From 8ecc07884e5b958f0308b969c83df6839f2e9325 Mon Sep 17 00:00:00 2001 From: Paras-96 Date: Tue, 21 May 2024 17:19:53 +0530 Subject: [PATCH] Rectified Grammar, Spacing Issue, added new resources link 1. Grammar and Spacing Corrections: - Fixed various grammar mistakes throughout the repository. - Addressed spacing inconsistencies for improved readability. 2. New Learning Resources: - Machine Learning Course: Added the Machine Learning Course to the Courses section, providing a structured learning path with real-world examples. --- README.md | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 63ab6ff..e52ccc1 100644 --- a/README.md +++ b/README.md @@ -139,9 +139,9 @@ Contributions most welcome. * [Artificial Intelligence: A New Synthesis](http://www.amazon.com/Artificial-Intelligence-Synthesis-Nils-Nilsson/dp/1558604677) - Beginning with elementary reactive agents, Nilsson gradually increases their cognitive horsepower to illustrate the most important and lasting ideas in AI * [On Intelligence](http://www.amazon.com/Jeff-Hawkins/e/B001KHNZ7C/ref=sr_ntt_srch_lnk_11?qid=1435480927&sr=8-11) - Hawkins develops a powerful theory of how the human brain works, explaining why computers are not intelligent and how, based on this new theory, we can finally build intelligent machines. Also audio version available from audible.com * [How To Create A Mind](http://www.amazon.com/How-Create-Mind-Thought-Revealed/dp/0143124048/ref=pd_sim_14_3?ie=UTF8&refRID=0QY72H7NGRYH79R7S3K7) - Kurzweil discusses how the brain works, how the mind emerges, brain-computer interfaces, and the implications of vastly increasing the powers of our intelligence to address the world’s problems -* [Deep Learning](http://www.deeplearningbook.org/) - Goodfellow, Bengio and Courville's introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. +* [Deep Learning](http://www.deeplearningbook.org/) - Goodfellow, Bengio, and Courville's introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. * [The Elements of Statistical Learning: Data Mining, Inference, and Prediction](https://web.stanford.edu/~hastie/ElemStatLearn/) - Hastie and Tibshirani cover a broad range of topics, from supervised learning (prediction) to unsupervised learning including neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. -* [Deep Learning and the Game of Go](https://www.manning.com/books/deep-learning-and-the-game-of-go) - Deep Learning and the Game of Go teaches you how to apply the power of deep learning to complex human-flavored reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game. +* [Deep Learning and the Game of Go](https://www.manning.com/books/deep-learning-and-the-game-of-go) - Deep Learning and the Game of Go teaches you how to apply the power of deep learning to complex human-flavored reasoning tasks by building a Go-playing AI. After exposing you to the foundations of the machine and deep learning, you'll use Python to create a bot and then teach it the game's rules. * [Deep Learning for Search](https://www.manning.com/books/deep-learning-for-search) - Deep Learning for Search teaches you how to leverage neural networks, NLP, and deep learning techniques to improve search performance. * [Deep Learning with PyTorch](https://www.manning.com/books/deep-learning-with-pytorch) - PyTorch puts these superpowers in your hands, providing a comfortable Python experience that gets you started quickly and then grows with you as you—and your deep learning skills—become more sophisticated. Deep Learning with PyTorch will make that journey engaging and fun. * [Deep Reinforcement Learning in Action](https://www.manning.com/books/deep-reinforcement-learning-in-action) - Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects. @@ -152,17 +152,18 @@ Contributions most welcome. * [Succeeding with AI](https://www.manning.com/books/succeeding-with-ai) - An introduction to managing successful AI projects and applying AI to real-life situations. * [Elements of AI (Part 1) - Reaktor/University of Helsinki](https://www.elementsofai.com/) - An Introduction to AI is a free online course for everyone interested in learning what AI is, what is possible (and not possible) with AI, and how it affects our lives – with no complicated math or programming required. * [Essential Natural Language Processing](https://www.manning.com/books/essential-natural-language-processing) - A hands-on guide to NLP with practical techniques, numerous Python-based examples and real-world case studies. -* [Kaggle's micro courses](https://www.kaggle.com/learn/overview) - A series of micro courses by offering practical and hands-on knowledge ranging from Python to Deep Learning. +* [Kaggle's micro courses](https://www.kaggle.com/learn/overview) - A series of micro-courses by offering practical and hands-on knowledge ranging from Python to Deep Learning. * [Transfer Learning for Natural Language Processing](https://www.manning.com/books/transfer-learning-for-natural-language-processing?utm_source=github&utm_medium=organic&utm_campaign=book_azunre_transfer_3_10_20) - A book that gets you up to speed with the relevant ML concepts and then dives into transfer learning for NLP. * (Stanford Deep Learning Series][https://www.youtube.com/playlist?list=PLoROMvodv4rOABXSygHTsbvUz4G_YQhOb] -* [Amazon Machine Learning Developer Guide](https://docs.aws.amazon.com/machine-learning/latest/dg/machinelearning-dg.pdf) - A book for ML developers which itroduces the ML concepts & strategies with lots of practical usages. +* [Amazon Machine Learning Developer Guide](https://docs.aws.amazon.com/machine-learning/latest/dg/machinelearning-dg.pdf) - A book for ML developers that introduces the ML concepts & strategies with lots of practical usages. * [Machine Learning for Humans](https://medium.com/machine-learning-for-humans/why-machine-learning-matters-6164faf1df12) - A series of simple, plain-English explanations accompanied by math, code, and real-world examples. +* [Machine Learning Course](https://www.scaler.com/courses/machine-learning-course-training/) - This course includes essential machine learning topics like Supervised Learning, Unsupervised Learning, Reinforcement Learning, and real-world project examples. ## Books * [Machine Learning for Mortals (Mere and Otherwise)](https://www.manning.com/books/machine-learning-for-mortals-mere-and-otherwise) - Early access book that provides basics of machine learning and using R programming language. -* [How Machine Learning Works](https://livebook.manning.com/book/how-machine-learning-works/welcome/v-5) - Mostafa Samir. Early access book that introduces machine learning from both practical and theoretical aspects in a non-threating way. -* [MachineLearningWithTensorFlow2ed](https://www.manning.com/books/machine-learning-with-tensorflow-second-edition) - a book on general purpose machine learning techniques regression, classification, unsupervised clustering, reinforcement learning, auto encoders, convolutional neural networks, RNNs, LSTMs, using TensorFlow 1.14.1. +* [How Machine Learning Works](https://livebook.manning.com/book/how-machine-learning-works/welcome/v-5) - Mostafa Samir. Early access book that introduces machine learning from both practical and theoretical aspects in a non-threatening way. +* [MachineLearningWithTensorFlow2ed](https://www.manning.com/books/machine-learning-with-tensorflow-second-edition) - a book on general-purpose machine learning techniques regression, classification, unsupervised clustering, reinforcement learning, auto encoders, convolutional neural networks, RNNs, LSTMs, using TensorFlow 1.14.1. * [Serverless Machine Learning](https://www.manning.com/books/serverless-machine-learning-in-action) - a book for machine learning engineers on how to train and deploy machine learning systems on public clouds like AWS, Azure, and GCP, using a code-oriented approach. * [The Hundred-Page Machine Learning Book](http://themlbook.com/) - all you need to know about Machine Learning in a hundred pages, supervised and unsupervised learning, SVM, neural networks, ensemble methods, gradient descent, cluster analysis and dimensionality reduction, autoencoders and transfer learning, feature engineering and hyperparameter tuning. * [Trust in Machine Learning](https://www.manning.com/books/trust-in-machine-learning) - a book for experienced data scientists and machine learning engineers on how to make your AI a trustworthy partner. Build machine learning systems that are explainable, robust, transparent, and optimized for fairness. @@ -191,8 +192,8 @@ Contributions most welcome. * [Computers and Thought: A practical Introduction to Artificial Intelligence](http://www.cs.bham.ac.uk/research/projects/poplog/computers-and-thought/) - The book covers computer simulation of human activities, such as problem solving and natural language understanding; computer vision; AI tools and techniques; an introduction to AI programming; symbolic and neural network models of cognition; the nature of mind and intelligence; and the social implications of AI and cognitive science. * [Society of Mind](http://aurellem.org/society-of-mind/index.html) - Marvin Minsky's seminal work on how our mind works. Lot of Symbolic AI concepts have been derived from this basis. * [Artificial Intelligence and Molecular Biology](https://web.archive.org/web/20060627060706/http://www.biosino.org/mirror/www.aaai.org/Press/Books/Hunter/hunter-contents.html) - The current volume is an effort to bridge that range of exploration, from nucleotide to abstract concept, in contemporary AI/MB research. -* [Brief Introduction To Educational Implications Of Artificial Intelligence](http://pages.uoregon.edu/moursund/Books/AIBook/index.htm) - This book is designed to help preservice and inservice teachers learn about some of the educational implications of current uses of Artificial Intelligence as an aid to solving problems and accomplishing tasks. -* [Encyclopedia: Computational intelligence](http://www.scholarpedia.org/article/Encyclopedia:Computational_intelligence) - Scholarpedia is a peer-reviewed open-access encyclopedia written and maintained by scholarly experts from around the world. +* [Brief Introduction To Educational Implications Of Artificial Intelligence](http://pages.uoregon.edu/moursund/Books/AIBook/index.htm) - This book is designed to help preservice and in-service teachers learn about some of the educational implications of current uses of Artificial Intelligence as an aid to solving problems and accomplishing tasks. +* [Encyclopedia: Computational intelligence](http://www.scholarpedia.org/article/Encyclopedia:Computational_intelligence) - Scholarpedia is a peer-reviewed open-access encyclopedia written and maintained by scholarly experts worldwide. * [Ethical Artificial Intelligence](http://arxiv.org/abs/1411.1373) - a book by Bill Hibbard that combines several peer reviewed papers and new material to analyze the issues of ethical artificial intelligence. * [Golden Artificial Intelligence](https://golden.com/wiki/Cluster%3A_Artificial_intelligence) - a cluster of pages on artificial intelligence and machine learning. * [R2D3](http://www.r2d3.us/) - A website with explanations on topics from Machine Learning to Statistics. All helped with beautiful animated infographics and real life examples. Available in various languages.