Course Titles: ['GenAI Applied to Quantitative Finance: For Control Implementation', 'Navigating LLM Tradeoffs: Techniques for Speed, Cost, Scale & Accuracy', 'Creating Problem-Solving Agents using GenAI for Action Composition', 'Improving Real World RAG Systems: Key Challenges & Practical Solutions', 'Framework to Choose the Right LLM for your Business', 'Building Smarter LLMs with Mamba and State Space Model', 'Generative AI - A Way of Life - Free Course', 'Building LLM Applications using Prompt Engineering - Free Course', 'Building Your First Computer Vision Model - Free Course', 'Bagging and Boosting ML Algorithms - Free Course', 'MidJourney: From Inspiration to Implementation - Free Course', 'Understanding Linear Regression - Free Course', 'The Working of Neural Networks - Free Course', 'The A to Z of Unsupervised ML - Free Course', 'Building Your first RAG System using LlamaIndex - Free Course', 'Data Preprocessing on a Real-World Problem Statement - Free Course', 'Exploring Stability.AI - Free Course', 'Building a Text Classification Model with Natural Language Processing - Free Course', 'Getting Started with Large Language Models', 'Introduction to Generative  AI', 'Nano Course: Dreambooth-Stable Diffusion for Custom Images', 'A Comprehensive Learning Path for Deep Learning in 2023', 'A Comprehensive Learning Path to Become a Data Scientist in 2024', 'Nano Course: Building Large Language Models for Code', 'Certified AI & ML BlackBelt+ Program', 'Machine Learning Summer Training', 'AI Ethics by Fractal', 'A Comprehensive Learning Path to Become a Data Engineer in 2022', 'Certified Business Analytics Program', "Certified Machine Learning Master's Program (MLMP)", 'Certified Natural Language Processing MasterÆs Program', "Certified Computer Vision Master's Program", 'Applied Machine Learning - Beginner to Professional', 'Ace Data Science Interviews', 'Writing Powerful Data Science Articles', 'Machine Learning Certification Course for Beginners', 'Data Science Career Conclave', 'Top Data Science Projects for Analysts and Data Scientists', 'Getting Started with Git and GitHub for Data Science Professionals', 'Machine Learning Starter Program', 'Data Science Hacks, Tips and Tricks', 'Introduction to Business Analytics', 'Introduction to PyTorch for Deep Learning', 'Introductory Data Science for Business Managers', 'Introduction to Natural Language Processing', 'Getting started with Decision Trees', 'Introduction to Python', 'Loan Prediction Practice Problem (Using Python)', 'Big Mart Sales Prediction Using R', 'Twitter Sentiment Analysis', 'Pandas for Data Analysis in Python', 'Support Vector Machine (SVM) in Python and R', 'Evaluation Metrics for Machine Learning Models', 'Fundamentals of Regression Analysis', 'Getting Started with scikit-learn (sklearn) for Machine Learning', 'Convolutional Neural Networks (CNN) from Scratch', 'Dimensionality Reduction for Machine Learning', 'K-Nearest Neighbors (KNN) Algorithm in Python and R', 'Ensemble Learning and Ensemble Learning Techniques', 'Linear Programming for Data Science Professionals', 'Naive Bayes from Scratch', 'Learn Swift for Data Science', 'Introduction to Web Scraping using Python', 'Tableau for Beginners', 'Getting Started with Neural Networks', 'Introduction to AI & ML', 'Winning Data Science Hackathons - Learn from Elite Data Scientists', 'Hypothesis Testing for Data Science and Analytics']
Course Descriptions: ['This course explores the application of Generative AI in quantitative finance, focusing on building sustainable trading algorithms through keyword extraction, sentiment analysis, and time-series forecasting. Learn to predict commodity prices, such as gold, by integrating data from financial news sources, leveraging sentiment analysis, and optimizing models for robust trading signals.', "This course provides a concise guide to optimizing Large Language Models (LLMs) by navigating tradeoffs in speed, cost, scale, and accuracy. Learn practical techniques like LoRA, model quantization, and parameter-efficient fine-tuning to improve performance while reducing costs. You'll explore various deployment strategies and understand how to evaluate LLMs using industry-standard benchmarks, making this course ideal for anyone seeking efficient, scalable AI solutions.", 'This introductory course provides a concise overview of Agentic AI systems, covering their evolution, current state, and practical applications. You will explore key topics including the history of Agentic AI systems, the role of agents today, multi-agent systems, and practical solutions for implementing them. Perfect for those seeking a foundational understanding of intelligent Agentic AI systems in action.', '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. Through hands-on demos, you will gain insights into better chunking, embedding models, and agentic RAG systems for more robust, real-world applications.', '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. The course emphasizes practical decision-making with real-world case studies, helping businesses navigate the rapidly evolving LLM landscape effectively.', "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.", "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╖E3. 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.", "\nThis 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. 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:\n\nProfessionals:\xa0Individuals looking to deepen their knowledge and apply advanced LLM and prompt engineering techniques to solve complex problems across various domains.\nAspiring Students:\xa0Individuals looking to deepen their knowledge and apply advanced LLM and prompt engineering techniques to solve complex problems across various domains.\n\n\n", '\nThis course will help you gain a deep understanding of Computer Vision and build advanced CV models using the PyTorch framework. With a carefully curated list of resources and exercises, this course is your guide to becoming a Computer Vision expert. Master the techniques to build convolutional neural networks, and classify images.\nWho Should Enroll:\n\nProfessionals: Individuals looking to expand their skill set and leverage CV across different industries.\nAspiring Students: For those setting out on their journey to master image data analysis and leave a mark in the tech world.\n\n', "\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.\n\nWho Should Enroll:\n\nProfessionals:\xa0Individuals looking to deepen their knowledge and apply advanced machine learning techniques like Bagging and Boosting to solve complex problems across various domains\nAspiring Students:\xa0Individuals looking to deepen their knowledge and apply advanced ML techniques to bring value to businesses\n\n", "\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.\n\nWho Should Enroll:\n\nCreative Professionals:\xa0Individuals looking to enhance their creativity and apply MidJourney tools to various artistic and visual projects.\nAspiring Creatives:\xa0Those beginning their journey into visual storytelling and digital art, seeking to learn the fundamentals of MidJourney and its alternatives.\n\n", '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. With a carefully curated list of resources and exercises, this course serves as your comprehensive guide to mastering linear regression.\xa0', '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. It is recommended that you complete the advanced Machine Learning course before taking up this course.', 'Unsupervised 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.', 'This course will guide you\xa0through building your first Retrieval-Augmented Generation (RAG) system\xa0using LlamaIndex.\xa0You 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.', '\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:\n\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\xa0and making a mark in the tech world.\n\n', "\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.\n\nWho Should Enroll:\n\nProfessionals:\xa0Individuals aiming to enhance their skill set and apply Stability.AI tools/Stable Diffusion in various fields.\nAspiring Students:\xa0Those beginning their journey to mastering Generative AI tool deployment and customization, looking to make an impact in the evolving world of Generative AI\n\n", '\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:\n\nProfessionals: Expand your skill set with NLP for real-world applications in diverse industries.\xa0\nAspiring Students: Master text data analysis and kickstart your career in AI and NLP.\n\n', '\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.\n', "\n\nThis course will provide you with a comprehensive understanding of generative AI, including text and image generation techniques. By the end of the course, you will be have an understanding of using generative AI tools to create diverse content. You'll learn how generative AI works, engage in practical exercises, and gain the skills to implement these techniques in real-world projects.\nWho Should Enroll:\n\n\nProfessionals: Individuals looking to enhance their skills in generative AI and apply advanced techniques to create innovative solutions across various domains.\n\nAspiring Students: Individuals eager to enter the field of generative AI and apply generative AI techniques to tackle complex problems and generate creative content across different fields.\n\n\n\n\n", '\nHave you ever wondered how to turn your dreams into reality by creating images of your dog traveling around the world or yourself alongside Elon Musk or playing cricket with MSD?\nThis is exactly where the dreambooth model comes into the picture. With the help of Dreambooth, you can personalize the stable diffusion for a particular subject.\nGiven just 5 images of our subject, dreambooth can create new images across diverse scenes, poses, views, and lighting conditions that do not appear in the reference images.\nIn this free nano course on Dreambooth, Sandeep will discuss the historical journey of stable diffusion, its current landscape, and a brief understanding of the stable diffusion training process. Then we will move on to the dreambooth, its training process and finetune dreambooth on our custom dataset.\n\n', '\nThe most common question we get from beginners in the field of Deep Learning is - Where to begin? The journey to becoming a Deep Learning expert can be difficult if one does not have the right resources to follow. There are a million resources to refer and it is tough to decide where to start from.\nWe are here to help you take your first steps into the world of Deep Learning. Here is a free learning path for people who want to become a Deep Learning expert in 2023. We have arranged the best resources in a logical manner along with exercises to make sure that you only need to follow one single source to become a data scientist.\n', '\nWhere do I begin? Data science is such a huge field - where do you even start learning about Data Science?\nThese are career-defining questions often asked by data science aspirants. There are a million resources out there to refer but the learning journey can be quite exhausting if you donÆt know where to start.\nDonÆt worry, we are here to help you take your first steps into the world of data science! HereÆs the learning path for people who want to become a data scientist in 2023. We have arranged and compiled all the best resources in a structured manner so that you have a unified resource to become a successful data scientist.\nMoreover, we have added the most in-demand skills for the year 2023 for data scientists including storytelling, model deployment, and much more along with exercises and assignments.\n', '\nIn this Free Nano GenAI Course on Building Large Language Models for Code, you will-\n\nLearn how to train LLMs for Code from Scratch covering Training Data Curation, Data Preparation, Model Architecture, Training, and Evaluation Frameworks.\nExplore each step in-depth, delving into the algorithms and techniques used to create StarCoder, a 15B code generation model trained on 80+ programming languages.\nUnderstand and learn the best practices to train your own StarCoder on the data\n\n\n\n', '\nWhat happens when you combine ALL of Analytics VidhyaÆs comprehensive courses, curated and designed by instructors with decades of data science experience? You get the AI & ML BlackBelt+ program!\nThere are multiple elements that go into becoming an AI expert. Data Science, Machine Learning and Deep Learning are the core components you would need in our journey to break into the wonderful world of AI applications.\nAI & ML BlackBelt+ is a thoughtfully curated program designed for anyone wanting to learn data science, machine learning, deep learning in their quest to become an AI professional. It all starts here, so are you ready to take the ride?\nYou will get access to ALL the courses Analytics Vidhya has curated and designed as part of AI & ML Blackbelt+. What are you waiting for? Start your AI journey today!\n', 'This is the second step of the Machine Learning Summer Training, want to know more click here.', '\nAI has a huge influence on our lives. From typing on our smartphones, to personalized recommendations on our favourite shopping websites, intelligent machines are everywhere. Our interactions with technology have become more personalized, but with humans ultimately behind these creations, the question is: where does the responsibility lie? Why and how should we begin the AI ethics conversation at Fractal?\nLearning plan:\nThe video course is followed by MCQ\xa0test to gauge the depth of your understanding and help you retain your learning.\xa0\nLearners can take the e-learning and complete the MCQ Test activity post viewing the video.\n', '\nWhere do I begin? Data Engineering is such a huge field - where do you even start learning about Data Engineering?\nThese are career-defining questions often asked by data engineering aspirants. There are a million resources out there to refer but the learning journey can be quite exhausting if you donÆt know where to start.\nDonÆt worry, we are here to help you take your first steps into the world of data engineering! HereÆs the learning path for people who want to become a data engineer in 2022. We have arranged and compiled all the best resources in a structured manner so that you have a unified resource to become a successful data engineer.\nMoreover, we have added the most in-demand skills for the year 2022 for data engineers including storytelling, model deployment, and much more along with exercises and assignments.\xa0\n', '\nWith increase in data generated across the globe, the demand for Business Analytics professionals is rising continuously.\nIn short, aiming for a role in Business Analytics has never been a better career choice!\nCertified Business Analytics Program aims to provide you all the tools and techniques, along with hands on experience you need to succeed as a Business Analytics professional.\nThis program covers tools like Excel, Tableau, SQL, Python and covers all the techniques like Statistics and Exploratory Data Analysis. The program also covers Predictive modeling and basics of Machine Learning.\nMore importantly, the program helps you prepare your Resume, prepares you for Business Analytics Interviews and provides one on one mentorship during the program.\n', '\n\nNYC Taxi Trip Duration Prediction\n\n\n\nCustomer Churn Prediction\n\n\n\nWeb Page Classification\n\n\n\nMalaria Detection from blood cell Images\n\n\n\nPredict Survivors from Titanic\n\n\n\nSales Prediction for Large Super Markets\n\n\n\nMovie Recommender System\n\n\n\nArticle Recommender System\n\n\n\nOnline Book Recommender System\n\n\n\nMarket Basket Analysis for a Super Market\n\n\n\nForecasting the daily count of Airlines booking using historical data\n\n\n\nUsing Time series models for forecasting energy consumption\n\n\n\nForecasting web Traffic using Deep Learning\n\n\n', '\nNatural Language Processing (NLP) is one of the fastest growing field within Artificial Intelligence. It enables machines to understand information contained in any text.\nNLP bridges the gap between the abstract but omnipresent human languages with concise and concrete programming languages. As more and more organizations invest in data - they would need NLP experts.\nThis comprehensive program empowers you to become an NLP practitioner, even if you are a beginner and have no prior knowledge of the concepts.\nDownload Brochure\n', '\nWe have designed this certified program for Computer Vision enthusiasts like you who are looking for a place to start. Computer Vision is currently among the hottest fields in the industry. The demand for computer vision experts is outstripping the supply! So youÆve picked the perfect time to get into this field. This comprehensive program powers you to become a computer vision expert. The beauty of this program is that it assumes no prior knowledge of concepts. We start from the ground up by learning the basics of Python, statistics, core machine learning algorithms & fundamentals of Deep Learning.\nOnce your base is rock solid, jump over to the Computer Vision using Deep Learning course. It is designed to give you a taste of how the underlying techniques work in current State-of-the-Art Computer Vision systems, and walks you through remarkable Computer Vision applications in a hands-on manner so that you can create such solutions on your own.\n\nObject detection\nFace detection\nImage Classification\nImage Segmentation\nImage generation, and many others!\n\n\nYou can then combine your technical knowledge with the learning from the Ace Data Science Interviews course to land your dream job in data science & computer vision! The program consists of five comprehensive and rich courses curated exclusively by Analytics Vidhya.\n\xa0\nDownload Brochure\n', '\nMachine Learning is re-shaping and revolutionising the world and disrupting industries and job functions globally. It is no longer a buzzword - many different industries have already seen automation of business processes and disruptions from Machine Learning. In this age of machine learning, every aspiring data scientist is expected to upskill themselves in machine learning techniques & tools and apply them in real-world business problems.\n\nPre-requisites for the Applied Machine Learning course\nThis course requires no prior knowledge about Data Science or any tool.\nDownload Brochure\n', '\nAre you trying to get into data science roles but getting rejected by employers? Are you scared of getting into data science interviews? Or don\'t know what to expect in data science interviews? This is just the course you need.\nWhile you might know the tools and techniques in data science, clearing a data science interview might still prove very difficult. You need to show your problem solving skills and technical prowess in these data science interviews.\nThis course has been created based on hundreds of interviews we have taken, companies we have helped in data science interviews and several data science experts in the industry.\nKey learnings and takeaways from "Ace Data Science Interviews" course:\xa0\n\nUnderstand different roles existing in data science ecosystem(e.g.Data Scientist, Data Engineers, Data Analyst etc.)\nLearn what skill sets required for each of these roles\nUnderstand different types of Interviews which happen in Data Science Industry\nTips and tricks to Ace your Data Science Interviews\nHow to build your digital presence including LinkedIn and GitHub profile\nLearn the process to create a professional experience for data science roles.\nFramework to solve Guesstimates and case studies used in data science interviews\n\n\nDownloadable Resources:\n\nInfographic for 7 step process to "Ace Data Science Interviews"\ne-book containing more than 240 interview questions from interviews in industry.\nInterview Questions on machine learning, statistics, Model building, Machine Learning production, SQL\nChecklist for your LinkedIn and GitHub profiles\n\n', '\n"Either write something worth reading or do something worth writing." - Benjamin Franklin\nThe best way to learn any concept, especially in data science, is by writing about it. That not only helps you understand what you learned in more detail, but sharing it with the community helps others understand how a particular data science idea works.\nBut hereÆs the thing - most people want to write, but just canÆt get past the initial challenges. This might sound familiar to a lot of people:\n\nWhat should I write about?\nWill anyone read my article?\nHow do I make my article stand out?\nShould I even write?\n\n\nIf youÆve ever asked yourself these questions, youÆll find the answers in this free crash course on how to write impactful and awesome data science articles!\n', '\nMachine Learning is the science of teaching machines how to learn by themselves. Machine Learning is reshaping and revolutionizing the world and disrupting industries and job functions globally.\xa0\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.\xa0\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.\n', '\nIt feels like half the world wants to move into data science these days, with spectacular perks and a plethora of openings on offer in the industry. Organizations are investing heavily in data science talent to stay or move ahead of their competitors. As a data science aspirant, you couldnÆt have picked a better time to change your career!\n\nBut this comes with its own set of challenges. We are often asked by folks about how they should transition into data science. People from all sorts of backgrounds û IT, Sales, Finance, HR, Healthcare, etc. û they all want a piece of the data science pie.\nIn this exclusive course called the ôData Science Career Conclaveö, Analytics Vidhya has brought together leading data science experts to share their view on a broad range of data science career topics.\n\xa0\nWhat is being covered in this Data Science Career Conclave?\nAs we said, a broad range of topics related to transitioning into a data science career. HereÆs a brief list of topics you can look forward to:\n\nDifferent Roles in Data Science - Which Role is Right for You? - by Mathangi Sri\nWhat are Hiring Managers Really Looking For? - by Kiran R\nHow to Build your Digital Profile for Data Science - by Dipanjan Sarkar\nPanel Discussion: How can you Transition into Data Science in 12 Months?\n\n', '\nThis is a very common question interviewers ask in data science interviews. We have conducted hundreds of these interviews for both data analyst and data scientist roles and this is quite often the jackpot question. This is especially true if youÆre a fresher or a relative newcomer to data science.\nJust doing courses or attaining certifications isnÆt good enough. Almost everyone we know holds certifications in various aspects of data science. It adds no value to your resume if you donÆt combine it with practical experience.\nAnd thatÆs where open-source data science projects play such a key role!\n', '\nEver heard of version control? It is one of the most important concepts in a data scientistÆs daily role - and yet most newcomers and beginners havenÆt even come across it! This is a fallacy you must overcome as soon as possible.\nYou need to understand how to navigate through Git and GitHub if you want to make it as a data science professional. While a lot of folks know about these tools (having used them for cloning open source code from Google Research and other top data science organizations), they never really understand their real purpose.\nThe beauty of version control will be akin to a revelation. The way you can create a remote project and have all your team members work on different features parallelly yet independently but still have a stable running code at the end of the day - priceless! A lot of the problem we face in data science while working remotely and independently will be erased with a quick understanding of Git and GitHub.\nYes, this course really is that important!\n', "\n\n\nWe generate more than 2.5 quintillion bytes of data every day - and companies across the globe are hiring data scientists to make sense of this data.\nA data scientistÆs job is one of the most sought-after in the 21st century. Companies are increasingly looking for professionals with a variety of skills in Machine Learning. And that's what we are here to give you via our Machine Learning Starter Program.\nThis is the perfect starting point to ignite your fledgling machine learning career and take a HUGE step towards your dream data scientist role.\n", '\nThe Data Science Hacks, Tips and Tricks course is your one stop destination to become a better and more efficient data scientist!\nWe have poured in our decades of experience with data science and programming (especially Python programming!), to provide you with time-saving hacks related to:\n\nPython tips and tricks\nData exploration tips and tricks\nData preprocessing hacks\nEfficient use of Jupyter notebooks\nPython functions\nBuilding predictive models (hacks to build machine learning models in no time!),\n\n\nAnd much more!\xa0\nWe have created the Data Science hacks, tips and tricks course in a way that you can go through each hack as a separate module. Since the goal of the hacks, tips and tricks is to provide you with efficient code to solve problems, the videos are a demo of these hacks, tips and tricks. The videos are self-explanatory.\nThis free course by Analytics Vidhya covers a broad range of data science hacks, tips and tricks, including Python programming hacks, tips and tricks to ace data science tasks like data preprocessing and data exploration, and much more. Get started today!\n', '\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:\n\nData is ubiquitous! Organizations need people who can use Business Analytics tools and techniques to make sense of this data.\xa0\nIt is one of the hottest field in the industry right now\nThere 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\n\n', '\nWelcome to the world of PyTorch - a deep learning framework that has changed and re-imagined the way we build deep learning models.\n\xa0\nPyTorch was recently voted as the favorite deep learning framework among researchers. It has left TensorFlow behind and continues to be the deep learning framework of choice for many experts and practitioners.\n\xa0\nPyTorch is super flexible and is quite easy to grasp, even for deep learning beginners. If you work on deep learning and computer vision projects, youÆll love working with PyTorch.\n', '\nThe data science revolution is well and truly disrupting multiple and diverse industries. It has become the centerpiece of strategic decision making for organizations. Are you prepared to enable it for your business and your current role?\nThere is a serious shortage of decision-makers in the data science universe. While projects are springing up everywhere, the managers and leaders required to guide and mentor data science teams are rare to find.\nData Science for Managers is a thoughtfully curated program designed especially for decision-makers. Whether youÆre a manager, team leader, CxO or entrepreneur, you NEED to be data science educated. And this is the perfect program to get you there.\nYou will get access to three of the most comprehensive courses in this certified program. Enable yourself, and your business, to be ready for the Artificial Intelligence revolution!\n\nKey Takeaways from Certified Program: Introductory Data Science for Business Managers\n\n\nArtificial Intelligence and Machine Learning for Business Leaders: The ultimate Artificial Intelligence & Machine Learning course for CxOs, Managers, Team Leaders and Entrepreneurs\n\nIntroduction to Data Science: Every data science manager and leader should have a good hold on core data science techniques. This course will teach you the basics of the most popular data science language û Python, the basics of core statistics, and introduce you to the essential machine learning algorithms used in businesses today\n\nTableau 2.0 - Master Tableau from Scratch: Convert your data into actionable insights, create dashboards to impress your clients, and learn Tableau tips, tricks, and best practices for your analytics, business intelligence or data science role!\n\n\n\nWhy you should take this certified program?\n\n\nUpskill yourself for the AI Revolution:\xa0Artificial Intelligence has already started making a huge impact in various industries, roles and functions. The time to upskill yourself and become familiar with artificial intelligence and machine learning is NOW. This comprehensive program will enable you to do just that.\n\nEasy to understand content:\xa0Understanding data science concepts can be difficult. Especially if youÆre a mid or late-career transitioner coming from a non-technical background. ThatÆs why all the courses in this program have been curated and designed for people from all walks of life. We donÆt assume anything û this is data science from scratch.\n\nExperienced Instructors:\xa0All the material in this program was created by instructors who bring immense industry experience of data science. Combined among us, we have more than two decades of teaching experience.\n\nIndustry Relevant:\xa0All the courses in this program have been vetted by industry experts. This ensures relevance in the industry and enables you with the content which matters most.\n\nReal life problems:\xa0All projects in the program are modelled on real-world scenarios. We mean it when we say ôindustry relevantö!\n\n\nPrerequisites of\xa0Certified Program: Introductory Data Science for Business Managers\nThis program requires no past knowledge about Data Science or any tool.\n', '\nNatural Language Processing is the art of extracting information from unstructured text. Learn basics of Natural Language Processing, Regular Expressions & text sentiment analysis using machine learning in this course.\n\n', '\nWhat is a Decision Tree?\nA Decision Tree is a flowchart like structure, where each node represents a decision, each branch represents an outcome of the decision, and each terminal node provides a prediction / label.\n\nWhy learn about Decision Trees?\n\nDecision Trees are the most widely and commonly used machine learning algorithms.\nDecision Trees can be used for solving both classification as well as regression problems.\nDecision Trees are robust to Outliers, so if you have Outliers in your data - you can still build Decision Tree models without worrying about impact of Outliers on your model.\nDecision Trees are easy to interpret and hence have multiple applications in different industries.\n\n', '\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!\n\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\n\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 donÆt need to be well-versed with them all.\nMost data scientists have a few go-to libraries for their daily tasks like:\n\nfor performing data cleaning and analysis - pandas\nfor basic statistical tools û numpy, scipy\nfor data visualization û matplotlib, seaborn\n\n', "\nThis course is designed for people who want to solve binary classification problems. 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\n", '\nSales prediction is a very common real life problem that each company faces at least once in its life time. 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. \n', '\nWhat is Sentiment Analysis?\n\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.\xa0\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.\n\n\nWhat are the applications of Sentiment Analysis in the industry?\n\nIn the age of social media, it is extremely common to comment about\xa0\na movie you liked or\xa0\na book you didnÆt like or\xa0\na product you bought was not up to the mark.\n\n\nTherefore, a lot of companies use sentiment analysis for their products since it provides direct feedback of the customerÆs 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.\n\n\xa0\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:\n\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.\n\nFor other interesting problems involving sentiment/emotion detection, you can visit: https://datahack.analyticsvidhya.com/contest/all/\n\nWhat is the range of sentiments that can be observed and analysed?\n\nIn the earlier days of\xa0Natural language processing\xa0and 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\xa0\n\n\nCan I add this project to my resume and use it in my Interview?\n\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.\n\n', '\nPandas is one of the most popular Python libraries in data science. In fact, Pandas is among those elite libraries that draw instant recognition from programmers of all backgrounds, from developers to data scientists.\nAccording to a recent survey by StackOverflow, Pandas is the 4th most used library/framework in the world. That is quite an achievement!\nPandas is the first library we import when we fire up our Jupyter notebooks (æimport pandas as pdÆ is indelibly etched in our minds!). It is a super flexible tool that enables us to perform data analysis and data manipulation on Pandas dataframes in double-quick time.\n', '\nWant to learn the popular machine learning algorithm - Support Vector Machines (SVM)? Support Vector Machines can be used to build both Regression and Classification Machine Learning models.\nThis free course will not only teach you basics of Support Vector Machines (SVM) and how it works, it will also tell you how to implement it in Python and R.\nThis course on SVM would help you understand hyperplanes and Kernel tricks to leave you with one of the most popular machine learning algorithms at your disposal.\n', '\nEvaluation metrics form the backbone of improving your machine learning model. Without these evaluation metrics, we would be lost in a sea of machine learning model scores - unable to understand which model is performing well.\n\xa0\nWondering where evaluation metrics fit in? HereÆs how the typical machine learning model building process works:\n\nWe build a machine learning model (both regression and classification included)\nGet feedback from the evaluation metric(s)\nMake improvements to the model\nUse the evaluation metric to gauge the modelÆs performance, and\nContinue until you achieve a desirable accuracy\n\n\xa0\nEvaluation metrics, essentially, explain the performance of a machine learning model. An important aspect of evaluation metrics is their capability to discriminate among model results.\n\xa0\nIf youÆve ever wondered how concepts like AUC-ROC, F1 Score, Gini Index, Root Mean Square Error (RMSE), and Confusion Matrix work, well - youÆve come to the right course!\n', '\nLinear regression and logistic regression are typically the first algorithms we learn in data science. These are two key concepts not just in machine learning, but in statistics as well.\n\xa0\nDue to their popularity, a lot of data science aspirants even end up thinking that they are the only forms of regression! Or at least linear regression and logistic regression are the most important among all forms of regression analysis.\n\xa0\nThe truth, as always, lies somewhere in between. There are multiple types of regression apart from linear regression:\n\nRidge regression\nLasso regression\nPolynomial regression\nStepwise regression, among others.\n\n\xa0\nLinear regression is just one part of the regression analysis umbrella. Each regression form has its own importance and a specific condition where they are best suited to apply.\n\xa0\nRegression analysis marks the first step in predictive modeling. The different types of regression techniques are widely popular because theyÆre easy to understand and implement using a programming language of your choice.\n', '\nScikit-learn, or sklearn for short, is the first Python library we turn to when building machine learning models. Sklearn is unanimously the favorite Python library among data scientists. As a newcomer to machine learning, you should be comfortable with sklearn and how to build ML models, including:\n\nLinear Regression using sklearn\nLogistic Regression using sklearn, and so on.\n\n\xa0ThereÆs no question - scikit-learn provides handy tools with easy-to-read syntax. Among the pantheon of popular Python libraries, scikit-learn (sklearn) ranks in the top echelon along with Pandas and NumPy.\nWe love the clean, uniform code and functions that scikit-learn provides. The excellent documentation is the icing on the cake as it makes a lot of beginners self-sufficient with building machine learning models using sklearn.\nIn short, sklearn is a must-know Python library for machine learning. Whether you want to build linear regression or logistic regression models, decision tree or a random forest, sklearn is your go-to library.\n', '\nConvolutional Neural Networks, or CNN as theyÆre popularly called, are the go-to deep learning architecture for computer vision tasks, such as object detection, image segmentation, facial recognition, among others. CNNs have even been extended to the field of video analysis!\nIf you are picking one deep learning architecture to learn and are not sure where to start, you should go for convolutional neural networks. Deep learning enthusiasts and experts with CNN knowledge are being widely sourced in the industry.\nItÆs your time to use this CNN skillset and shine!\n', '\nHave you worked on a dataset with more than a thousand features? How about 40,000 features? We are generating data at an unprecedented pace right now and working with massive datasets in machine learning projects is becoming mainstream.\nThis is where the power of dimensionality reduction techniques comes to the fore. Dimensionality reduction is actually one of the most crucial aspects in machine learning projects.\nYou can use dimensionality reduction techniques to reduce the number of features in your dataset without having to lose much information and keep (or improve) the modelÆs performance. ItÆs a really powerful way to deal with huge datasets, as youÆll see in this course!\nEvery data scientist, aspiring established, should be aware of the different dimensionality reduction techniques, such as Principal Component Analysis (PCA), Factor Analysis, t-SNE, High Correlation Filter, Missing Value Ratio, among others.\nSo in this beginner-friendly course, you will learn the basics of dimensionality reduction and why you should know dimensionality reduction in machine learning. We will also cover 12 dimensionality reduction techniques! This course is as comprehensive an introduction as you can get!\n\n', '\nK-Nearest Neighbor (KNN) is one of the most popular machine learning algorithms. As a newcomer or beginner in machine learning, youÆll find KNN to be among the easiest algorithms to pick up.\nAnd despite its simplicity, KNN has proven to be incredibly effective at certain tasks in machine learning.\n\xa0\nThe KNN algorithm is simple to understand, easy to explain and perfect to demonstrate to a non-technical audience (thatÆs why stakeholders love it!). ThatÆs a key reason why itÆs widely used in the industry and why you should know how the algorithm works.\n', 'Ensemble learning is a powerful machine learning algorithm that is used across industries by data science experts. The beauty of ensemble learning techniques is that they combine the predictions of multiple machine learning models.\xa0You must have used or come across several of these ensemble learning techniques in your machine learning journey:- Bagging- Boosting- Stacking- Blending, etc.\xa0These ensemble learning techniques include popular machine learning algorithms such as XGBoost, Gradient Boosting, among others. You must be getting a good idea of how vast and useful ensemble learning can be!', '\nOptimization is the way of life. We all have finite resources and time and we want to make the most of them. From using your time productively to solving supply chain problems for your company û everything uses optimization.\nAnd thatÆs where learning linear programming will make you a better data science professional.\nWe are solving optimization problems everyday - without realizing it. Think of how you distributed the chocolate among your peers or siblings - thatÆs your way of optimizing the situation. On the other hand devising inventory and warehousing strategy for an e-tailer can be very complex. Millions of SKUs with different popularity in different regions to be delivered in defined time and resources.\nAnd linear programming helps us solve these optimization problems with ease and efficiency. As a data science professional, you are bound to come across these optimization problems that you will solve using linear programming.\n\xa0Simply put, you should know what linear programming is, and the different methods to solve linear programming problems.\n', '\nNaive Bayes ranks in the top echelons of the machine learning algorithms pantheon. It is a popular and widely used machine learning algorithm and is often the go-to technique when dealing with classification problems.\nThe beauty of Naive Bayes lies in itÆs incredible speed. YouÆll soon see how fast the Naive Bayes algorithm works as compared to other classification algorithms. It works on the Bayes theorem of probability to predict the class of unknown datasets. YouÆll learn all about this inside the course!\nSo whether youÆre trying to solve a classic HR analytics problem like predicting who gets promoted, or youÆre aiming to predict loan default - the Naive Bayes algorithm will get you on your way.\n', '\nhe Swift programming language is quickly becoming the language of choice for a lot of data science experts and professionals. SwiftÆs flexibility, ease of use, excellent documentation, and quick execution speed are key reasons behind the languageÆs recent prominence in the data science space.\nSwift is a more efficient, stable and secure programming language as compared to Python. In fact, Swift is also a good language to build for mobile. In fact, itÆs the official language for developing iOS applications for the iPhone!\nThe cherry on the cake for Swift? It has the support of the likes of Google, Apple, and FastAI behind it!\nôI always hope that when I start looking at a new language, there will be some mind-opening new ideas to find, and Swift definitely doesnÆt disappoint. Swift tries to be expressive, flexible, concise, safe, easy to use, and fast. Most languages compromise significantly in at least one of these areas.ö û Jeremy Howard\nAnd when Jeremy Howard endorses a language and starts using it for his daily data science work, you need to drop everything and listen.\nIn this free course on Swift for Data Science, we will learn about Swift as a programming language and how it fits into the data science space. If youÆre a Python user, youÆll notice the subtle differences and the incredible similarities between the two. We showcase Swift code as well in the course so get started!\n\n', '\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.\nThatÆs 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 youÆre 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 û 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 websiteÆs 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 weÆll implement web scraping using Python in a real-world project. ThereÆs a lot to unpack here so enroll today and start learning!\n', '\nTableau is the gold standard in business intelligence, analytics and data visualization tools. 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. HereÆs GartnerÆs most recent ranking in 2020:\n\n\nIn this Tableau for Beginners course, you will learn everything you need to get started with this wonderful visualization and business intelligence tool. YouÆll 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!\n\xa0\nNote: If youÆre looking to build and master dashboards and storyboards in Tableau, make sure you check out the popular æMastering Tableau from Scratch: Become a Data Visualization RockstarÆ course!\n', '\nIntroduction to Neural Networks\nWhat is a neural network? How does it work? What does a neural network do? Learn neural networks for free in this course and get your neural network questions answered, including applications of neural networks in deep learning.\n\nLearn how neural networks work in deep learning\nDo you want to acquire a super power? How about learning neural networks? Neural networks are at the heart of the deep learning revolution thatÆs happening around us right now.\nNeural networks are the present and the future. The different neural network architectures like convolutional neural networks (CNN), recurrent neural networks (RNN), and others have altered the deep learning landscape.\nBut as a beginner in this field, youÆll have a ton of questions:\n\nWhat is a neural network?\nWhy do we need to learn neural networks?\nHow popular are neural networks?\nWhat are the advantages of neural networks?\nWhat kind of challenges you could face when applying neural networks?\nWhat exactly should you learn about neural networks?\nWhat are the core concepts that make up neural networks?\nWhat are the different types of neural networks in deep learning?\nDo you need to know programming to build a neural network?\nWhich programming language is best for building neural networks? Python or R?\nWhat are the different applications of neural networks?\nWhat kind of problems or projects can you solve using neural networks?\n\n\nFrom classifying images and translating languages to building a self-driving car, neural networks are powering the world around us. Thanks to the idea of neural networks like CNN and RNN, deep learning has penetrated into multiple and diverse industries, and it continues to break new ground on an almost weekly basis!\n', '\nAnalytics VidhyaÆs æIntroduction to AI and MLÆ 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 hasnÆt been a better time to get into this field!\n', '\nThere is no substitute for experience. And that holds true in Data Science competitions as well. These cut-throat hackathons require a lot of trial-and-error, effort, and dedication to reach the ranks of the elite.\nThis course is an amalgamation of various talks by top data scientists and machine learning hackers, experts, practitioners, and leaders who have participated and won dozens of hackathons. They have already gone through the entire learning process and they showcase their work and thought process in these talks.\xa0\nThis course features top data science hackers and experts, including Sudalai Rajkumar (SRK), Dipanjan Sarkar, Rohan Rao, Kiran R and many more!\nFrom effective feature engineering to choosing the right validation strategy, there is a LOT to learn from this course so get started today!\n', '\nStatistics is the study of the collection, analysis, interpretation, presentation, and organisation of data. For all the data science and machine learning enthusiasts it is paramount to be well versed with various statistical concepts such as Hypothesis testing\nEvery day we find ourselves testing new ideas, finding the fastest route to the office, the quickest way to finish our work, or simply finding a better way to do something we love. The critical question, then, is whether our idea is significantly better than what we tried previously.\nThese ideas that we come up with on such a regular basis û thatÆs essentially what a hypothesis is. And testing these ideas to figure out which one works and which one is best left behind, is called hypothesis testing.\n\xa0\n']
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Length of Course Titles: 68
Length of Course Descriptions: 68
Length of Course Ratings: 68
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