to become a data science professional\nSkills Required in Data Science\nAI and ML Black Belt+\nWhere to Go from here!!\nKey Takeaways of the Introduction to AI & ML Course\nYou will learn the current state of AI and ML, how they are disrupting businesses globally\nSolid understanding of what AI and ML mean, what they represent in the current market and industry, how they work, and why you should learn about them.", "Course Description": "You will learn the current state of AI and ML, how they are disrupting businesses globally.Solid understanding of what AI and ML mean, what they represent in the current market and industry, how they work, and why you should learn about them.", "embedding": [0.033010873943567276, -0.0037443675100803375, -0.06265446543693542, -0.03193158656358719, -0.03072107397019863, 0.049835141748189926, 0.02028553932905197, -0.010320648550987244, 0.04109951853752136, -0.03465070575475693, 0.052309855818748474, 0.014111394993960857, -0.011258536949753761, 0.10770227015018463, 0.047897785902023315, -0.06871812790632248, 0.03490553796291351, 0.001865567173808813, -0.010826120153069496, 0.01707809790968895, 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Leverage your Python skills to start your Data Science journey. This course is intended for beginners with no coding or Data Science background.\nDo you want to enter the field of Data Science? Are you intimidated by the coding you would need to learn? Are you looking to learn Python to switch to a data science career?\nYou have come to just the right place!\nMost industry experts recommend starting your Data Science journey with Python\nAcross biggest companies and startups, Python is the most used language for Data Science and Machine Learning Projects\nStackoverflow survey for 2019 had Python outrank Java in the list of most loved languages\nPython is a very versatile language since it has a wide array of functionalities already available. The sheer range of functionalities might sound too exhaustive and complicated, you don\u2019t need to be well-versed with them all.\nMost data scientists have a few go-to libraries for their daily tasks like:\nfor performing data cleaning and analysis - pandas\nfor basic statistical tools \u2013 numpy, scipy\nfor data visualization \u2013 matplotlib, seaborn\nWhy Python and how popular is it for Data Science?\nPython has rapidly become the go-to language in the data science space and is among the first things recruiters search for in a data scientist's skill set.\nIt consistently ranks top in global data science surveys and its widespread popularity will only keep on increasing in the coming years.\nOver the years, with strong community support, this language has obtained a dedicated library for data analysis and predictive modelling.\nThis python data science course will help you learn Python libraries like Pandas and use them efficiently for data science and data analysis. Are you ready to power up your career and learn the best data science language?\nWhat does a Data Scientist do?\nData Science is an amalgamation of Statistics, Computer Science and specific domain knowledge.\nAs more and more data gets generated across the world, we need to leverage it to make decisions and improve them.\nA data scientist performs operations on the data provided to analyze and interpret it.\nWhich companies use Python?\nMany of the biggest and most popular companies use Python. Some of them are:\nGoogle, NASA, Amazon\nSocial networking sites like Instagram, Reddit, Quora, etc\nMedia streaming companies like Netflix and Spotify\nRideshare companies like Uber and Lyft\n\u201cPython has been an important part of Google since the beginning and remains so as the system grows and evolves. Today dozens of Google engineers use Python, and we are looking for more people with skills in this language.\u201d\n - Peter Norvig, Director of Research at Google Inc.\nSo get onboard the Data Science train by learning Python and upskill yourself with one of the Top Data Science Courses offered by Analytics Vidhya!\nCommon Questions Beginner ask about Python for Data Science Courses?\nDo I need to learn coding to learn Python?\nIf you are totally new to programming, no need to get intimidated by learning a whole new language.\nPython is a very easy language to learn:\nIt does not have a complicated syntax and understanding Python is very intuitive.\nYou don\u2019t need to be skilled in coding for getting started in Python.\nThis course is for beginners we will start right from the foundations to performing data analysis tasks in Python.\nI am familiar with other Programming Languages like Java/C++. Will this course help me to migrate to Python?\nDo you know that Python is essentially a wrapper on C? That is what makes it fast and easy to understand!\nThough Python has recently become popular amongst Data Scientists, it was originally a general-purpose language.\nPython is still object-oriented and follows many of the paradigms that Java does.\nSo if you are familiar with the concepts of programming, you can migrate to Python easily with this course.\nHow much Python do I need to know to enter Data Science?\nThough Python has hundreds of libraries and many more functionalities, you don\u2019t need to know all of them for learning Data Science\nRather than becoming an expert in the entire language, you would need to just be acquainted with the basic syntax of Python.\nWe will also cover the most popular libraries used by Data Scientists and which you would be using too as a future Data Scientist!\nWhat if I don\u2019t have Python installed on my system?\nOne of the best things about Python is the wide variety of platform that support writing it.\nWe will provide easy to follow instructions to work with Python using Anaconda, an extremely popular package manager platform.  No matter what Operating System you are using, we have you covered with guides for all of them\nWhat are the most popular open-source libraries that Python supports?\npandas, numpy, scipy, matlplotlib, seaborn are used for Data Science and Data Analysis\nscikit-learn, tensorflow, keras are used for basic and advanced machine learning\nlibraries for deep learning like OpenCV(Computer Vision), NLTK(Natural Language Processing)\nWill I be able to apply what I have learnt here to machine learning and data science projects?\nThe Python for Data Science course is designed to help you completely understand Python and start using it immediately fr Data Science projects.\nWith regular assignments, quizzes and hands-on projects, you will be full equipped with the essential data science skillsets.", "Course Description": "This python data science course will help you learn Python libraries like Pandas and use them efficiently for data science and data analysis. 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Machine Learning is reshaping and revolutionizing the world and disrupting industries and job functions globally. \nMachine learning is so extensive that you probably use it numerous times a day without knowing it. From unlocking your mobile phones using your face to giving your attendance using a biometric machine, machine learning is being used in almost every stage. \nIn this age of machine learning, every aspiring data scientist is expected to upskill themselves in machine learning techniques & tools and apply them to real-world business problems.\nWhat will I learn from this course?\nPython libraries like Numpy, Pandas, etc. to analyze your data efficiently.\nImportance of Statistics and Exploratory Data Analysis (EDA) in the data science field.\nLinear Regression, Logistic Regression, and Decision Trees for building machine learning models.\nUnderstand how to solve Classification and Regression problems using machine learning\nHow to evaluate your machine learning models using the right evaluation metrics?\nImprove and enhance your machine learning model\u2019s accuracy through feature engineering\nProjects covered in this course\n1. Customer Churn Prediction\nA Bank wants to take care of customer retention for their product: savings accounts. The bank wants you to identify customers likely to churn balances below the minimum balance in the next quarter. You have the customers information such as age, gender, demographics along with their transactions with the bank. Your task as a data scientist would be to predict the propensity to churn for each customer.\nProjects covered in this course\n2. NYC Taxi Trip Duration Prediction\nUber, Lyft, Ola and many more online ride hailing services are trying hard to use their extensive data to create data products such as pricing engines, driver allotment etc. To improve the efficiency of taxi dispatching systems for such services, it is important to be able to predict how long a driver will have his taxi occupied or in other words the trip duration. This project will cover techniques to extract important features and accurately predict trip duration for taxi trips in New York using data from TLC commission New York.", "Course Description": "In this free machine learning certification course, you will learn Python, the basics of machine learning, how to build machine learning models, and feature engineering techniques to improve the performance of your machine learning models.", "embedding": [-0.0015167508972808719, 0.04977001994848251, -0.0796661525964737, -0.03892146423459053, 0.006811038590967655, 0.039726242423057556, 0.06352942436933517, 0.003065627533942461, -0.0012820750707760453, 0.002530435100197792, 0.0656222328543663, 0.03108465112745762, 0.02152343839406967, 0.09734120965003967, 0.03830043226480484, -0.08287319540977478, 0.003912947606295347, -0.011837058700621128, -0.004387141205370426, 0.0018576381262391806, -0.01766081526875496, -0.0025171474553644657, -0.019638163968920708, 0.05264247581362724, -0.03483756631612778, 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This course will serve as a learning path to help beginners navigate through the complex terrain of Deep Learning.Course curriculum\nUnderstanding the working of Neural Networks\nHow are Neural Networks trained - Forward Propagation\nUnderstanding Loss Functions + Hands on\nReading: Creating a Custom Loss Function (Optional)\nOptimization Techniques - Gradient Descent\nWhat is Back Propagation?\nTypes of Gradient Descent\nCommon Optimization Techniques - Part 1\nCommon Optimization Techniques - Part 2\nBuilding a Deep Neural Network (Hands-on Regression Model)\nBuilding a Deep Neural Network (Hands-on Classification Model)\nQuiz\nThe Working of Neural Networks\nThis free course will help you understand the end-to-end working of neural networks in a simple manner. By the end of this course, you will be able to build advanced Deep Learning models using the PyTorch framework. With a carefully curated list of resources and exercises, this course serves as your comprehensive guide to mastering deep learning. It is recommended that you complete the advanced Machine Learning course before taking up this course.\nWho Should Enroll:\nProfessionals: Individuals looking to expand their skill set and apply deep learning across different industries.\nAspiring Students: For those setting out on their journey to mastering deep learning and making a mark in the tech world.\nKey Takeaways from the course\nUtilize leading AI tools like ChatGPT, Microsoft Copilot, and DALL\u00b7E3 to create text and image content, enhancing your ability to innovate and streamline your creative processes.\nHands-On Experience: Engage with exercises designed to reinforce your learning and apply concepts in real-world scenarios.", "Course Description": "This free course will help you understand the end-to-end working of neural networks in a simple manner. By the end of this course, you will be able to build advanced Deep Learning models using the PyTorch framework. With a carefully curated list of resources and exercises, this course serves as your comprehensive guide to mastering deep learning. 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In this free course let us understand Linear Regression using a Business Case Study\nCourse curriculum\nLinear Regression\nIntroduction to the Problem Statement\nResources for this Course\nIntroduction to Linear Regression\nSignificance of Slope and Intercept in the linear regression\nHow Model Decides The Best-Fit Line\nLet\u2019s Build a Simple Linear Regression Model\nModel Understanding Using Descriptive Approach\nModel Understanding Using Descriptive Approach - II\nModel Building Using Predictive Approach\nQuiz: Linear regression\nUnderstanding Learning Regression\nThis free course will help you understand the fundamentals of linear regression in a straightforward manner. By the end of this course, you will be able to build predictive models using linear regression techniques. With a carefully curated list of resources and exercises, this course serves as your comprehensive guide to mastering linear regression. \nWho Should Enroll:\nProfessionals: Individuals looking to expand their skill set and apply machine learning across different industries.\nAspiring Students: For those setting out on their journey to mastering machine learning and making a mark in the tech world.\nKey Takeaways from the course\nLearn Linear Regression technique and build real-world Machine Learning Models.\nHands-On Experience: Engage with exercises designed to reinforce your learning and apply concepts in real-world scenarios.", "Course Description": "This free course will help you understand the fundamentals of linear regression in a straightforward manner. By the end of this course, you will be able to build predictive models using linear regression techniques. 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Learn text classification, NLP models with PyTorch, and real-world applications. Perfect for beginners and professionals alike, dive into AI-driven text analysis and hands-on projects.\nGain practical insights into Natural Language Processing (NLP) with our comprehensive course. Learn to build NLP models using PyTorch, delve into classification models, and apply techniques like bag-of-words, count vectorizer and so on. Perfect for professionals seeking to enhance their skills and aspiring students entering the tech industry.\nWho Should Enroll:\nProfessionals: Expand your skill set with NLP for real-world applications in diverse industries. \nAspiring Students: Master text data analysis and kickstart your career in AI and NLP.\nCourse curriculum\nIntroduction to NLP\nWhat is NLP\nCommon tasks in a NLP Project\nNLP Libraries\nResources for the Course\nMethods of Text Preprocessing - Part 1\nMethods of Text Preprocessing - Part 2\nMethods of Text Preprocessing - Part 3\nQuiz\nBuilding a basic classification model\nIntroduction to dataset and problem statement\nCreating a Basic Review Classification Model\nUnderstanding TF-IDF and its implementations\nUnderstanding N-grams\nAdvanced Preprocessing Techniques\nBuilding an basic ANN model\nLimitations of ANN\nQuiz\nKey Takeaways from the course\nAcquire practical skills in building NLP models using PyTorch\nCareer Readiness: Prepare to apply NLP across industries, equipping yourself for roles in data science, AI, and text analysis with hands-on exercises.", "Course Description": "Gain practical insights into Natural Language Processing (NLP) with our comprehensive course. Learn to build NLP models using PyTorch, delve into classification models, and apply techniques like bag-of-words, count vectorizer and so on. 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We will be covering popular clustering algorithms and DBSCAN and show you its applications on a real-world business problem.\nWhy Unsupervised Machine Learning?\nUnsupervised machine learning helps uncover hidden patterns and structures in data without labeled examples. It is essential for exploratory data analysis, reducing dimensionality, and discovering intrinsic relationships within datasets. Mastering unsupervised techniques enhances data preprocessing and drives insights in complex datasets where labels are scarce or unavailable.\nWho Should Enroll:\nProfessionals: Individuals looking to expand their skill set and apply unsupervised learning across different industries.\nAspiring Students: For those setting out on their journey to mastering machine learning and making a mark in the tech world.\nCourse curriculum\nUnderstanding Unsupervised Machine Learning\nResources to be used in this course.\nSetting the Context\nChoosing Clustering Algorithms\nSolving our Problem using k-means - Part 1\nSolving our Problem using k-means - Part 2\nFinding optimal K value\nAnalysis and Insights Based on the Plots\nIntroduction to Hierarchical Clustering Analysis (HCA)\nSolving our Problem using Hierarchical Clustering\nIntroduction to DBSCAN Clustering\nSolving our Problem using DBSCAN\nReading: Applications of Clustering in the Real World\nProject\nKey Takeaways from the course\nLearn machine learning techniques and build real-world Unsupervised ML Models.\nHands-On Experience: Engage with exercises designed to reinforce your learning and apply concepts in real-world scenarios.", "Course Description": "Get ahead of the crowd with this free course on Unsupervised Machine Learning Models. 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Decision Tree\nProject\nBoosting\nIntroduction to Boosting\nAdaBoost Step-by-Step Explanation\nHands-on - AdaBoost\nGradient Boosting Machines (GBM)\nHands-on Gradient Boost\nOther Algo (XGBoost, LightBoost. CatBoost)\nProject: Anova Insurance\nBagging and Boosting ML Algorithms\nThis course will provide you with a hands-on understanding of Bagging and Boosting techniques in machine learning. By the end of the course, you will be proficient in implementing and tuning these ensemble methods to enhance model performance. You'll learn to apply algorithms like Random Forest, AdaBoost, and Gradient Boosting to a real-world dataset, equipping you with the skills to improve predictive accuracy and robustness in your projects.\nWho Should Enroll:\nProfessionals: Individuals looking to deepen their knowledge and apply advanced machine learning techniques like Bagging and Boosting to solve complex problems across various domains\nAspiring Students: Individuals looking to deepen their knowledge and apply advanced ML techniques to bring value to businesses\nKey Takeaways from the Course\nLear to effectively use Bagging and Boosting Algorithms\nHands-On Experience: Engage in practical exercises to reinforce learning and apply concepts, ensuring you gain the skills to utilize these algorithms", "Course Description": "This course will provide you with a hands-on understanding of Bagging and Boosting techniques in machine learning. By the end of the course, you will be proficient in implementing and tuning these ensemble methods to enhance model performance. 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This free course will serve as the perfect path to help beginners navigate the complex terrain of Data Preprocessing and prepare any data for modelling.\nCourse curriculum\nPreparing the Dataset for Machine Learning Model\nResources to be used in this course\nIntroduction to Problem Statement\nReading Material - Understanding the Data\nML-workflow\nTasks to be Performed\nCombining Product Attribute Data with POS Data\nCombining all the tables in the Dataframe\nUnderstanding the Combined Data\nTreating Missing Values - Part 1\nTreating Missing Values Part - 2\nOutlier Detection and Treatment\nPreparing the Dataset for Supervised and Unsupervised Models\nGenerative AI for Data Analysis\nData Processing on a Real World Problem Statement\nThis course will help you get a practical understanding of Data Preprocessing. After this course, you can work on any data and prepare it for modelling. With a carefully curated list of resources, this course is your first step to becoming a Data Scientist. By the end of the course, you will have mastered techniques like EDA and Missing Value Treatment.\nWho Should Enroll:\nProfessionals: Individuals looking to expand their skill set on data cleaning and preparation.\nAspiring Students: For those setting out on their journey to become a data scientist and making a mark in the tech world.\nKey Takeaways from the course\nInclude a list of items to support the central theme of your page. Bulleted lists are a great way to parse information into digestible pieces.\nLearn data preprocessing tasks\nHands-On Experience: Engage with exercises designed to reinforce your learning and apply concepts in real-world scenarios", "Course Description": "This course will help you get a practical understanding of Data Preprocessing. After this course, you can work on any data and prepare it for modelling. With a carefully curated list of resources, this course is your first step to becoming a Data Scientist. 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Learn to evaluate LLMs based on accuracy, cost, scalability, and more, while exploring real-world applications to make informed, strategic AI decisions.\nThis course will guide you through the process of selecting the most suitable Large Language Model (LLM) for various business needs. By examining factors such as accuracy, cost, scalability, and integration, you will understand how different LLMs perform in specific scenarios, from customer support to healthcare and strategy development. The course emphasizes practical decision-making with real-world case studies, helping businesses navigate the rapidly evolving LLM landscape effectively.\nCourse curriculum\nIntroduction\nIntroduction\nIt's an LLM World!\nIt's an LLM World!\nUnderstand Your Business\nUnderstand Your Business\nFramework to Choose the Right LLM\nFramework to Choose the Right LLM\nCase Studies\nCase Studies\nConclusion\nConclusion\nWho should Enroll?\nBusiness leaders seeking to implement AI-driven solutions efficiently.\nData scientists exploring LLMs for industry-specific applications.\nTech professionals involved in AI integration and decision-making processes.\nKey Takeaways\nUnderstand how to evaluate and select the right LLM for business needs.\nLearn to assess LLMs based on accuracy, cost, scalability, and integration.\nGain insights into real-world LLM applications through case studies.\nDevelop practical decision-making skills for LLM adoption in various industries.\nFAQ\nKey factors include the LLM's accuracy, cost, scalability, technical compatibility, support, security, and compliance with privacy laws. The decision-making framework ensures the chosen LLM aligns with specific business requirements.\nNo, different LLMs are suited to different tasks. Selecting the right LLM depends on the specific business problem, required capabilities, and available resources.\nAccuracy is crucial, especially in fields like healthcare and education. LLMs must perform reliably across datasets, ensuring consistency and stability in results for critical business applications.\nKey decisions include choosing an LLM fine-tuned for medical data, ensuring accuracy, maintaining privacy, and complying with healthcare regulations.\nYes, open-source LLMs, like Llama3, can be viable alternatives, especially when customized to specific business needs. They are catching up with closed-source options in performance.", "Course Description": "This course will guide you through the process of selecting the most suitable Large Language Model (LLM) for various business needs. By examining factors such as accuracy, cost, scalability, and integration, you will understand how different LLMs perform in specific scenarios, from customer support to healthcare and strategy development. 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Explore practical solutions, advanced retrieval strategies, and agentic RAG systems to improve context, relevance, and accuracy in AI-driven applications.\nThis course explores the key challenges in building real-world Retrieval-Augmented Generation (RAG) systems and provides practical solutions. Topics include improving data retrieval, dealing with hallucinations, context selection, and optimizing system performance using advanced prompting, retrieval strategies, and evaluation techniques. Through hands-on demos, you will gain insights into better chunking, embedding models, and agentic RAG systems for more robust, real-world applications.\nCourse curriculum\nImproving Real World RAG System\nIntroduction to RAG Systems\nResources\nRAG System Challenges Practical Solutions\nHands-on: Solution for Missing Content in RAG\nOther Key Challenges\nPractical Solutions\nHands-on: Solution for Missed Top Ranked, Not in Context, Not Extracted _ Incorrect SpecificityHands-on- Solution for Missed\nWrong Format Problem Solution\nHands-on: Solution for Wrong Format\nIncomplete Problem Solution\nHyDE\nOther Practical Solutions from recent Research Papers\nWho should Enroll?\nAI/ML professionals aiming to enhance RAG system performance and solve real-world challenges.\nDevelopers/Engineers building search, conversational, or generative AI systems needing better data retrieval and context handling.\nResearchers/Enthusiasts seeking hands-on experience with advanced RAG techniques and agentic systems.\nKey Takeaways\nMaster RAG systems with a solid grasp of architecture and components.\nSolve key challenges like missing content and hallucinations.\nOptimize performance with advanced chunking and retrieval strategies.\nDevelop practical decision-making skills for LLM adoption in various industries.\nFrequently Asked Questions (FAQs)\nA basic understanding of AI/ML principles is needed, along with some experience in machine learning frameworks such as PyTorch or TensorFlow. Familiarity with natural language processing (NLP) concepts will be helpful but not mandatory.\nYes, the course provides practical, hands-on exercises through demos and notebooks. You\u2019ll have opportunities to implement RAG systems and experiment with real-world use cases, focusing on improving retrieval and generation tasks.\nYou\u2019ll need access to Python, Jupyter Notebooks, and relevant libraries such as LangChain, Hugging Face, and vector databases. The course will guide you through setting up the necessary environment for practicing the techniques.\nUnlike general AI/ML courses, this course zeroes in on Retrieval-Augmented Generation (RAG) systems, addressing practical challenges like hallucinations, retrieval errors, and context optimization, with a strong emphasis on real-world applications.\nYes, the course covers advanced techniques like hyperparameter tuning, chunking strategies, embedding models, context compression, and agentic RAG systems, giving you the tools to build and optimize high-performing RAG systems.", "Course Description": "This course explores the key challenges in building real-world Retrieval-Augmented Generation (RAG) systems and provides practical solutions. Topics include improving data retrieval, dealing with hallucinations, context selection, and optimizing system performance using advanced prompting, retrieval strategies, and evaluation techniques. 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Master the art of integrating SSM with deep learning, unravel the complexities of models like Mamba, and elevate your understanding of Generative AI's newest and most innovative models. This course is designed to equip you with the skills needed to understand these cutting-edge AI models and how they work, making you proficient in the latest AI techniques and architectures.\nCourse curriculum\nCourse Overview\nCourse Overview\nAn Alternative to Transformers\nAre RNNs a Solution\nThe Problem with Transformers\nUnderstanding State Space Models\nWhat is a State Space Model?\nThe Discrete Representation\nThe Recurrent Representation\nThe Convolution Representation\nThe Three Representations\nThe Importance of the A Matrix\nMamba - A Selective State Space Model\nWhat Problem does it attempt to Solve?\nSelectively Retaining Information\nSpeeding Up Computations\nExploring the Mamba Block\nJamba - Mixing Mamba with Transformers\nWho should Enroll?\nAI and ML professionals looking to specialize in State Space Models and Mamba architecture.\nData scientists interested in exploring advanced Generative AI models and architectures.\nNLP practitioners who want to integrate SSMs like Mamba in their workflows and use cases.\nKey Takeaways\nA comprehensive understanding of State Space Models (SSM)\nIn-depth exploration of The Mamba Architecture\nVisual guides and workflows on SSM and Mamba\nAdvanced applications, comparisons and practical use cases.\nFAQ\nState Space Models (SSM) are used in machine learning to model and predict systems that evolve over time. They represent the system's state as a dynamic process, helping to capture temporal patterns in data, making them useful for tasks like time series forecasting, control systems, and natural language processing.\nState Space Models (SSM) and traditional Recurrent Neural Networks (RNNs) both handle sequential data, but they differ in approach. SSMs use a mathematical framework to model the system's state and evolution over time explicitly. In contrast, RNNs use neural networks to implicitly learn patterns in sequences without explicitly modeling the system's state.\nMamba is an alternative AI architecture designed to address the limitations of traditional transformers. It enhances efficiency with optimizations like RMSnorm and offers significant improvements in inference speed\u2014up to 5\u00d7 higher throughput. Mamba also scales linearly with sequence length, making it highly effective for handling real-world data, even with sequences up to a million tokens. As a versatile backbone, Mamba achieves state-of-the-art performance across various domains, including language, audio, and genomics. Notably, the Mamba-3B model outperforms transformers of the same size and rivals those twice its size in both pretraining and downstream evaluation.\nMamba architecture differs from traditional transformer models by leveraging state-space models (SSMs) instead of the self-attention mechanism. This key difference allows Mamba to achieve linear complexity scaling with sequence length, a significant improvement over the quadratic scaling seen in transformers. While transformers excel in parallel processing with self-attention, Mamba's use of SSMs enables it to handle sequences more efficiently, especially in tasks involving long sequences, while still supporting parallel processing during training.\nState Space Models (SSM) are used in NLP for similar applications as other Language Models (LLMs), such as predicting and modeling sequential language patterns. However, SSMs stand out due to their ability to handle long text sequences more efficiently, making them particularly advantageous in tasks that involve processing extensive dependencies within the text.\nTransformers use self-attention mechanisms to process input data in parallel, allowing large language models to efficiently learn relationships between words in a sequence, improving performance in tasks like translation and text generation.\nRNNs are still used in AI because they excel at handling sequential data with strong temporal dependencies, and their simpler architecture can be advantageous in specific applications where transformers might be overkill.\nMamba architecture offers improved efficiency in deep learning models, particularly in handling complex tasks and large-scale AI applications, making it a powerful alternative to traditional transformers.\nState Space Models improve AI accuracy by explicitly modeling the underlying state of a system over time, leading to better predictions and more interpretable results, especially in time-sensitive applications.", "Course Description": "Unlock the Power of State Space Models (SSM) like Mamba with our comprehensive course designed for AI professionals, data scientists, and NLP enthusiasts. Master the art of integrating SSM with deep learning, unravel the complexities of models like Mamba, and elevate your understanding of Generative AI's newest and most innovative models. This course is designed to equip you with the skills needed to understand these cutting-edge AI models and how they work, making you proficient in the latest AI techniques and architectures.", "embedding": [0.031609829515218735, 0.030047543346881866, -0.023937895894050598, -0.01581113412976265, -0.03761903569102287, 0.0014129928313195705, -0.005335650406777859, -0.010662323795258999, -0.005756339058279991, -0.05422200635075569, 0.019428228959441185, 0.01446389127522707, -0.0019443127093836665, 0.060704298317432404, 0.008855621330440044, -0.08249682933092117, 0.04384516552090645, -0.011828546412289143, -0.02690601907670498, 0.012278599664568901, -0.011656423099339008, -0.014702205546200275, -0.021520055830478668, 0.06980033218860626, -0.023814057931303978, -0.03127977252006531, -0.05148251727223396, -0.009828198701143265, -0.00011459870438557118, -0.068275086581707, -0.013224436901509762, 0.011303434148430824, 0.0395146980881691, 0.0020341479685157537, 2.0075988231837982e-06, 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