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METHOD:PUBLISH
X-ORIGINAL-URL:https://webgyrlzcode.org
X-WR-CALDESC:Events for Webgyrlz Code Computer Science Training
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X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:UTC
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TZOFFSETFROM:+0000
TZOFFSETTO:+0000
TZNAME:UTC
DTSTART:20230101T000000
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BEGIN:VEVENT
DTSTART;TZID=UTC:20240801T190000
DTEND;TZID=UTC:20240801T220000
DTSTAMP:20260421T134228
CREATED:20240416T122430Z
LAST-MODIFIED:20240416T122430Z
UID:2825-1722538800-1722549600@webgyrlzcode.org
SUMMARY:Algorythm™| Become a Machine Learning Ninja
DESCRIPTION:WHAT IS THIS ALGORYTHM COURSE ABOUT? \nThis course provides a robust foundation on machine learning concepts and applications. This course is designed for students who have little to no technical background\, yet are committed to venture into the AI space. \nWHO IS THIS COURSE FOR? \n\n(Non-tech) Entrepreneurs who want to build AI startups\nCareer switchers from non-tech background\nStudents exploring AI space\n\nCOURSE AGENDA: \n-> Difference between ML\, DL and Data Science \n-> Description & Applications of LLMs\, NLP\, Computer Vision\, BayesianAI \n-> Introduction of the following concepts: \n\nSupervised learning vs Unsupervised learning\nLearning and logic regression\nK-means clustering\nDecision Tree\nBoosting and bagging algorithm\nTime series modeling\nKernel SVM\nNaive Bayes\nRandom forest classifiers\n\n-> Existing applications of ML + Opportunities \nKey Takeaways: \n🤖 Live Q&A and Case Discussions (with monthly invitation to a Q&A with the alumni network) \n🤖 Coursework\, handouts\, & Case Challenges \n🤖 Certificate of completion upon request \nP.S More Algorythm courses coming up on each one of these concepts\, follow for updates. \nReading Appetizers: \n🤖 ALGORYTHM | How Can Sherlock Holmes Use Data Science for his Stellar Detective Work? \n🤖 ALGORYTHM | What is applied AI? A potential to solve world problems or…? \nWelcome aboard\, master of the machines!
URL:https://webgyrlzcode.org/event/algorythm-become-a-machine-learning-ninja-16/
CATEGORIES:Artificial Intelligence,Virtual event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240728T190000
DTEND;TZID=UTC:20240728T220000
DTSTAMP:20260421T134228
CREATED:20240416T135257Z
LAST-MODIFIED:20240416T135257Z
UID:2918-1722193200-1722204000@webgyrlzcode.org
SUMMARY:MINDSHOP™| AI FOR ALL
DESCRIPTION:AI FOR ALL
URL:https://webgyrlzcode.org/event/mindshop-ai-for-all-15/
CATEGORIES:Artificial Intelligence,Tech,Virtual event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240725T190000
DTEND;TZID=UTC:20240725T220000
DTSTAMP:20260421T134228
CREATED:20240416T122345Z
LAST-MODIFIED:20240416T122345Z
UID:2823-1721934000-1721944800@webgyrlzcode.org
SUMMARY:Algorythm™| Become a Machine Learning Ninja
DESCRIPTION:WHAT IS THIS ALGORYTHM COURSE ABOUT? \nThis course provides a robust foundation on machine learning concepts and applications. This course is designed for students who have little to no technical background\, yet are committed to venture into the AI space. \nWHO IS THIS COURSE FOR? \n\n(Non-tech) Entrepreneurs who want to build AI startups\nCareer switchers from non-tech background\nStudents exploring AI space\n\nCOURSE AGENDA: \n-> Difference between ML\, DL and Data Science \n-> Description & Applications of LLMs\, NLP\, Computer Vision\, BayesianAI \n-> Introduction of the following concepts: \n\nSupervised learning vs Unsupervised learning\nLearning and logic regression\nK-means clustering\nDecision Tree\nBoosting and bagging algorithm\nTime series modeling\nKernel SVM\nNaive Bayes\nRandom forest classifiers\n\n-> Existing applications of ML + Opportunities \nKey Takeaways: \n🤖 Live Q&A and Case Discussions (with monthly invitation to a Q&A with the alumni network) \n🤖 Coursework\, handouts\, & Case Challenges \n🤖 Certificate of completion upon request \nP.S More Algorythm courses coming up on each one of these concepts\, follow for updates. \nReading Appetizers: \n🤖 ALGORYTHM | How Can Sherlock Holmes Use Data Science for his Stellar Detective Work? \n🤖 ALGORYTHM | What is applied AI? A potential to solve world problems or…? \nWelcome aboard\, master of the machines!
URL:https://webgyrlzcode.org/event/algorythm-become-a-machine-learning-ninja-15/
CATEGORIES:Artificial Intelligence,Virtual event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240721T190000
DTEND;TZID=UTC:20240721T220000
DTSTAMP:20260421T134228
CREATED:20240416T135155Z
LAST-MODIFIED:20240416T135155Z
UID:2916-1721588400-1721599200@webgyrlzcode.org
SUMMARY:MINDSHOP™| AI FOR ALL
DESCRIPTION:AI FOR ALL
URL:https://webgyrlzcode.org/event/mindshop-ai-for-all-14/
CATEGORIES:Artificial Intelligence,Tech,Virtual event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240718T190000
DTEND;TZID=UTC:20240718T220000
DTSTAMP:20260421T134228
CREATED:20240416T122212Z
LAST-MODIFIED:20240416T122212Z
UID:2821-1721329200-1721340000@webgyrlzcode.org
SUMMARY:Algorythm™| Become a Machine Learning Ninja
DESCRIPTION:WHAT IS THIS ALGORYTHM COURSE ABOUT? \nThis course provides a robust foundation on machine learning concepts and applications. This course is designed for students who have little to no technical background\, yet are committed to venture into the AI space. \nWHO IS THIS COURSE FOR? \n\n(Non-tech) Entrepreneurs who want to build AI startups\nCareer switchers from non-tech background\nStudents exploring AI space\n\nCOURSE AGENDA: \n-> Difference between ML\, DL and Data Science \n-> Description & Applications of LLMs\, NLP\, Computer Vision\, BayesianAI \n-> Introduction of the following concepts: \n\nSupervised learning vs Unsupervised learning\nLearning and logic regression\nK-means clustering\nDecision Tree\nBoosting and bagging algorithm\nTime series modeling\nKernel SVM\nNaive Bayes\nRandom forest classifiers\n\n-> Existing applications of ML + Opportunities \nKey Takeaways: \n🤖 Live Q&A and Case Discussions (with monthly invitation to a Q&A with the alumni network) \n🤖 Coursework\, handouts\, & Case Challenges \n🤖 Certificate of completion upon request \nP.S More Algorythm courses coming up on each one of these concepts\, follow for updates. \nReading Appetizers: \n🤖 ALGORYTHM | How Can Sherlock Holmes Use Data Science for his Stellar Detective Work? \n🤖 ALGORYTHM | What is applied AI? A potential to solve world problems or…? \nWelcome aboard\, master of the machines!
URL:https://webgyrlzcode.org/event/algorythm-become-a-machine-learning-ninja-14/
CATEGORIES:Artificial Intelligence,Virtual event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240714T190000
DTEND;TZID=UTC:20240714T220000
DTSTAMP:20260421T134228
CREATED:20240416T135114Z
LAST-MODIFIED:20240416T135114Z
UID:2914-1720983600-1720994400@webgyrlzcode.org
SUMMARY:MINDSHOP™| AI FOR ALL
DESCRIPTION:AI FOR ALL
URL:https://webgyrlzcode.org/event/mindshop-ai-for-all-13/
CATEGORIES:Artificial Intelligence,Tech,Virtual event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240711T190000
DTEND;TZID=UTC:20240711T220000
DTSTAMP:20260421T134228
CREATED:20240416T122143Z
LAST-MODIFIED:20240416T122143Z
UID:2819-1720724400-1720735200@webgyrlzcode.org
SUMMARY:Algorythm™| Become a Machine Learning Ninja
DESCRIPTION:WHAT IS THIS ALGORYTHM COURSE ABOUT? \nThis course provides a robust foundation on machine learning concepts and applications. This course is designed for students who have little to no technical background\, yet are committed to venture into the AI space. \nWHO IS THIS COURSE FOR? \n\n(Non-tech) Entrepreneurs who want to build AI startups\nCareer switchers from non-tech background\nStudents exploring AI space\n\nCOURSE AGENDA: \n-> Difference between ML\, DL and Data Science \n-> Description & Applications of LLMs\, NLP\, Computer Vision\, BayesianAI \n-> Introduction of the following concepts: \n\nSupervised learning vs Unsupervised learning\nLearning and logic regression\nK-means clustering\nDecision Tree\nBoosting and bagging algorithm\nTime series modeling\nKernel SVM\nNaive Bayes\nRandom forest classifiers\n\n-> Existing applications of ML + Opportunities \nKey Takeaways: \n🤖 Live Q&A and Case Discussions (with monthly invitation to a Q&A with the alumni network) \n🤖 Coursework\, handouts\, & Case Challenges \n🤖 Certificate of completion upon request \nP.S More Algorythm courses coming up on each one of these concepts\, follow for updates. \nReading Appetizers: \n🤖 ALGORYTHM | How Can Sherlock Holmes Use Data Science for his Stellar Detective Work? \n🤖 ALGORYTHM | What is applied AI? A potential to solve world problems or…? \nWelcome aboard\, master of the machines!
URL:https://webgyrlzcode.org/event/algorythm-become-a-machine-learning-ninja-13/
CATEGORIES:Artificial Intelligence,Virtual event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240707T190000
DTEND;TZID=UTC:20240707T220000
DTSTAMP:20260421T134228
CREATED:20240416T135013Z
LAST-MODIFIED:20240416T135013Z
UID:2912-1720378800-1720389600@webgyrlzcode.org
SUMMARY:MINDSHOP™| AI FOR ALL
DESCRIPTION:AI FOR ALL
URL:https://webgyrlzcode.org/event/mindshop-ai-for-all-12/
CATEGORIES:Artificial Intelligence,Tech,Virtual event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240704T190000
DTEND;TZID=UTC:20240704T220000
DTSTAMP:20260421T134228
CREATED:20240416T122105Z
LAST-MODIFIED:20240416T122105Z
UID:2817-1720119600-1720130400@webgyrlzcode.org
SUMMARY:Algorythm™| Become a Machine Learning Ninja
DESCRIPTION:WHAT IS THIS ALGORYTHM COURSE ABOUT? \nThis course provides a robust foundation on machine learning concepts and applications. This course is designed for students who have little to no technical background\, yet are committed to venture into the AI space. \nWHO IS THIS COURSE FOR? \n\n(Non-tech) Entrepreneurs who want to build AI startups\nCareer switchers from non-tech background\nStudents exploring AI space\n\nCOURSE AGENDA: \n-> Difference between ML\, DL and Data Science \n-> Description & Applications of LLMs\, NLP\, Computer Vision\, BayesianAI \n-> Introduction of the following concepts: \n\nSupervised learning vs Unsupervised learning\nLearning and logic regression\nK-means clustering\nDecision Tree\nBoosting and bagging algorithm\nTime series modeling\nKernel SVM\nNaive Bayes\nRandom forest classifiers\n\n-> Existing applications of ML + Opportunities \nKey Takeaways: \n🤖 Live Q&A and Case Discussions (with monthly invitation to a Q&A with the alumni network) \n🤖 Coursework\, handouts\, & Case Challenges \n🤖 Certificate of completion upon request \nP.S More Algorythm courses coming up on each one of these concepts\, follow for updates. \nReading Appetizers: \n🤖 ALGORYTHM | How Can Sherlock Holmes Use Data Science for his Stellar Detective Work? \n🤖 ALGORYTHM | What is applied AI? A potential to solve world problems or…? \nWelcome aboard\, master of the machines!
URL:https://webgyrlzcode.org/event/algorythm-become-a-machine-learning-ninja-12/
CATEGORIES:Artificial Intelligence,Virtual event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240630T190000
DTEND;TZID=UTC:20240630T220000
DTSTAMP:20260421T134228
CREATED:20240416T134920Z
LAST-MODIFIED:20240416T134920Z
UID:2910-1719774000-1719784800@webgyrlzcode.org
SUMMARY:MINDSHOP™| AI FOR ALL
DESCRIPTION:AI FOR ALL
URL:https://webgyrlzcode.org/event/mindshop-ai-for-all-11/
CATEGORIES:Artificial Intelligence,Tech,Virtual event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240627T190000
DTEND;TZID=UTC:20240627T220000
DTSTAMP:20260421T134228
CREATED:20240416T122020Z
LAST-MODIFIED:20240416T122020Z
UID:2815-1719514800-1719525600@webgyrlzcode.org
SUMMARY:Algorythm™| Become a Machine Learning Ninja
DESCRIPTION:WHAT IS THIS ALGORYTHM COURSE ABOUT? \nThis course provides a robust foundation on machine learning concepts and applications. This course is designed for students who have little to no technical background\, yet are committed to venture into the AI space. \nWHO IS THIS COURSE FOR? \n\n(Non-tech) Entrepreneurs who want to build AI startups\nCareer switchers from non-tech background\nStudents exploring AI space\n\nCOURSE AGENDA: \n-> Difference between ML\, DL and Data Science \n-> Description & Applications of LLMs\, NLP\, Computer Vision\, BayesianAI \n-> Introduction of the following concepts: \n\nSupervised learning vs Unsupervised learning\nLearning and logic regression\nK-means clustering\nDecision Tree\nBoosting and bagging algorithm\nTime series modeling\nKernel SVM\nNaive Bayes\nRandom forest classifiers\n\n-> Existing applications of ML + Opportunities \nKey Takeaways: \n🤖 Live Q&A and Case Discussions (with monthly invitation to a Q&A with the alumni network) \n🤖 Coursework\, handouts\, & Case Challenges \n🤖 Certificate of completion upon request \nP.S More Algorythm courses coming up on each one of these concepts\, follow for updates. \nReading Appetizers: \n🤖 ALGORYTHM | How Can Sherlock Holmes Use Data Science for his Stellar Detective Work? \n🤖 ALGORYTHM | What is applied AI? A potential to solve world problems or…? \nWelcome aboard\, master of the machines!
URL:https://webgyrlzcode.org/event/algorythm-become-a-machine-learning-ninja-11/
CATEGORIES:Artificial Intelligence,Virtual event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240623T190000
DTEND;TZID=UTC:20240623T220000
DTSTAMP:20260421T134228
CREATED:20240416T134826Z
LAST-MODIFIED:20240416T134826Z
UID:2908-1719169200-1719180000@webgyrlzcode.org
SUMMARY:MINDSHOP™| AI FOR ALL
DESCRIPTION:AI FOR ALL
URL:https://webgyrlzcode.org/event/mindshop-ai-for-all-10/
CATEGORIES:Artificial Intelligence,Tech,Virtual event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240620T190000
DTEND;TZID=UTC:20240620T220000
DTSTAMP:20260421T134228
CREATED:20240416T121931Z
LAST-MODIFIED:20240416T121931Z
UID:2813-1718910000-1718920800@webgyrlzcode.org
SUMMARY:Algorythm™| Become a Machine Learning Ninja
DESCRIPTION:WHAT IS THIS ALGORYTHM COURSE ABOUT? \nThis course provides a robust foundation on machine learning concepts and applications. This course is designed for students who have little to no technical background\, yet are committed to venture into the AI space. \nWHO IS THIS COURSE FOR? \n\n(Non-tech) Entrepreneurs who want to build AI startups\nCareer switchers from non-tech background\nStudents exploring AI space\n\nCOURSE AGENDA: \n-> Difference between ML\, DL and Data Science \n-> Description & Applications of LLMs\, NLP\, Computer Vision\, BayesianAI \n-> Introduction of the following concepts: \n\nSupervised learning vs Unsupervised learning\nLearning and logic regression\nK-means clustering\nDecision Tree\nBoosting and bagging algorithm\nTime series modeling\nKernel SVM\nNaive Bayes\nRandom forest classifiers\n\n-> Existing applications of ML + Opportunities \nKey Takeaways: \n🤖 Live Q&A and Case Discussions (with monthly invitation to a Q&A with the alumni network) \n🤖 Coursework\, handouts\, & Case Challenges \n🤖 Certificate of completion upon request \nP.S More Algorythm courses coming up on each one of these concepts\, follow for updates. \nReading Appetizers: \n🤖 ALGORYTHM | How Can Sherlock Holmes Use Data Science for his Stellar Detective Work? \n🤖 ALGORYTHM | What is applied AI? A potential to solve world problems or…? \nWelcome aboard\, master of the machines!
URL:https://webgyrlzcode.org/event/algorythm-become-a-machine-learning-ninja-10/
CATEGORIES:Artificial Intelligence,Virtual event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240616T190000
DTEND;TZID=UTC:20240616T220000
DTSTAMP:20260421T134228
CREATED:20240416T134728Z
LAST-MODIFIED:20240416T134728Z
UID:2906-1718564400-1718575200@webgyrlzcode.org
SUMMARY:MINDSHOP™| AI FOR ALL
DESCRIPTION:AI FOR ALL
URL:https://webgyrlzcode.org/event/mindshop-ai-for-all-9/
CATEGORIES:Artificial Intelligence,Tech,Virtual event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240613T190000
DTEND;TZID=UTC:20240613T220000
DTSTAMP:20260421T134228
CREATED:20240416T121821Z
LAST-MODIFIED:20240416T121821Z
UID:2811-1718305200-1718316000@webgyrlzcode.org
SUMMARY:Algorythm™| Become a Machine Learning Ninja
DESCRIPTION:WHAT IS THIS ALGORYTHM COURSE ABOUT? \nThis course provides a robust foundation on machine learning concepts and applications. This course is designed for students who have little to no technical background\, yet are committed to venture into the AI space. \nWHO IS THIS COURSE FOR? \n\n(Non-tech) Entrepreneurs who want to build AI startups\nCareer switchers from non-tech background\nStudents exploring AI space\n\nCOURSE AGENDA: \n-> Difference between ML\, DL and Data Science \n-> Description & Applications of LLMs\, NLP\, Computer Vision\, BayesianAI \n-> Introduction of the following concepts: \n\nSupervised learning vs Unsupervised learning\nLearning and logic regression\nK-means clustering\nDecision Tree\nBoosting and bagging algorithm\nTime series modeling\nKernel SVM\nNaive Bayes\nRandom forest classifiers\n\n-> Existing applications of ML + Opportunities \nKey Takeaways: \n🤖 Live Q&A and Case Discussions (with monthly invitation to a Q&A with the alumni network) \n🤖 Coursework\, handouts\, & Case Challenges \n🤖 Certificate of completion upon request \nP.S More Algorythm courses coming up on each one of these concepts\, follow for updates. \nReading Appetizers: \n🤖 ALGORYTHM | How Can Sherlock Holmes Use Data Science for his Stellar Detective Work? \n🤖 ALGORYTHM | What is applied AI? A potential to solve world problems or…? \nWelcome aboard\, master of the machines!
URL:https://webgyrlzcode.org/event/algorythm-become-a-machine-learning-ninja-9/
CATEGORIES:Artificial Intelligence,Virtual event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240609T190000
DTEND;TZID=UTC:20240609T220000
DTSTAMP:20260421T134228
CREATED:20240416T134603Z
LAST-MODIFIED:20240416T134603Z
UID:2904-1717959600-1717970400@webgyrlzcode.org
SUMMARY:MINDSHOP™| AI FOR ALL
DESCRIPTION:AI FOR ALL
URL:https://webgyrlzcode.org/event/mindshop-ai-for-all-8/
CATEGORIES:Artificial Intelligence,Tech,Virtual event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240606T190000
DTEND;TZID=UTC:20240606T220000
DTSTAMP:20260421T134228
CREATED:20240416T121725Z
LAST-MODIFIED:20240416T121725Z
UID:2809-1717700400-1717711200@webgyrlzcode.org
SUMMARY:Algorythm™| Become a Machine Learning Ninja
DESCRIPTION:WHAT IS THIS ALGORYTHM COURSE ABOUT? \nThis course provides a robust foundation on machine learning concepts and applications. This course is designed for students who have little to no technical background\, yet are committed to venture into the AI space. \nWHO IS THIS COURSE FOR? \n\n(Non-tech) Entrepreneurs who want to build AI startups\nCareer switchers from non-tech background\nStudents exploring AI space\n\nCOURSE AGENDA: \n-> Difference between ML\, DL and Data Science \n-> Description & Applications of LLMs\, NLP\, Computer Vision\, BayesianAI \n-> Introduction of the following concepts: \n\nSupervised learning vs Unsupervised learning\nLearning and logic regression\nK-means clustering\nDecision Tree\nBoosting and bagging algorithm\nTime series modeling\nKernel SVM\nNaive Bayes\nRandom forest classifiers\n\n-> Existing applications of ML + Opportunities \nKey Takeaways: \n🤖 Live Q&A and Case Discussions (with monthly invitation to a Q&A with the alumni network) \n🤖 Coursework\, handouts\, & Case Challenges \n🤖 Certificate of completion upon request \nP.S More Algorythm courses coming up on each one of these concepts\, follow for updates. \nReading Appetizers: \n🤖 ALGORYTHM | How Can Sherlock Holmes Use Data Science for his Stellar Detective Work? \n🤖 ALGORYTHM | What is applied AI? A potential to solve world problems or…? \nWelcome aboard\, master of the machines!
URL:https://webgyrlzcode.org/event/algorythm-become-a-machine-learning-ninja-8/
CATEGORIES:Artificial Intelligence,Virtual event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240602T190000
DTEND;TZID=UTC:20240602T220000
DTSTAMP:20260421T134228
CREATED:20240416T134523Z
LAST-MODIFIED:20240416T134523Z
UID:2902-1717354800-1717365600@webgyrlzcode.org
SUMMARY:MINDSHOP™| AI FOR ALL
DESCRIPTION:AI FOR ALL
URL:https://webgyrlzcode.org/event/mindshop-ai-for-all-7/
CATEGORIES:Artificial Intelligence,Tech,Virtual event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240530T190000
DTEND;TZID=UTC:20240530T220000
DTSTAMP:20260421T134228
CREATED:20240416T121544Z
LAST-MODIFIED:20240416T121544Z
UID:2807-1717095600-1717106400@webgyrlzcode.org
SUMMARY:Algorythm™| Become a Machine Learning Ninja
DESCRIPTION:WHAT IS THIS ALGORYTHM COURSE ABOUT? \nThis course provides a robust foundation on machine learning concepts and applications. This course is designed for students who have little to no technical background\, yet are committed to venture into the AI space. \nWHO IS THIS COURSE FOR? \n\n(Non-tech) Entrepreneurs who want to build AI startups\nCareer switchers from non-tech background\nStudents exploring AI space\n\nCOURSE AGENDA: \n-> Difference between ML\, DL and Data Science \n-> Description & Applications of LLMs\, NLP\, Computer Vision\, BayesianAI \n-> Introduction of the following concepts: \n\nSupervised learning vs Unsupervised learning\nLearning and logic regression\nK-means clustering\nDecision Tree\nBoosting and bagging algorithm\nTime series modeling\nKernel SVM\nNaive Bayes\nRandom forest classifiers\n\n-> Existing applications of ML + Opportunities \nKey Takeaways: \n🤖 Live Q&A and Case Discussions (with monthly invitation to a Q&A with the alumni network) \n🤖 Coursework\, handouts\, & Case Challenges \n🤖 Certificate of completion upon request \nP.S More Algorythm courses coming up on each one of these concepts\, follow for updates. \nReading Appetizers: \n🤖 ALGORYTHM | How Can Sherlock Holmes Use Data Science for his Stellar Detective Work? \n🤖 ALGORYTHM | What is applied AI? A potential to solve world problems or…? \nWelcome aboard\, master of the machines!
URL:https://webgyrlzcode.org/event/algorythm-become-a-machine-learning-ninja-7/
CATEGORIES:Artificial Intelligence,Virtual event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240526T190000
DTEND;TZID=UTC:20240526T220000
DTSTAMP:20260421T134228
CREATED:20240416T134322Z
LAST-MODIFIED:20240416T134322Z
UID:2900-1716750000-1716760800@webgyrlzcode.org
SUMMARY:MINDSHOP™| AI FOR ALL
DESCRIPTION:AI FOR ALL
URL:https://webgyrlzcode.org/event/mindshop-ai-for-all-6/
CATEGORIES:Artificial Intelligence,Tech,Virtual event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240524T200000
DTEND;TZID=UTC:20240524T213000
DTSTAMP:20260421T134228
CREATED:20240416T124110Z
LAST-MODIFIED:20240416T124110Z
UID:2839-1716580800-1716586200@webgyrlzcode.org
SUMMARY:May 24: How to Use AI to Learn to Code\, Delivered by Erik Gross
DESCRIPTION:Artificial intelligence is quickly taking over all of our lives. Like any tool\, it can be used for construction or destruction – good or evil. \nOn May 24 (Friday evening) the Co-Founder of The Tech Academy\, Erik Gross\, will teach students how to leverage AI in order to write and debug effective code. This live online free class will start at 5:00 p.m. PST (7 CST / 8 EST). \nLearn about useful tools that will help you on your journey as a technology professional! \nRegister your attendance for this powerful and useful workshop now!
URL:https://webgyrlzcode.org/event/may-24-how-to-use-ai-to-learn-to-code-delivered-by-erik-gross/
CATEGORIES:Artificial Intelligence,Coding,Tech,Virtual event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240523T190000
DTEND;TZID=UTC:20240523T220000
DTSTAMP:20260421T134228
CREATED:20240416T121451Z
LAST-MODIFIED:20240416T121451Z
UID:2805-1716490800-1716501600@webgyrlzcode.org
SUMMARY:Algorythm™| Become a Machine Learning Ninja
DESCRIPTION:WHAT IS THIS ALGORYTHM COURSE ABOUT? \nThis course provides a robust foundation on machine learning concepts and applications. This course is designed for students who have little to no technical background\, yet are committed to venture into the AI space. \nWHO IS THIS COURSE FOR? \n\n(Non-tech) Entrepreneurs who want to build AI startups\nCareer switchers from non-tech background\nStudents exploring AI space\n\nCOURSE AGENDA: \n-> Difference between ML\, DL and Data Science \n-> Description & Applications of LLMs\, NLP\, Computer Vision\, BayesianAI \n-> Introduction of the following concepts: \n\nSupervised learning vs Unsupervised learning\nLearning and logic regression\nK-means clustering\nDecision Tree\nBoosting and bagging algorithm\nTime series modeling\nKernel SVM\nNaive Bayes\nRandom forest classifiers\n\n-> Existing applications of ML + Opportunities \nKey Takeaways: \n🤖 Live Q&A and Case Discussions (with monthly invitation to a Q&A with the alumni network) \n🤖 Coursework\, handouts\, & Case Challenges \n🤖 Certificate of completion upon request \nP.S More Algorythm courses coming up on each one of these concepts\, follow for updates. \nReading Appetizers: \n🤖 ALGORYTHM | How Can Sherlock Holmes Use Data Science for his Stellar Detective Work? \n🤖 ALGORYTHM | What is applied AI? A potential to solve world problems or…? \nWelcome aboard\, master of the machines!
URL:https://webgyrlzcode.org/event/algorythm-become-a-machine-learning-ninja-6/
CATEGORIES:Artificial Intelligence,Virtual event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240519T190000
DTEND;TZID=UTC:20240519T220000
DTSTAMP:20260421T134228
CREATED:20240416T134112Z
LAST-MODIFIED:20240416T134112Z
UID:2898-1716145200-1716156000@webgyrlzcode.org
SUMMARY:MINDSHOP™| AI FOR ALL
DESCRIPTION:AI FOR ALL
URL:https://webgyrlzcode.org/event/mindshop-ai-for-all-5/
CATEGORIES:Artificial Intelligence,Tech,Virtual event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240516T190000
DTEND;TZID=UTC:20240516T220000
DTSTAMP:20260421T134228
CREATED:20240416T121337Z
LAST-MODIFIED:20240416T121337Z
UID:2803-1715886000-1715896800@webgyrlzcode.org
SUMMARY:Algorythm™| Become a Machine Learning Ninja
DESCRIPTION:WHAT IS THIS ALGORYTHM COURSE ABOUT? \nThis course provides a robust foundation on machine learning concepts and applications. This course is designed for students who have little to no technical background\, yet are committed to venture into the AI space. \nWHO IS THIS COURSE FOR? \n\n(Non-tech) Entrepreneurs who want to build AI startups\nCareer switchers from non-tech background\nStudents exploring AI space\n\nCOURSE AGENDA: \n-> Difference between ML\, DL and Data Science \n-> Description & Applications of LLMs\, NLP\, Computer Vision\, BayesianAI \n-> Introduction of the following concepts: \n\nSupervised learning vs Unsupervised learning\nLearning and logic regression\nK-means clustering\nDecision Tree\nBoosting and bagging algorithm\nTime series modeling\nKernel SVM\nNaive Bayes\nRandom forest classifiers\n\n-> Existing applications of ML + Opportunities \nKey Takeaways: \n🤖 Live Q&A and Case Discussions (with monthly invitation to a Q&A with the alumni network) \n🤖 Coursework\, handouts\, & Case Challenges \n🤖 Certificate of completion upon request \nP.S More Algorythm courses coming up on each one of these concepts\, follow for updates. \nReading Appetizers: \n🤖 ALGORYTHM | How Can Sherlock Holmes Use Data Science for his Stellar Detective Work? \n🤖 ALGORYTHM | What is applied AI? A potential to solve world problems or…? \nWelcome aboard\, master of the machines!
URL:https://webgyrlzcode.org/event/algorythm-become-a-machine-learning-ninja-5/
CATEGORIES:Artificial Intelligence,Virtual event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240512T190000
DTEND;TZID=UTC:20240512T220000
DTSTAMP:20260421T134228
CREATED:20240416T134012Z
LAST-MODIFIED:20240416T134012Z
UID:2896-1715540400-1715551200@webgyrlzcode.org
SUMMARY:MINDSHOP™| AI FOR ALL
DESCRIPTION:AI FOR ALL
URL:https://webgyrlzcode.org/event/mindshop-ai-for-all-4/
CATEGORIES:Artificial Intelligence,Tech,Virtual event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240509T190000
DTEND;TZID=UTC:20240509T220000
DTSTAMP:20260421T134228
CREATED:20240416T121238Z
LAST-MODIFIED:20240416T121238Z
UID:2801-1715281200-1715292000@webgyrlzcode.org
SUMMARY:Algorythm™| Become a Machine Learning Ninja
DESCRIPTION:WHAT IS THIS ALGORYTHM COURSE ABOUT? \nThis course provides a robust foundation on machine learning concepts and applications. This course is designed for students who have little to no technical background\, yet are committed to venture into the AI space. \nWHO IS THIS COURSE FOR? \n\n(Non-tech) Entrepreneurs who want to build AI startups\nCareer switchers from non-tech background\nStudents exploring AI space\n\nCOURSE AGENDA: \n-> Difference between ML\, DL and Data Science \n-> Description & Applications of LLMs\, NLP\, Computer Vision\, BayesianAI \n-> Introduction of the following concepts: \n\nSupervised learning vs Unsupervised learning\nLearning and logic regression\nK-means clustering\nDecision Tree\nBoosting and bagging algorithm\nTime series modeling\nKernel SVM\nNaive Bayes\nRandom forest classifiers\n\n-> Existing applications of ML + Opportunities \nKey Takeaways: \n🤖 Live Q&A and Case Discussions (with monthly invitation to a Q&A with the alumni network) \n🤖 Coursework\, handouts\, & Case Challenges \n🤖 Certificate of completion upon request \nP.S More Algorythm courses coming up on each one of these concepts\, follow for updates. \nReading Appetizers: \n🤖 ALGORYTHM | How Can Sherlock Holmes Use Data Science for his Stellar Detective Work? \n🤖 ALGORYTHM | What is applied AI? A potential to solve world problems or…? \nWelcome aboard\, master of the machines!
URL:https://webgyrlzcode.org/event/algorythm-become-a-machine-learning-ninja-4/
CATEGORIES:Artificial Intelligence,Virtual event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240505T190000
DTEND;TZID=UTC:20240505T220000
DTSTAMP:20260421T134228
CREATED:20240416T133909Z
LAST-MODIFIED:20240416T133909Z
UID:2894-1714935600-1714946400@webgyrlzcode.org
SUMMARY:MINDSHOP™| AI FOR ALL
DESCRIPTION:AI FOR ALL
URL:https://webgyrlzcode.org/event/mindshop-ai-for-all-3/
CATEGORIES:Artificial Intelligence,Tech,Virtual event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240502T190000
DTEND;TZID=UTC:20240502T220000
DTSTAMP:20260421T134228
CREATED:20240416T121029Z
LAST-MODIFIED:20240416T121029Z
UID:2799-1714676400-1714687200@webgyrlzcode.org
SUMMARY:Algorythm™| Become a Machine Learning Ninja
DESCRIPTION:WHAT IS THIS ALGORYTHM COURSE ABOUT? \nThis course provides a robust foundation on machine learning concepts and applications. This course is designed for students who have little to no technical background\, yet are committed to venture into the AI space. \nWHO IS THIS COURSE FOR? \n\n(Non-tech) Entrepreneurs who want to build AI startups\nCareer switchers from non-tech background\nStudents exploring AI space\n\nCOURSE AGENDA: \n-> Difference between ML\, DL and Data Science \n-> Description & Applications of LLMs\, NLP\, Computer Vision\, BayesianAI \n-> Introduction of the following concepts: \n\nSupervised learning vs Unsupervised learning\nLearning and logic regression\nK-means clustering\nDecision Tree\nBoosting and bagging algorithm\nTime series modeling\nKernel SVM\nNaive Bayes\nRandom forest classifiers\n\n-> Existing applications of ML + Opportunities \nKey Takeaways: \n🤖 Live Q&A and Case Discussions (with monthly invitation to a Q&A with the alumni network) \n🤖 Coursework\, handouts\, & Case Challenges \n🤖 Certificate of completion upon request \nP.S More Algorythm courses coming up on each one of these concepts\, follow for updates. \nReading Appetizers: \n🤖 ALGORYTHM | How Can Sherlock Holmes Use Data Science for his Stellar Detective Work? \n🤖 ALGORYTHM | What is applied AI? A potential to solve world problems or…? \nWelcome aboard\, master of the machines!
URL:https://webgyrlzcode.org/event/algorythm-become-a-machine-learning-ninja-3/
CATEGORIES:Artificial Intelligence,Virtual event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240428T190000
DTEND;TZID=UTC:20240428T220000
DTSTAMP:20260421T134228
CREATED:20240416T133804Z
LAST-MODIFIED:20240416T133804Z
UID:2892-1714330800-1714341600@webgyrlzcode.org
SUMMARY:MINDSHOP™| AI FOR ALL
DESCRIPTION:AI FOR ALL
URL:https://webgyrlzcode.org/event/mindshop-ai-for-all-2/
CATEGORIES:Artificial Intelligence,Tech,Virtual event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240425T190000
DTEND;TZID=UTC:20240425T220000
DTSTAMP:20260421T134228
CREATED:20240416T120944Z
LAST-MODIFIED:20240416T120944Z
UID:2797-1714071600-1714082400@webgyrlzcode.org
SUMMARY:Algorythm™| Become a Machine Learning Ninja
DESCRIPTION:WHAT IS THIS ALGORYTHM COURSE ABOUT? \nThis course provides a robust foundation on machine learning concepts and applications. This course is designed for students who have little to no technical background\, yet are committed to venture into the AI space. \nWHO IS THIS COURSE FOR? \n\n(Non-tech) Entrepreneurs who want to build AI startups\nCareer switchers from non-tech background\nStudents exploring AI space\n\nCOURSE AGENDA: \n-> Difference between ML\, DL and Data Science \n-> Description & Applications of LLMs\, NLP\, Computer Vision\, BayesianAI \n-> Introduction of the following concepts: \n\nSupervised learning vs Unsupervised learning\nLearning and logic regression\nK-means clustering\nDecision Tree\nBoosting and bagging algorithm\nTime series modeling\nKernel SVM\nNaive Bayes\nRandom forest classifiers\n\n-> Existing applications of ML + Opportunities \nKey Takeaways: \n🤖 Live Q&A and Case Discussions (with monthly invitation to a Q&A with the alumni network) \n🤖 Coursework\, handouts\, & Case Challenges \n🤖 Certificate of completion upon request \nP.S More Algorythm courses coming up on each one of these concepts\, follow for updates. \nReading Appetizers: \n🤖 ALGORYTHM | How Can Sherlock Holmes Use Data Science for his Stellar Detective Work? \n🤖 ALGORYTHM | What is applied AI? A potential to solve world problems or…? \nWelcome aboard\, master of the machines!
URL:https://webgyrlzcode.org/event/algorythm-become-a-machine-learning-ninja-2/
CATEGORIES:Artificial Intelligence,Virtual event
END:VEVENT
END:VCALENDAR