Resume Screening | TFIDFVectorizer | KNearestNeighbour | Python | Machine Learning | NLP tutorial

AI can rapidly process huge volumes of data, which makes it a most valuable tool for high-volume recruitment processes. Intelligent resume screening improves the quality of hire because it reduces human errors, unconscious bias and can make predictions. Please find the complete playlist for quantum computing https://www.youtube.com/watch?v=qMT_WoV9Yxg&list=PLdpmGL_ZpofW-4BpFZcoL4XWvxHx2TCon Please find the complete playlist for NLP ( Natural language Processing) https://www.youtube.com/watch?v=obQ_28gdoOg&list=PLdpmGL_ZpofX9za3QxI7VMRCODd6bqmpU Please find the complete playlist for speech recognition https://www.youtube.com/watch?v=D3JyZFkoJEE&list=PLdpmGL_ZpofVjHY4h8fv9etcGE7Z7H_hV Please find the complete playlist for deep learning below https://www.youtube.com/watch?v=IWoAEYNbQ-c&list=PLdpmGL_ZpofU-wdGcsUwdByrTCs9iNS_S please findthe complete playlist for backpropagation algorith below https://www.youtube.com/watch?v=TnG9JScUxCg&list=PLdpmGL_ZpofVNbMdrbVnAzOCZJdSpQleW please find the complete playlist for Gradient Descent algorithm below https://www.youtube.com/watch?v=S1d94WIg2yo&list=PLdpmGL_ZpofV_As-yjZ4IWx_CLDubHLjJ Please find the complete playlist for math below https://www.youtube.com/watch?v=rnQf-CVusE4&list=PLdpmGL_ZpofWBceEapT6Go6CgNGMjMu8Z Please find the complete playlist for statistics below https://www.youtube.com/watch?v=SwhPrVOcOzg&list=PLdpmGL_ZpofX52Txk3C09L_DtJQ0HqDbw Please find the complete playlist for supervised machine learning https://www.youtube.com/watch?v=6WyWz6ghrtU&list=PLdpmGL_ZpofWOVrXb5SVsXzNN2Wj2xh8a

Resume Screening | TFIDFVectorizer | KNearestNeighbour | Python | Machine Learning | NLP tutorial

AI can rapidly process huge volumes of data, which makes it a most valuable tool for high-volume recruitment processes. Intelligent resume screening improves the quality of hire because it reduces human errors, unconscious bias and can make predictions. Please find the complete playlist for quantum computing https://www.youtube.com/watch?v=qMT_WoV9Yxg&list=PLdpmGL_ZpofW-4BpFZcoL4XWvxHx2TCon Please find the complete playlist for NLP ( Natural language Processing) https://www.youtube.com/watch?v=obQ_28gdoOg&list=PLdpmGL_ZpofX9za3QxI7VMRCODd6bqmpU Please find the complete playlist for speech recognition https://www.youtube.com/watch?v=D3JyZFkoJEE&list=PLdpmGL_ZpofVjHY4h8fv9etcGE7Z7H_hV Please find the complete playlist for deep learning below https://www.youtube.com/watch?v=IWoAEYNbQ-c&list=PLdpmGL_ZpofU-wdGcsUwdByrTCs9iNS_S please findthe complete playlist for backpropagation algorith below https://www.youtube.com/watch?v=TnG9JScUxCg&list=PLdpmGL_ZpofVNbMdrbVnAzOCZJdSpQleW please find the complete playlist for Gradient Descent algorithm below https://www.youtube.com/watch?v=S1d94WIg2yo&list=PLdpmGL_ZpofV_As-yjZ4IWx_CLDubHLjJ Please find the complete playlist for math below https://www.youtube.com/watch?v=rnQf-CVusE4&list=PLdpmGL_ZpofWBceEapT6Go6CgNGMjMu8Z Please find the complete playlist for statistics below https://www.youtube.com/watch?v=SwhPrVOcOzg&list=PLdpmGL_ZpofX52Txk3C09L_DtJQ0HqDbw Please find the complete playlist for supervised machine learning https://www.youtube.com/watch?v=6WyWz6ghrtU&list=PLdpmGL_ZpofWOVrXb5SVsXzNN2Wj2xh8a

Technical Topic Tuesday -59- Machine Learning #machinelearning #datascience #technology #ai #ml

​@Passion, People & Purpose #machinelearning #datascience #technology #ai #ml Machine Learning Purpose: to help enhance not only many industrial, enterprise & professional processes but also our everyday living What? a subset of artificial intelligence focused on building systems that can learn from historical data, identify patterns, & make logical decisions with little to no human intervention How? 1. Collecting and preparing the Data 2. Choosing a Model 3. Training the Model 4. Evaluating the Model 5. Parameter Tuning 6. Making Predictions Let me know when is comes to build a ML model what do you see the major challenge? or any other steps would you like add to achieve more accuracy? Happy Learning and Sharing! Until we meet, happy leading, and let's lead together. Stay safe. Bye for now. Source - https://www.google.com/ #SoLeadSaturday Podcast/Show https://www.netapp.com/artificial-intelligence/what-is-machine-learning/ https://www.sas.com/en_us/insights/analytics/machine-learning.html https://quantilus.com/why-is-machine-learning-important-and-how-will-it-impact-business/ https://www.simplilearn.com/tutorials/machine-learning-tutorial/machine-learning-steps https://developers.google.com/machine-learning/guides/rules-of-ml https://neptune.ai/blog/improving-machine-learning-deep-learning-models Find me on - YoutTube - https://bit.ly/3dA0Qko #SoLeadSaturday Community Website - https://vaishalilambe.club/ Twitter - https://bit.ly/3Id2AMx LinkedIn - https://bit.ly/3tBe4Ft Instagram - @PassionPeoplePurpose Tiktok - @PassionPeoplePurpose Website - https://bit.ly/3GETAiS Facebook - https://bit.ly/3ry025c Apple Podcasts - https://apple.co/3KikjEi Google Podcasts - https://bit.ly/3fwzX0q Anchor - https://bit.ly/3rrij3R Spotify - https://spoti.fi/3Fw1tG4 Breaker - https://bit.ly/3Ib6ZQf Overcast - https://bit.ly/33GATNe Pocket casts - https://bit.ly/3tuNmyu Radio Public - https://bit.ly/33sfHLc Castbox - https://bit.ly/3KdUpl7 Video Editor - Vaishali Lambe Thumbnail Designer - Vaishali Lambe

Deep Learning Indepth Tutorials In 5 Hours With Krish Naik

Please get all the materials and pdfs in the below link which is for free. https://courses.ineuron.ai/Deep-Learning-Community-Class Join our amazing Programs Currently in iNeuron there are 3 main program that are going on. Full Stack Data science program with job guaranteed which started from May 6th(Price : 15k+ gst)-Lifetime Access https://courses.ineuron.ai/Full-Stack-Data-Science-Bootcamp Full Stack Data Analytics with placement assistance starting from June 18th(Price: 4000rs inr including gst)- Lifetime Access https://courses.ineuron.ai/Full-Stack-Data-Analytics Tech Neuron with 210+ courses (price: 7080 including gst for 2 years subscription) https://courses.ineuron.ai/neurons/Tech-Neuron From my side you can avail additional 10% discount by using coupon code Krish10 Or Sudhanshu10. Don't miss this opportunity grab it before it is too late. Happy Learning!! Connect with me here: Twitter: https://twitter.com/Krishnaik06 Facebook: https://www.facebook.com/krishnaik06 instagram: https://www.instagram.com/krishnaik06

DeepMind’s New AI Thinks It Is A Genius! 🤖

❤️ Check out Lambda here and sign up for their GPU Cloud: https://ift.tt/08gdxK2 📝 The paper "DeepMind Gopher - Scaling Language Models: Methods, Analysis & Insights from Training Gopher" is available here: https://ift.tt/38zMbnl https://ift.tt/fB7UIry ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://ift.tt/jKhG0Ey - https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Balfanz, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, B Shang, Christian Ahlin, Eric Martel, Geronimo Moralez, Gordon Child, Ivo Galic, Jace O'Brien, Jack Lukic, Javier Bustamante, John Le, Jonas, Jonathan, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Michael Albrecht, Michael Tedder, Nevin Spoljaric, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Rajarshi Nigam, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Ted Johnson, Thomas Krcmar, Timothy Sum Hon Mun, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/jKhG0Ey Thumbnail background design: Felícia Zsolnai-Fehér - http://felicia.hu Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/pu0EWqd Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/WArbnvg

Deep Learning Indepth Tutorials In 5 Hours With Krish Naik

Please get all the materials and pdfs in the below link which is for free. https://courses.ineuron.ai/Deep-Learning-Community-Class Join our amazing Programs Currently in iNeuron there are 3 main program that are going on. Full Stack Data science program with job guaranteed which started from May 6th(Price : 15k+ gst)-Lifetime Access https://courses.ineuron.ai/Full-Stack-Data-Science-Bootcamp Full Stack Data Analytics with placement assistance starting from June 18th(Price: 4000rs inr including gst)- Lifetime Access https://courses.ineuron.ai/Full-Stack-Data-Analytics Tech Neuron with 210+ courses (price: 7080 including gst for 2 years subscription) https://courses.ineuron.ai/neurons/Tech-Neuron From my side you can avail additional 10% discount by using coupon code Krish10 Or Sudhanshu10. Don't miss this opportunity grab it before it is too late. Happy Learning!! Connect with me here: Twitter: https://twitter.com/Krishnaik06 Facebook: https://www.facebook.com/krishnaik06 instagram: https://www.instagram.com/krishnaik06

machine learning about your future #short #easy

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machine learning about your future #short #easy

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Author Interview: SayCan - Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

#saycan #robots #ai This is an interview with the authors Brian Ichter, Karol Hausman, and Fei Xia. Original Paper Review Video: https://youtu.be/Ru23eWAQ6_E Large Language Models are excellent at generating plausible plans in response to real-world problems, but without interacting with the environment, they have no abilities to estimate which of these plans are feasible or appropriate. SayCan combines the semantic capabilities of language models with a bank of low-level skills, which are available to the agent as individual policies to execute. SayCan automatically finds the best policy to execute by considering a trade-off between the policy's ability to progress towards the goal, given by the language model, and the policy's probability of executing successfully, given by the respective value function. The result is a system that can generate and execute long-horizon action sequences in the real world to fulfil complex tasks. OUTLINE: 0:00 - Introduction & Setup 3:40 - Acquiring atomic low-level skills 7:45 - How does the language model come in? 11:45 - Why are you scoring instead of generating? 15:20 - How do you deal with ambiguity in language? 20:00 - The whole system is modular 22:15 - Going over the full algorithm 23:20 - What if an action fails? 24:30 - Debunking a marketing video :) 27:25 - Experimental Results 32:50 - The insane scale of data collection 40:15 - How do you go about large-scale projects? 43:20 - Where did things go wrong? 45:15 - Where do we go from here? 52:00 - What is the largest unsolved problem in this? 53:35 - Thoughts on the Tesla Bot 55:00 - Final thoughts Paper: https://ift.tt/HGetosQ Website: https://ift.tt/RIrf48J Abstract: Large language models can encode a wealth of semantic knowledge about the world. Such knowledge could be extremely useful to robots aiming to act upon high-level, temporally extended instructions expressed in natural language. However, a significant weakness of language models is that they lack real-world experience, which makes it difficult to leverage them for decision making within a given embodiment. For example, asking a language model to describe how to clean a spill might result in a reasonable narrative, but it may not be applicable to a particular agent, such as a robot, that needs to perform this task in a particular environment. We propose to provide real-world grounding by means of pretrained skills, which are used to constrain the model to propose natural language actions that are both feasible and contextually appropriate. The robot can act as the language model's "hands and eyes," while the language model supplies high-level semantic knowledge about the task. We show how low-level skills can be combined with large language models so that the language model provides high-level knowledge about the procedures for performing complex and temporally-extended instructions, while value functions associated with these skills provide the grounding necessary to connect this knowledge to a particular physical environment. We evaluate our method on a number of real-world robotic tasks, where we show the need for real-world grounding and that this approach is capable of completing long-horizon, abstract, natural language instructions on a mobile manipulator. The project's website and the video can be found at this https URL Authors: Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Daniel Ho, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Eric Jang, Rosario Jauregui Ruano, Kyle Jeffrey, Sally Jesmonth, Nikhil J Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Kuang-Huei Lee, Sergey Levine, Yao Lu, Linda Luu, Carolina Parada, Peter Pastor, Jornell Quiambao, Kanishka Rao, Jarek Rettinghouse, Diego Reyes, Pierre Sermanet, Nicolas Sievers, Clayton Tan, Alexander Toshev, Vincent Vanhoucke, Fei Xia, Ted Xiao, Peng Xu, Sichun Xu, Mengyuan Yan Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/Fh9K5RX BitChute: https://ift.tt/dlhPLgz LinkedIn: https://ift.tt/XdSrB6I BiliBili: https://ift.tt/ypvC0JH If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://ift.tt/Ta7RGEF Patreon: https://ift.tt/xMLVEZI Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

This New AI is Photoshop For Your Hair! 🧔

❤️ Check out Weights & Biases and sign up for a free demo here: https://ift.tt/O5MvGSN ❤️ Their mentioned post is available here (thank you Soumik Rakshit!): https://ift.tt/w7MhNcz 📝 The paper "Barbershop: GAN-based Image Compositing using Segmentation Masks" is available here: https://ift.tt/15kzR6I https://ift.tt/B1KhVYw ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://ift.tt/Oe7h1Ns - https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Balfanz, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, B Shang, Christian Ahlin, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Jack Lukic, Javier Bustamante, John Le, Jonas, Jonathan, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Michael Albrecht, Michael Tedder, Nevin Spoljaric, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Paul F, Rajarshi Nigam, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Ted Johnson, Thomas Krcmar, Timothy Sum Hon Mun, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/Oe7h1Ns Thumbnail background design: Felícia Zsolnai-Fehér - http://felicia.hu Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/cESyZqL Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/tKro8kZ