Participants of Bertelsmann Technology Scholarship created an awesome list of resources and they want to share it with the world, if you find illegal resources please report to us and we will remove it.
Big Thanks to everyone from Bertelsmann Technology Scholarship participants for their contribution to this list of awesome resources
| Topic | Description |
|---|---|
| Advances in AI | Newsletter with brief description of things happening around AI / ML world, often provides links to working code repositories exemplifying particular technique / phenomenon. |
| Research Papers on AI | Multiple Research Papers on AI by Lex Friedman |
| Programming | Cheatsheets of all programming language |
| Deep Learning (an MIT Press Book) | The Deep Learning book is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. |
| Deep Learning Wizard | You will the learn the suite of tools to build an end-to-end deep learning pipeline. |
| AI Expert Roadmap | Aset of charts demonstrating the paths that you can take and the technologies that you would want to adopt in order to become a data scientist, machine learning or an AI expert. |
| Projects that matter Work that matters Data Science for Social Good. | Solve for Good is a platform for social good organizations to post data projects they need help with, for volunteers to help scope those projects into well-defined problems, and to help solve those problems. |
| DEEP LEARNING | This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning. |
| Dive into Deep Learning | Interactive deep learning book with code, math, and discussions. Implemented with NumPy/MXNet, PyTorch, and TensorFlow |
| Mathematics for Computer Science. | This course covers elementary discrete mathematics for computer science and engineering. |
| Mathematics for Machine Learning. | We wrote a book on Mathematics for Machine Learning that motivates people to learn mathematical concepts. we aim to provide the necessary mathematical skills to read those other books. |
| Grokking Deep Learning. | The book teaches you to build deep learning neural networks from scratch. |
| Pandas Cheat Sheet. | Data Wrangling with pandas Cheat Sheet. |
| NumPy and Pandas Tutorial – Data Analysis with Python | In this post, we will provide an overview of the common functionalities of NumPy and Pandas. Topic covered in the blog are: Overview of NumPy, Overview of Pandas, and Using Matplotlib. |
| Data Analysis with Python (Numpy, Pandas, Matplotlib, Seaborn). | In this tutorial you'll learn the whole process of Data Analysis: reading data from multiple sources (CSVs, SQL, Excel, etc), processing them using NumPy and Pandas, visualize them using Matplotlib and Seaborn and clean and process it to create reports. |
| Introduction to Python Programming. | In this course, you'll learn the fundamentals of the Python programming language, along with programming best practices. |
| NumPy Basic: Exercises, Practice, Solution. | NumPy Basic [41 exercises with solution]. |
| A Beginners Guide to AI Product Management. | In 2017 I shipped my first Artificial Intelligence (AI) product. Here are 13 AI Product Management basics I learned during that time. |
| fast.ai Making neural nets uncool again. | Free online courses include: Practical Deep Learning for Coders, Deep Learning from the Foundations, Practical Data Ethics, Computational Linear Algebra, and Code-First Introduction to Natural Language Processing |
| Essential Guide to AI Product Management. | As a practicing APM and organizer of a successful AI Meetup, I wanted to share useful resources, best practices and tips that I came across and learned from my experience. |
| Data Science vs. Artificial Intelligence vs. Machine Learning vs. Deep Learning. | It’s very common these days to come across these terms - data science, artificial intelligence, machine learning, deep learning, neural networks, and much more. But what do these buzzwords actually mean? And why should you care about one or the other? |
| Machine Learning by Andrew Ng TOP INSTRUCTOR | In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. |
| DEEPLIZARD is Building Collective Intelligence by offering deep learning courses. | Deeplizard's deep learning road map include: Machine Learning & Deep Learning Fundamentals, Keras - Python Deep Learning Neural Network API, Neural Network Programming - Deep Learning with PyTorch |
| Learn leading-edge technologies Blockchain, Data Science, AI and more. | Congnitive Class Courses are all FREE & all that you need to invest is your time! They also have virtual lab environment that enables users to practice what you learn. |
| Scaling down Deep Learning | Constructing the MNIST-1D dataset. As with the original MNIST dataset, the task is to learn to classify the digits 0-9. |
| How To Learn Machine Learning For Free. | A recommendation about best free resources for learning machine learning from MIT open course, coursera and others. |
| Deep learning chapter 1: what is a Neural Network? | A detailed video about artificial neural networks with mentioning neurons and other components. |
| State-of-the-art research papers about deep learning. | The latest papers in machine learning and its, algorithms. |
| Machine Learning & Deep Learning Fundamentals | Course on Deep Learning Concepts |
| With DeepMind AI could be one of humanity’s most useful inventions. | We’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence. |
| A Short Practical Introduction to Machine Learning: Predicting Survival on the Titanic! | In this article, I will show you how I my first submission to Kaggle. You can also code-along with me if you’d like. |
| Elements of AI free online course! | The Elements of AI is a series of free online courses created by Reaktor and the University of Helsinki. We want to encourage as broad a group of people as possible to learn what AI is, what can (and can’t) be done with AI |
| Artificial Intelligence | The resource I'm sharing is a great podcast made by Lex Fridman. He shares a lot of knowledge about AI Research and Industry. |
| The Batch Weekly Newsletter | The Batch presents the most important AI events and perspective in a curated, easy-to-read report for engineers and business leaders. |
| Machine Learning Engineering book written by Andriy Burkov. | If you intend to use machine learning to solve business problems at scale, I'm delighted you got your hands on this book. |
| The Hundred-Page Machine Learning Book by Andriy Burkov. | This book provides a great practical guide to get started and execute on ML within a few days without necessarily knowing much about ML. |
| Learn Python, Data Viz, Pandas & More Tutorials by Kaggle | We pare down complex topics to their key practical components, so you gain usable skills in a few hours (instead of weeks or months). |
| A visual introduction to machine learning | Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco. |
| Face it. Your Project Requirements are Poorly Written! podcast | A podcast about project requirements which is quite useful in preparing a good business case/ project requirements. |
| "Machine Learning" youtube playlist by StatQuest with Josh Starmer | Machine Learning covers a lot of topics and this can be intimidating. However, there is no reason to fear, this play list will help you trough it all, one step at a time. |
| "CS50's Introduction to Artificial Intelligence with Python 2020" by CS50 | This course explores the concepts and algorithms at the foundation of modern artificial intelligence. |
| ML Cheatsheet | A brief summary about ML |
| MIT 6.S191 (2018): Deep Learning playlist by Alexander Amini | MIT 6.S191 (2018): 11 lectures: start by introduction and end by Computer Vision Meets Social Networks |
| Full Stack Deep Learning | Full Stack Deep Learning helps you bridge the gap from training machine learning models to deploying AI systems in the real world. |
| Deep learning (Convolution neural network) with google street view | Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States |
| Deep learning, machine learning, python, algorithms and pytorch | Four free live online courses. Build real-world projects. Earn certifications. Interact with a global community. |
| Python | This course is an introduction to both fundamental python programming concepts and the Python programming language. |
| Artificial Intelligence for Business Leaders | It’s aimed primarily at business leaders new to AI, and its focus is on understanding what AI is and means in a business context. |
| Deep learning, and transfer learning | What is transfer learning? Exploring the popular deep learning approach. |
| Deep learning, and machine learning topics | A Beginner’s Guide to Important Topics in AI, Machine Learning, and Deep Learning. |
| GPT-3, and natural language processing. | Creative writing by OpenAI’s GPT-3 model, demonstrating poetry, dialogue, puns, literary parodies, and storytelling. |
| Artificial Intelligence, free content, Stanford | Deep learning, natural language processing, convolution neural neywork |
| Data science | Acquire Data Science skills through application-oriented courses. |
| SCUM | Learn how to scrum with the best of them. |
| Artificial intelligence. | Artificial Intelligence Full Course |
| Artificial intelligence, tutorials. | Artificial Intelligence Tutorial |
| Neural networks | This book will teach you many of the core concepts behind neural networks and deep learning. |
| Numpy Tutorial | Python Numpy Tutorial (with Jupyter and Colab) |
| NumPy for Matlab users | NumPy for Matlab users |
| The Fundamental Concepts of PyTorch | This repository introduces the fundamental concepts of PyTorch through self-contained examples. |
| MNIST-1D Dataset | Scaling down Deep Learning |
| Convolution Neural Networks | Stanford CS231n: Convolutional Neural Networks for Visual Recognition |
| AI Podcast | Most Research in Deep Learning is a Total Waste of Time - Jeremy Howard |
| Machine Learning, Deep Learning, Python, R, free course | A course covers Regression, Decision Trees, SVM, Neural Networks, CNN, Time Series Forecasting and more using both Python & R |
| Artificial Neural Networks (ANN), Keras, Python, R | Understand Deep Learning and build Neural Networks using TensorFlow 2.0 and Keras in Python and R |
| Neural Networks | Neural networks class - Université de Sherbrooke |
| Machine learning, pytorch, python, data analysis | A collection of courses include: quantum computing, pandas, matplotlib, python, game development, etc..... |
| Awesome Artificial Intelligence (AI) | A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers. |
| The machine learning glossary | The main concepts and buzzwords, Machine learning applications, The training data, The core algorithms: machine learning methods |
| Machine Learning, Roadmap 2020 | Getting into machine learning is quite the adventure. And as any adventurer knows, sometimes it can be helpful to have a compass to figure out if you're heading in the right direction. |
| Machine learning, crash course, google | A self-study guide for aspiring machine learning practitioners. |
| Reinforcement Learning 101 | Learn the essentials of Reinforcement Learning! |
| Artificial neural networks | Everything you need to know about Neural Networks |
| Data Analysis with Python: Zero to Pandas | Participate in live online courses. Build real-world projects. Earn certifications. Interact with a global community. |
| Machine learning | Topic covered in this channel are on Machine Learning, Artificial Intelligence, Data Engineering |
| Machine Learrning | Machine Learning for Everyone. In simple words. With real-world examples. Yes, again. |
| Product Managers, AI, Machine Learning | Why Software Product Managers Need to Understand AI and Machine Learning |
| Reinforcement Learning | This series is all about reinforcement learning (RL)! Here, we’ll gain an understanding of the intuition, the math, and the coding involved with RL. |
| Accuracy, Precision, Recall, and F1 Score | Explain the most important metrics in simple terms & using simple examples. |
| Secure and Private AI by Udacity | Learn how to extend PyTorch with the tools necessary to train AI models that preserve user privacy. |
| Statistics, Classification, Prediction | It is important to distinguish prediction and classification. |
| Statistics, Classification Accuracy | I discuss a particular problem related to classification, namely the harm done by using improper accuracy scoring rules. |
| Machine learning | YouTube playlist explain K-nearest- neighbor and python. |
| Data sscience | Explaining Data Science to a Non-Data Scientist |
| Machine learning | Machine learning roadmap 2020 |
| Machine Learning | A curated list of awesome machine learning frameworks, libraries and software (by language). |
| Machine learning operations (MLops) | An awesome list of references for MLOps - Machine Learning Operations |
| Machine learning, Deep learning | Free online courses for non-engineering and engineering students. |
| Machine learning | Make inferences and recommendations using data, train a computer, and consider ethical implications of machine learning. |
| Machine learning | Machine Learning with Python: Zero to GBMs |
| Machine learning | Stephen tells us about conversations he has had with people who have been working in the world of machine learning for some time. |
| Data science | Data Science Tutorial Library - 370+ Free Tutorials. |
| Deploy for targeted Use Cases (Gartner) | This is related to gartner source present in our Slides Section |
| Machine learning, data science, Mathematics | YouTube playlist with a series on data science and machine learning. |
| Deploy for Targeted Use Cases (gartner) | Discusses about 5G Tech in the Industry |
| Business Problem before Data (PWC) | This is the PWC Article on Addressing Business Problem before Data |
| Business Problem Before Data (PWC) | This is another article that explains about addressing the business problem before Data |
| Success depends on Data (IBM THINK) | This is the article leading to our lesson 2 slides in the source section. |
| Success depends on Data (PWC) | This is a great article that explains about Business Process Management, Data Management and maintenance. Success depends on data |
| Amazon web services (AWS). | Complete AWS Tutorial for beginners who want to learn AWS from scratch with examples and Hands-on. |
| Python | This Python 3 tutorial course aims to teach everyone the basics of programming computers using Python. |
| Data Science | Learn Data Science is this full tutorial course for absolute beginners. |
| AI | An open-data and free platform built by the research community to facilitate the collaborative development of AI |
| Neural Networks And Deeplearning | This is a web book written by Michael Nielsen. It gives an in depth explanation of how neural networks work. It even goes into great depths on some of the mathematical aspects of deeplearning like gradient descent, normalization, regularization... etc. The book also shows you how to implement neural networks from scratch using python and train them on the MNIST dataset |
| (Udacity) Lesson 2.3 Using AI and ML in Business | This is notes from Udacity |
| Machine Learning Mastery | Jason Brownlee is a really helpful guy always ready to help out with any questions on ML. He has a series of books and free resources on the web. Focus on teaching developers to move into Data Science and ML: Machine Learning Mastery. He also has an email course; he sends a little ML project via email every week. Very cool. |
| Graphic design platform | Canva is a graphic design platform (infographics, documents and other visual content). The app already includes templates for users to use. |
| Python, Java, Kotlin | This is a learning platform with many hands-on projects with step-by-step implementations, various examples and exercises. I found this to be very valuable to me and i hope that other students will find it the same way :) Happy coding everyone!!! + Accessing it by the link below will give students a 3 months free trial : ) |
| Machine learning, deep learning, mathematics | 3Blue1Brown is one of the best YouTube channels which explain machine learning, deep learning, mathematics, etc... |
| Data analysis | Intro to Data Analysis is free course by Udacity. |
| Statistics | OpenIntro Statistics is a complete foundation for Statistics, also serving as a foundation for Data Science. |
| Machine learning, AI | Machine learning & AI Hands on 3 Projects. Get well verse with Machine learning and AI by working on hands-on projects. |
| Machine learning, deep learning, artificial neural networks | A. W. Moore courses on statistical data mining, probability, the foundations of statistical data analysis, machine learning and data mining algorithms. |
| Python | Python full Course Python Quizzes, Python Projects in Games, Data Analysis & Python Scripting for all Python 3 Developers |
| Product manager | Digital Product Management: Modern Fundamentals |
| Product Design | Learn product validation, UI/UX practices, Google’s Design Sprint and the process for setting and tracking actionable metrics. |
| Python | Automate the Boring Stuff with Python |
| Harvard CSS50 artificial intelligence course | Learn to use machine learning in Python in this introductory course on artificial intelligence. |
| AI Books | 18 pdf books around AI and Machine Learning mainly |
| Introduction into AI and ML | Notes on the Introduction to AI and ML Course |
| Statistics; Math | A small compendium of books centered in maths and statistics. |
| Text Classification using Python and different libraries and AI Business Solutions articles | Text Classification using Python tutorials plus AI Business Solutions articles by Kavita Ganesan, Ph.D founder of Opinosis Analytics. |
| Deep Learning Cheat Sheet | Brief description of common concepts found in Deep Learning. |
| Backpropagation (ML) | Article explaining the concept of Backpropagation by Andrej Karpathy. |
| Visualization | Experiment tracking, hyperparameter optimization, model and dataset versioning |
| (Udacity) Lesson 3.1 Data Fit And Annotation | This notes is from Udacity |
| Machine Learning Crash Course | A self-study guide for aspiring machine learning practitioners. |
| Scrum | Learn how to scrum with the best of ‘em |
| Machine Learning | MIT 6.036 |
| A/B Testing, free course, udacity | Online Experiment Design and Analysis. |
| Machine Learning, unsupervised Learning | This is the second course in the 3-course Machine Learning Series and is offered at Georgia Tech as CS7641. |
| machine learning, deep learning, NLP | Kavita’s Articles about text processing, natural language processing, etc... |
| Machine learning, AI, deep learning | Latest Headlines on AI, Machine Learning, Deep Learning, Robotics |
| AI, Natural Language Processing with Neural Networks | IN GERMAN LANGUAGE: >8hrs Interview Podacst with Richard Socher providing introduction and motivation in natural language processing (NLP) using neural networks. |
| AI Engineer | Career Insights: What does an AI Engineer do? |
| Data Science, Python Certification | Start your career as Data Scientist from scratch. Learn Data Science with Python. Predict trends with advanced analytics |
| Machine learning, python | Deep dive into Machine Learning with Python Programming. Implement practical scenarios & a project on Recommender System. |
| Machine learning algorithms | Bitcoins analysis using different machine learning algorithms. |
| Python | Udacity guide to map, filter and reduce functions in python. |
| Python | How to Work with Python Dictionaries. |
| Python, keras, pandas | An end-to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku. |
| Data Science, Machine Learning | The purpose of this book is to provide an accessible, textbook intended for students interested in gaining a better understanding of the mathematics |
| Machine learning, R | Learn to create Machine Learning Algorithms with R & Excels from popular Data Science experts. Code templates included. |
| Machine Learning Projects | Practice your skills in Data Science Projects with Python, by learning and then trying all these hands-on, interactive projects. |
| Machine Learning | Learn to create Machine Learning Algorithms in Python Data Science enthusiasts. Code templates included. |
| Data Visualization | Data Visualization in the real world. A little old but does make a great point for effective, efficient and concise visualization. |
| Convolution neural network | What is a Convolutional Neural Network? |
| Python | Python Bootcamp 2020 Build 15 working Applications and Games. |
| Tensorflow | CS 20: Tensorflow for Deep Learning Research |
| Data Science | This lecture series will cover several introductory concepts in data science and machine learning. |
| Free AI-powered coding assistant tool | Kite is a tool for code autocomplete, for Less Keystrokes and for Coding Faster. Kite is a free AI-powered coding assistant that will help you code faster and smarter. The Kite plugin integrates with all the top editors and IDEs to give you smart completions and documentation while you’re typing. |
| Run Jupyter Notebook in the cloud | Six easy ways to run your Jupyter Notebook in the cloud |
| AI Track Notes | It contains my notes on the lesson and related article and videos collected from the slack channels. Also I have included FAQ section for the queries. |
| Datasets for Machine Learning and Data Science | Best open-access datasets for machine learning, data science, sentiment analysis, computer vision, natural language processing (NLP), clinical data, and others. |
| Rasa Chatbot | How to create a Chatbot, using Rasa? To know the Answer, follow the Link.. |
| Lessons and notebooks on machine learning and applied machine learning in production. | How to responsibly develop, deploy and maintain applications that are made with machine learning. |
| Machine Learning Zero to Hero (Google I/O'19) | Image categorization example with Tensorflow and Keras |
| Deep Learning A-Z | Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Templates included. |
| Script of lessons | I wrote everything of the lesson down in one script, so people can read the ressources on the go :) this is the link to the 2nd lesson |
| Script of Lesson 3 | I wrote everthing of Lesson 3 down in a script for people to read on the go. I already postet it in some channels, (because I didn't notice this great opportunity), but for lesson 4 and 5 I will directly upload it here :) |
| A Course on ML Production Systems | Course details from @Google |
| Ethics of AI - online course | The Ethics of AI is a free online course created by the University of Helsinki |
| AI in healthcare | Usecases and Examples in Healthcare in 2020 |
| How to take a smart notes | 10 Principles to better organize your note-taking and writing |
| Julia | Source for learning all things Julia for free |
| Become a Data Scientist | A Complete 52 Week Curriculum to Become a Data Scientist in 2021 |
| Big Data | Explore practical, ethical — and spectacularly visual — ways to understand near-infinite data. |
| AI fairness and bias | Fairness and bias in artificial intelligence |
| AI Podcast | MIT Technology Review // In Machine we trust |
| AI Podcast | Exponential View. How AI and other exponential technologies are transforming business and society. |
| Python NLTK (2009) | Natural Language Processing with Python – Analyzing Text with the Natural Language Toolkit Steven Bird, Ewan Klein, and Edward Loper |
| Elements of AI (course for free) | Welcome to the Elements of AI free online course! |
| Linear Algebra | Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares |
| Statistics | Jeremy Balka's statistics channel, containing some introductory statistics videos. |
| codecademy | Learn to code for free |
| Machine Learning Podcast | Machine Learning from scratch |
| Allen Institute for AI Podcast | Natural Language Processing Highlights |
| Machine Learning, deep Learning, Reinforcement Learning | I found this website very useful for learning machine learning, Deep learning, with all 3 frameworks: Keras, PyTorch, TensorFlow. |
| Machine Learning | Course for Beginners to learn ML from Scratch and Projects are explained |
| Python | A smarter way to learn python |
| Machine Learning and NLP | Lectures from Stanford University Professors |
| Deep Learning with PyTorch | It is about the foundations of deep learning with PyTorch |
| Reinforcement Learning | You should take this course if you have an interest in machine learning and the desire to engage with it from a theoretical perspective. |
| The Hundred-Page Machine Learning Book | All you need to know about Machine Learning in a hundred pages |
| Coursera Mathematics for Machine Learning Specialization | Learn about the prerequisite mathematics for applications in data science and machine learning. |
| First Steps in Linear Algebra for Machine Learning | The main goal of the course is to explain the main concepts of linear algebra that are used in data analysis and machine learning. |
| Coursera Machine Learning for All | This course is designed to introduce you to Machine Learning without needing any programming. That means that this course doesn't cover the programming based machine learning tools like python and TensorFlow. |
| Coursera Machine Learning | This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. |
| The UX of AI | Using Google Clips to understand how a human-centered design process elevates artificial intelligence |
| The impact of ai on project management | Dr Greg Usher explores how artificial intelligence (AI) is shaping the world of project management. |
| Ai in project management | From theory to AI Project Management in practice |
| Visualization of Data Structures and Algorithms | Awesome visualization of Data Structures and Algorithms |
| Artificial Intelligence Expert in 2020 | Roadmap to becoming an Artificial Intelligence Expert in 2020 |
| These students figured out their tests were graded by AI — and the easy way to cheat | As COVID-19 has driven schools around the US to move teaching to online or hybrid models, many are outsourcing some instruction and grading to virtual education platforms, they use tests graded by AI — and the easy way to cheat |
| Introduction to the TensorFlow Challenges | TensorFlow is an open source framework developed by the Google Brain team aimed to make the powers of machine learning and neural networking easier to use. The following lectures were created by Tim Ruscica, otherwise known as “Tech With Tim” from his educational programming YouTube channel. They will help you to understand TensorFlow and some of its capabilities. |
| Data Science Math Skills | This course is designed to teach learners the basic math you will need in order to be successful in almost any data science math course and was created for learners who have basic math skills but may not have taken algebra or pre-calculus. Data Science Math Skills introduces the core math that data science is built upon, with no extra complexity, introducing unfamiliar ideas and math symbols one-at-a-time. |
| Innovations in Investment Technology: Artificial Intelligence | On this course, you’ll explore how technology has changed the way we invest money. You’ll consider the evolution of AI-driven online wealth management platforms, robo-advisors, and learn how they work and why they’re successful. |
| 180 Data Science and Machine Learning Projects with Python | An article with links for 180 data science and machine learning projects solved and explained with python. |
| Artificial Intelligence (AI) | Learn the fundamentals of Artificial Intelligence (AI), and apply them. Design intelligent agents to solve real-world problems including, search, games, machine learning, logic, and constraint satisfaction problems. |
| AI - get started | How get started-ai. What should I read? What should I watch? What should I do? |
| Learning AI | Steps to get started learning artificial-intelligence |
| Generative Adversarial Network (GANs) | The GANs Zoo, shows an updated List |
| GANs: Generative Adversarial Networks | This survey shows the latest updates in Generative adversarial network (GANs). |
| Statistics | A visual introduction to probability and statistics. |
| Deep Learning | Deep Learning for Coders with fastai and PyTorch |
| MongoDB | A free tutorial from MongoDB university M320: Data Modeling with certificate very important in Modeling text-based databases for APIs. |
| Pytorch | Pytorch tutorials for deep learning beginners from pytorch.org |
| The AI Podcast | The AI podcast, presented practical applications of ML . Its a great resource for any levels of people interested in ML |
| Confusion Matrix | Make the Confusion Matrix Less Confusing, tips |
| Confusion Matrix | What is Confusion Matrix, structure, how to create code for Confusion Matrix in Python |
| Deep Learning with Python | Learn deep learning with python, this is one of the best resources online. It contains book, notes, codes and video lectures |
| Data Science | Cheatsheets for Data Science |
| Artificial Neural Networks In Linguistic Data Processing | 7 Types Of Artificial Neural Networks In Linguistic Data Processing. What is meant by an artificial neural network? How does it work What are the types of artificial neural networks? |
| Python | Newletter Subscripton Python Weekly |
| Annotated Data for Machine Learning | How to Get Annotated Data for Machine Learning? |
| AI model vs carbon emission | AI model vs carbon emission |
| AI project ideas topics for beginners | Exploring some interesting Artificial Intelligence project ideas which beginners can work on to put their Python knowledge to test. In this article, you will find 16 top Artificial Intelligence project ideas |
| Free must read books-machine learning | Some of the best machine learning books for beginners freely available online (in pdf format) to download |
| Data Science Machine Learning | Machine Learning and Data Science cheat sheets |
| Machine Learning Project Ideas | Machine Learning Project Ideas |
| VIP CheatSheets in ML,DL,AI, data science. | VIP CheatSheets in ML,DL,AI, data science. Well organized, easy-to-digest study guides with visualizations& animations |
| Linear algebra | This in-depth course with 34 lectures by MIT will help you get started with it Down pointing backhand index |
| Artificial Intelligence for Robotics | Class will teach students basic methods in Artificial Intelligence, including probabilistic inference, planning and search, localization, tracking, mapping and control, all with a focus on robotics. |
| Recurrent Neural Networks | A friendly introduction to Recurrent Neural Networks |
| AI, Human Intelligence | "Humans Won't Be Able to Control Artificial Intelligence, Scientists Warn" Some smart robots can perform complex tasks on their own, without the programmers understanding how they learned them. |
| Entire Computer Science Curriculum | Entire Computer Science Curriculum in 1000 YouTube Videos/ Laconicml |
| ML and Data Science Cheat Sheet | Nice tips and notes. |
| Machine Learning | Machine Learning Course |
| Helen Dataset | The resulting dataset consists of 2000 training and 330 test images with highly accurate, detailed, and consistent annotations of the primary facial components. |
| Python - Data Science | Python Data Science Handbook |
| Computer Vision | RWTH AACHEN University - Computer Vision Lecture Notes 1 |
| Computer Vision | RWTH AACHEN University - Computer Vision Lecture Notes 2 |
| Linear Algebra | Linear Algebra in Python |
| Data Science Book Advise | What are the Top 10 Data Science and AI Books of 2020 |
| Data Analytics | Models and algorithms for the analysis of data sets |
| Data Science | Lecture Documentation of Harvard University |
| Data Science | Curriculum - Recommended curriculum for intro-level data science self-study |
| Algorithms | Algorithms Notes for Professionals book |
| C++ | Advanced level C++ Notes |
| SQL | Microsoft SQL Server Notes - Recommend for at least intermediate level. |
| Artificial Intelligence | Curious about A.I. ? Check out YouTube Originals latest episodes from the series of “The Age Of A.I.” which is presented by Robert Downey Jr |
| Python | Linear Regression in Python: Using NumPy, scipy, and statsmodels |
| Python | Python notes for professional developers |
| R | R Language notes for advanced developers |
| AI - Unsupervised Learning | Unsupervised Learning Algorithms in One Picture |
| AI NLP language understanding | Jumbled-up sentences show that AIs still don’t really understand language |
| AI language model | Google trained a trillion parameter ai language model |
| Data Visualization | Data Visualization quick-reference guide. |
| AI-Bias | This is how AI bias really happens—and why it’s so hard to fix |
| AI - biases- how to overcome them | AI - biases- how to overcome them |
| AI for project Mangers | The impact of AI on project management |
| Data Engineering | Roadmap of Data Engineering |
| Data Science | A comparison of pipenv and conda as of Jan 2021 based on various “data science-ish” criteria |
| Data Analysis | COVID-19 Data Analysis with Python |
| Superintelligence (AI/Ethics) | Superintelligence is a hypothetical agent that possesses intelligence far surpassing that of thebrightest and most gifted human minds. In light of recent advances in machine intelligence, a num-ber of scientists, philosophers and technologists have revived the discussion about the potentiallycatastrophic risks entailed by such an entity. In this article, we trace the origins and developmentof the neo-fear of superintelligence, and some of the major proposals for its containment. We arguethat total containment is, in principle, impossible, due to fundamental limits inherent to comput-ing itself. Assuming that a superintelligence will contain a program that includes all the programsthat can be executed by a universal Turing machine on input potentially as complex as the state ofthe world, strict containment requires simulations of such a program, something theoretically (andpractically) impossible. |
| AI Robotics + Theory of mind | Behavior modeling is an essential cognitive ability that underlies many aspects of human and animal social behavior (Watson in Psychol Rev 20:158, 1913), and an ability we would like to endow robots. Most studies of machine behavior modelling, however, rely on symbolic or selected parametric sensory inputs and built-in knowledge relevant to a given task. Here, we propose that an observer can model the behavior of an actor through visual processing alone, without any prior symbolic information and assumptions about relevant inputs. To test this hypothesis, we designed a non-verbal non-symbolic robotic experiment in which an observer must visualize future plans of an actor robot, based only on an image depicting the initial scene of the actor robot. We found that an AI-observer is able to visualize the future plans of the actor with 98.5% success across four different activities, even when the activity is not known a-priori. We hypothesize that such visual behavior modeling is an essential cognitive ability that will allow machines to understand and coordinate with surrounding agents, while sidestepping the notorious symbol grounding problem. Through a false-belief test, we suggest that this approach may be a precursor to Theory of Mind, one of the distinguishing hallmarks of primate social cognition. |
| Natural language predicts viral escape, NLP | Natural language predicts viral escape Viral mutations that evade neutralizing antibodies, an occurrence known as viral escape, can occur and may impede the development of vaccines. To predict which mutations may lead to viral escape, Hie et al. used a machine learning technique for natural language processing with two components: grammar (or syntax) and meaning (or semantics) (see the Perspective by Kim and Przytycka). Three different unsupervised language models were constructed for influenza A hemagglutinin, HIV-1 envelope glycoprotein, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein. Semantic landscapes for these viruses predicted viral escape mutations that produce sequences that are syntactically and/or grammatically correct but effectively different in semantics and thus able to evade the immune system. |
| NLP (Introduction) | Speech and Language Processing |
| Bayesian Reasoning and Machine Learning | Bayesian Reasoning and Machine Learning |
| Python (Java, C, C++, JavaScript, and Ruby) | Tool to Visualize Code Execution for Python, Java, C, C++, JavaScript, and Ruby |
| Grasshoper. Coding App for beginners | Coding App for beginners. For free. |
| Ken Jee's Data Science YouTube Channel | An informative Data Science YouTube channel. It provides insight into the data science community, career advice, beginner tips and project advice. |
| Introduction to Deep Learning from Logical Calculus to Artifical Intelligence | This book gives information about from emerging Artificial Intelligence to the methods of deep learning. |
| AI - robot-empathy-deception | Can AI learn empathy? |
| Interactive Plots only with Matplotlib | Interactive Plots only with Matplotlib! |
| Deep Learning with CIFAR-10 | Deep Learning with CIFAR-10 |
| Data Science | Youtube channel for beginner friendly advice in Data Science. How to go about structuring your learning and good project ideas and insights! |
| Guide to AI in retail | This deals with issues such as Personalization, Predictions, Communications, and the scope of cooperation between Tommy Hilfiger's recent project with IBM and The Fashion Institute of Technology (FIT). |
| AI-retail | In this article, we cover a variety of examples in which AI is being integrated in the retail industry, broken down into the following sub-categories |
| Software Testing | A Study of Automated Software Testing: Automation Tools and Frameworks |
| Amazon Web Services (AWS) | AWS Documentation for Rekognition = ML for image and video |
| Women Who Code Initiative | Women Who Code London Initiative- the largest and most active community of engineers dedicated to inspiring women to excel in technology careers. |
| Apple watch can detect covid-19 | Apple watch can detect covid-19 before symptoms arise new study shows |
| Women who code (network) | Women who code: Empowering Women in Technology |
| Data Science Portfolio | Advantageus advices and explanation on how to create an amazing data science portfolio |
| Inception | NLP- annotation tool |
| CATMA | NLP - annotation tool |
| Webanno | NLP - annotation tool |
| Product Management for AI by Google PM | Product Management for AI by Google PM, a lot of real-life examples |
| Data Science Portfolio | Build a Data Science Portfolio that Stands Out Using These Platforms |
| Python | Pandas CheatSheet |
| Statistics-Probability | A free statistics and probability course. Ideal for aspiring machine learning practitioners. |
| Python | Introduction to Python - A self study course |
| Machine Learning | Building Model RoadMap |
| Data Science | What is Data science, a good explanation for non-data sciencecist |
| OpenCV | OpenCV with Python for beginners, video series. |
| Design Patterns | Design Patterns video lectures. |
| Data Structures | Data Structure by Saurabh Shukla Sir |
| Python | Python tutorials video series |
| Python | How to gets the most of Machine Learning models. |
| Build and share data apps, Python | The fastest way to build and share data apps |
| AI and digital marketing | AI: A Conversational Marketing Masterclass" from CES 2021 |
| Unity ML | Getting started with ML agents in Unity [Tutorial] |
| Resources | AI for Earth technical resources |
| The Cambridge handbook of artificial intelligence | The book gives information about the definition, history, dimensions, and ethics of AI. |
| Artificial Intelligence: A Modern Approach | It gives general information about AI and AI techniques. It can be a guidebook to learn AI in detail. |
| Artificial Intelligence: A System Approach | The book consists of information about AI, AI history, and AI techniques. (It can be found as a pdf document by searching on the Internet.) |
| Introduction to Machine Learning | If you interest in machine learning methods and techniques theoretically, it can be a guidebook. |
| Machine learning for enterprises: Applications, algorithm selection, and challenges | It gives information about ML, ML algorithms, and accuracy of ML algorithms, and some ML application examples for enterprises. Besides, it mentions challenges in deploying machine learning in enterprises. |
| Measurementality (Podcast) | Measurementality is a new series of podcasts, webinars and reports created by the IEEE Standards Association (IEEE SA) in collaboration with The Radical AI Podcast focused on defining what counts in the Algorithmic Age. While it’s critical that Artificial Intelligence Systems (AIS) are transparent, responsible, and trustworthy, Measurementality will explore the deeper issues around what measurements of success we’re optimizing for in the first place. |
| AI and Well-Being | AI and Well-being |
| Tech ethics | Tech ethics |
| List of Math Formulas | It has all the math formulas and cheatsheets in pdf version. Hope it will be useful for someone who is learning python to implement some math concepts |
| Machine Learning for Cyber Security | A curated list of amazingly awesome tools and resources related to the use of machine learning for cyber security. (books, tutorials, datasets, podcasts ect.) |
| ML | Rules of Machine Learning: Best Practices for ML Engineering |
| MeshTensorFlow | Mesh TensorFlow (mtf) is a language for distributed deep learning |
| UX | The People + AI Guidebook was written to help user experience (UX) professionals and product managers follow a human-centered approach to AI. |
| Tensorflow object detection Api | using Tensorflow object detection Api |
| Machine Learning | Machine Learning Glossary |
| ComputerVision | OpenCV Library for ComputerVision |
| Federated Learning | Alex Ingerman, a product manager at Google AI, focusing on federated learning and other privacy-preserving technologies, explains in 4 minutes federated learning and how it helps all machine learning practitioners to protect their users’ privacy by default. |
| Deep learning | Architecture of LSTM |
| Deeplearning | Autoencoders in Deeplearning |
| Seven legal questions for data scientists | This article poses seven legal questions that data scientists should address before they deploy AI. This article is not legal advice. However, these questions and answers should help you better align your organization’s technology with existing and future laws, leading to less discriminatory and invasive customer interactions |
| Cloud Data Warehouse Performance Testing | Product Profile and Evaluation: Amazon Redshift, Microsoft Azure SQL Data Warehouse, Google BigQuery, and Snowflake Data Warehouse |
| Python | Python Exercises, Practice, Solution |
| Python | Beginner Python exercises |
| Linkedln | 10 Tips to Master LinkedIn in a Way You Never Imagined. Practical examples |
| Steps to tech career | Setting SMART Goals for Your Tech Career |
| Designing a Chatbot Conversation | Designing a Chatbot Conversation (UX Design, Experience and Interaction Design Process) |
| Statistics | Cheat Sheet |
| Web Scraping, Collecting Data Tools | 6 Web Scraping Tools That Make Collecting Data A Breeze |
| Backpropagation, stochastic gradient descen | This tutorial is designed to make the role of the stochastic gradient descent and back-propagation algorithms clear in training between networks. |
| Deep learning | Audio Data Preparation and Augmentation |
| Data Science | Algorithms , evidence and data science |
| Statistics | This introductory textbook provides an inexpensive, brief overview of statistics to help readers gain a better understanding of how statistics work and how to interpret them correctly. |
| Data Science | Probabilistic Programming and Bayesian Inference |
| Statistics | Probability and Statistics for Programmers |
| Statistics | 50 Essential Concepts |
| Machine Learning | End-to-End, Transferable Deep RL for Graph Optimization |
| Data Science | 100+ Free Data Science Books (Updated 2021 List) |
| Machine Learning | 100+ Free Machine Learning Books (Updated 2021 List) |
| Programming | 100+ Free Programming Books (Updated 2021 List) |
| Deep learning | Recognizing words |
| Introduction to Artificial Intelligence | The book defines AI, briefs AI history, and gives theoretical information about logic, search, problem-solving, reasoning with uncertainty, machine learning, and data mining, neural networks, and reinforcement learning. |
| Introduction to AI with Python | CS50’s Introduction to Artificial Intelligence with Python explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. |
| The hard lessons of modeling the coronavirus pandemic | Researchers build epidemiological models to get a better understanding of diseases. But with COVID-19, getting a clear view of the virus has been difficult. In the fight against COVID-19, disease modelers have struggled with misunderstanding and misuse of their work. They have also come to realize how unready the state of modeling was for this pandemic. |
| Trove - crowdsourcing marketplace where developers can gather images for AI models from regular individuals | Trove is a new crowdsourcing marketplace from Microsoft where developers can gather images for AI models from regular individuals, responsibly sourced through licensing and privacy frameworks. Get highly relevant and diverse images from real people, for your specific scenario and use case. |
| Automatic scripts exports-nbautoexport-jupyter-code-review | Nbautoexport is a tool for automatically running notebook exports when you save notebooks while using Jupyter. In this section, we'll walk through the basic setup and use. |
| Multi-Class Metrics | Explain why F1-scores are used, and how to calculate them in a multi-class setting. |
| Evaluating models | Describes how to use evaluation metrics for your model after it is trained, and provides some basic suggestions for ways you might be able to improve model performance. |
| TensorFlow Federated | TensorFlow Federated (TFF) is an open-source framework for machine learning and other computations on decentralized data. |
| Federated Learning Comics | An online comic from Google AI |
| Computer Vision | Magazine |
| Data Science | 10 NLP Terms Every Data Scientist Should Know |
| Python | Designing Custom 2D and 3D CNNs in PyTorch |
| Machine Learning | Predicting Manhattan Rent with Linear Regression |
| Data Science | You Should Master Data Analytics First Before Becoming a Data Scientist |
| Python | Learn how to use the loc method in the pandas Python library |
| Data Science | 7 Steps to a Successful Data Science Project Beginners Guide on Completing a Data Science Project from Scratch |
| Tensorflow | Tensorflow pre trained model tutorials |
| Isotonic Regression and Pava Algorithm | Isotonic Regression is used in the probability calibration of classifiers. Probability Calibration of classifiers deals with the process of optimizing the output of classifiers so that the outputs of the classifier model can be directly interpreted as a confidence level and Pava inspects the points and if it finds a point that violates the constraints, it pools that value with its adjacent members which ultimately go on to form a block. |
| Data Science | Career Development |
| Machine Learning | Supervised Learning |
| Software Testing Life Cycle | STLC (Software Testing Life Cycle) Phases, Entry, Exit Criteria |
| Machine Learning | Commonly used Machine Learning Algorithms (with Python and R Codes) |
| Agile Development | The Art of Agile Development: Pragmatic Guide to Agile Software Development |
| Machine Learning | The 10 Best Machine Learning Algorithms for Data Science Beginners |
| Machine Learning, Dataminig | This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). |
| AI reading list: 8 interesting books about artificial intelligence to check out | AI reading list: 8 interesting books about artificial intelligence to check out |
| Science fiction hasn’t prepared us to imagine machine learning | Science fiction did a great job preparing us for submarines and rockets. But it seems to be struggling lately. We don’t know what to hope for, what to fear, or what genre we’re even in. "Clearly some plot twist involving machine learning is underway. It’s been hard to keep up with new developments: from BERT (2018) to GPT-3 (2020)—which can turn a prompt into an imaginary news story—to, most recently, CLIP and DALL-E (2021), which can translate verbal descriptions into images." |
| Comet is doing for ML what Github did for code | Track your datasets, code changes, experimentation history, and models. Comet provides insights and data to build better models, faster while improving productivity, collaboration and explainability. |
| Kneed, kneedle algorithm, Python | This repository is an attempt to implement the kneedle algorithm. Given a set of x and y values, kneed will return the knee point of the function. The knee point is the point of maximum curvature. |
| Qlib, Quant investment strategies, investement platform | Qlib is an AI-oriented quantitative investment platform. It contains the full ML pipeline of data processing, model training, back-testing; and covers the entire chain of quantitative investment: alpha seeking, risk modeling, portfolio optimization, and order execution. |
| data science | data science course |
| Machine Learning | Building ML model |
| Artificial Intelligence | Neural Networks in jax |
| Data Visualization | Data visualization with swift |
| Tensorflow serving model | Deployment of model |
| How to think like a programmer — lessons in problem solving | How to think like a programmer — lessons in problem solving |
| Resume Bullet Point Examples That Get Interviews | 19 Resume Bullet Point Examples That Get Interviews |
| LinkedIn Skills: How to Add the Right Skills to LinkedIn | Article answers the questions: How to add skills for LinkedIn? How do you pick the “right” skills for LinkedIn? And more |
| Python, drone programming | Drone Programming With Python Course |
| Machine Learning is Fun! | The world’s easiest introduction to Machine Learning. This guide is for anyone who is curious about machine learning but has no idea where to start. |
| What to expect in your first data science role | What to expect in your first data science role |
| Machine Learning | ML Cheat Sheet |
| NetAdapt | Platform-Aware Neural Network Adaptation for Mobile Applications |
| Data Visualization | Data Visualization with seaborn |
| Software Architecture | Fundamentals of Software Architecture |
| Leetcode, prepare for technical interviews | The best platform to help you enhance your skills, expand your knowledge and prepare for technical interviews. A well-organized tool that helps you get the most out of LeetCode by providing structure to guide your progress towards the next step in your programming career |
| Three things to do when you don't have a computer science degree | Three things to do when you don't have a computer science degree, a lot of practical advices |
| Deep Learning | Convolutional Neural Networks in TensorFlow |
| Algorithms | The Coding Interview Bootcamp |
| Python | Learn how to use Google's Deep Learning Framework - TensorFlow with Python! Solve problems with cutting edge techniques! |
| Data Science | Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning |
| A Single Equation that Rules the World | The equation connects neuron firing, fluid convection, the Mandelbrot set and so much more and will definitely change your view of this world. |
| Statistics the complete mini course | Statistics the complete mini course |
| Analytics: The complete minicourse | Analytics: The complete minicourse |
| Machine Learning | Machine Learning Glossary - Google |
| Rules of Machine Learning: Best Practices for ML Engineering ~ Martin Zinkevich | This document is intended to help those with a basic knowledge of machine learning get the benefit of best practices in machine learning from around Google. It presents a style for machine learning, similar to the Google C++ Style Guide and other popular guides to practical programming. If you have taken a class in machine learning, or built or worked on a machine-learned model, then you have the necessary background to read this document. |
| Computer Vision | MR-CNN & S-CNN — Multi-Region & Semantic-aware CNNs |
| Java | Spring boot request routing example |
| Python | How to Make Out-of-Sample Forecasts with ARIMA in Python |
| Java | Java Programming and Software Engineering Fundamentals Specialization |
| C | Programming Fundamentals |
| Data Science | Data Science In 5 Minutes |
| Data Science | Data Science Full Course - Learn Data Science in 10 Hours |
| Data Analytics | Data Analytics For Beginners |
| Machine Learning | ML in 100 days |
| Machine Learning | Imbalanced data in classification |
| Machine Learning | Bayes Theorem |
| Machine Learning | Regression Analysis |
| ML Modelling | Loan Repayment ML Modeling |
| Math | Math skills for data science |
| Python | Fully Explained K-Nearest Neighbors with Python |
| Data Science | Introduction to Data Science |
| Data Science | Lecture Notes |
| Data Science | Introduction to Data Science |
| Interview Prepartion Data Science | Repo for interview questions as well as tutorials link |
| Adopting the power of conversational UX | Adopting the power of conversational UX |
| Data Engineering | We Don’t Need Data Scientists, We Need Data Engineers |
| Data Science | Must-have Chrome Extensions For Machine Learning Engineers And Data Scientists |
| Data Mining | Using Twitter Rest APIs in Python to Search and Download Tweets in Bulk |
| Data Science | Book Advise |
| Computer Vision | How Computer Vision Works |
| Neural Networks | How Neural Networks Work |
| Several Tutorials Notebooks for Python | The notebooks are about Data Science, EDA, ML, DL, CNNs, RNNs, etc., and some libraries such as seaborn and PyTorch |
| Data Science | Cheat Sheet |
| R Programming | Cheat Sheet |
| Git | Cheat Sheet |
| Data Science, Machine Learning Projects | This Repository is divided into four main parts. 1) Roadmap 2) Free online Courses 3) 500+ Datascience Projects 4) 100+ Free Machine Learning Books. |
| Numeric Analysis | The Gauss-Seidel Method |
| Web Developping | The only course you need to learn web development - HTML, CSS, JS, Node, and More! |
| Machine Learning | 100+ Machine Learning Algorithms |
| Machine Learning | A Brief Introduction to Machine Learning for Engineers |
| Machine Learning | Predicting Hard Drive Failure with Machine Learning |
| Data Science | Getting Started with Data Science: Making Sense of Data with Analytics |
| ML trends 2021 | 5 Trends to know in artificial intelligence and machine learning for 2021 |
| AI, ML, trends for 2021 | 5 Trends to know in Artificial Intelligence and Machine Learning for 2021 |
| AI for Frontend Developers | Answers to the questions: how is AI different from traditional software development? how can AI help frontend devs? how can frontend devs get started? |
| Machine Learning | 20+ Machine Learning Datasets & Project Ideas |
| How to build a facebook messenger chat bot from scratch | How to build a facebook messenger chat bot from scratch, Udemy course |
| Learn Google Drive From Beginner to Advanced | Learn Google Drive From Beginner to Advanced |
| Data Science | Cheat Sheet |
| Python | Time series |
| Go | Design Patterns in Go |
| Machine Learning | 140 Machine Learning Formulas |
| Python | Python Cheat Sheet |
| Best Practices for ML Engineering | Guide on Best Practices for ML Engineering by Martin Zinkevich - Research Scientist @ Google |
| Machine learning | Machine learning Book |
| Starting Out with Python | This book uses the Python language to teach programming concepts and problem-solving skills, without assuming any previous programming experience. With easy-to-understand examples, pseudocode, flowcharts, and other tools, the student learns how to design the logic of programs then implement those programs using Python. This book is ideal for an introductory programming course or a programming logic and design course using Python as the language. |
| Artificial Intelligence | The State of Ai by Andrew Ng |
| Artificial Intelligence | AI + US Government |
| Artificial Intelligence | NEWSLETTER, Published weekly. The most important artificial intelligence and machine learning news and articles. |
| AI for Product Managers | Cheat Sheets from Udacity Scholarship |
| MySQL | MySQL Cheat Sheet |
| Machine Learning | Your First Machine Learning Project in Python Step-By-Step |
| Don't build chatbot end to end | Advices for chatbot developing |
| Wireflow for chatbot | Why wireflow is important for building a graphical user interface? What does it take to have “wireflow” for conversational experience? You can find there answers for these and more questions. |
| Artificial Intelligence | Can A.I. make music? Can it feel excitement and fear? Is it alive? Will.i.am and Mark Sagar push the limits of what a machine can do. How far is too far, and how much further can we go? The Age of A.I. is an 8 part documentary series hosted by Robert Downey Jr. covering the ways Artificial Intelligence, Machine Learning and Neural Networks will change the world. |
| Python | Python dashboard libraries: exploring interaction voila, dash, and streamlit |
| Python | Natural Language Processing in Python |
| Deep Learning | Deep Learning Crash Course for Beginners |
| Python | Compose and Program Music in Python using Earsketch by Coursera Project Network |
| AI ethics | Coded Bias: Unmasking the Abuses of Face Recognition Technologies in Society |
| Python | Linear Algebra with Python |
| Machine Learning | Amazon's Machine Learning University on Youtube |
| Deep Learning | Yann LeCunn's Deep Learning course in New York University |
| Python, Machine Learning, Tensorflow, Deep Learning | Here I documented my journey & all the resources I used to get into the field of ML & DL. |
| AI.Applications | Android Kunjappan 5.25 A movie featuring a Robot as a caretaker for a very stubborn village old man in Kerala, India. |
| SQL | Cheat Sheets |
| JavaScript - The third age of JavaScript | presentation about the future of JS from the JSworld conference |
| How to build a machine learning model | How to build a machine learning model - Complete guide for your first data science project |
| How to get a job in data science (with no work experience) | How to get a job in data science (with no work experience) |
| Data Science | Introduction to Data Science |
| Artificial Intelligence | AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone--especially your non-technical colleagues--to take. |
| C# | Design Patterns in C# and .NET |
| Data Engineering | The Data Engineering Cookbook |
| Machine Learning | MACHINE LEARNING QUICK REFERENCE: BEST PRACTICES |
| Deep Learning | Deep Learning: An Introduction for Applied Mathematicians |
| Data Science | 3 Reasons Why You Should Choose MATLAB As The Programming Language To Learn Data Science |
| NLP | Center for Language Engineering Urdu and Pakistani regional languages processing center |
| Artificial Intelligence | Global AI Hub is a Swiss-based leading global community for AI education and AI career opportunities. |
| NLP | UrduHack Urduhack is an open-source NLP library for urdu language. It comes with a lot of battery included features to help you process Urdu data in the easiest way possible. |
| Artificial Intelligence | On March 9, 2016, the worlds of Go and artificial intelligence collided in South Korea for an extraordinary best-of-five-game competition, coined The DeepMind Challenge Match. |
| Machine Learning | Its a list of research papers with the code used and which are reproducable. So its a good resource to learn how others in this field solve problems. |
| I Landed a Job at an AI Startup after Studying for Only 4 Months | I Landed a Job at an AI Startup after Studying for Only 4 Months |
| Companies hiring data scientists right now | Companies hiring data scientists right now - website with a lot of job info |
| Python | Cheat Sheet: The pandas DataFrame Object |
| Data Science | Build data skills online |
| ['Synthetic humans' and insights into Artificial Intelligence. Could science fiction be our reality much sooner than we think? ]( How To Build A Human with Gemma Chan | Artificial Intelligence |
| Deep Learning MIT 6S.191 couse "Intro to Deep Learning" | This is a 6 lecture 2021 edition course 2021 on Deep Learning covering such topics as Foundation of DL, Recurrent Neural and Convolutional Networks, Deep Generative Modeling, Reinforcement Learning, and Deep Learning New Frontiers. |
| imbalanced datasets in machine learning | Handling imbalanced datasets in machine learning What should and should not be done when facing an imbalanced classes problem? |
| OpenAI | CLIP: Connecting Text and Images introducing a neural network called CLIP which efficiently learns visual concepts from natural language supervision. CLIP can be applied to any visual classification benchmark by simply providing the names of the visual categories to be recognized, similar to the “zero-shot” capabilities of GPT-2 and GPT-3. |
| Model Evaluation | When measuring model performance, we want to have sensible metrics that indicate how well the model is performing. This should encompass the model's ability to predict each of the training classes and give us insight into any biases that may exist on the model. By having these clear metrics, we can get a good idea of how the model will perform when deployed into production, which will ultimately drive performance of the product and its impact in the market. |
| precision and recall | In evaluating multi-class classification problems, we often think that the only way to evaluate performance is by computing the accuracy which is the proportion or percentage of correctly predicted labels over all predictions. |
| Overview of Modelling | As a product manager looking to develop AI products, it is important to understand the mechanics of ML. Model architecture, training data and model evaluation are all key elements of ML and are instrumental in successful AI product management. When using AI and products, there are numerous uncertainties that that can alter the course of development and by understand the core concepts, we can stay ahead of the unknowns and plan development accordingly. |
| Multi-Class Metrics Made Simple, Part I: Precision and Recall | Performance measures for precision and recall in multi-class classification can be a little — or very — confusing, so in this post I’ll explain how precision and recall are used and how they are calculated. It’s actually quite simple! But first, let’s start with a quick recap of precision and recall for binary classification. |
| Multi-Class Metrics Made Simple, Part II: the F1-score | article explains another popular performance measure, the F1-score, or rather F1-scores, as there are at least 3 variants. I’ll explain why F1-scores are used, and how to calculate them in a multi-class setting |
| The People + AI Guidebook | The Guidebook’s recommendations are based on data and insights from over a hundred individuals across Google product teams, industry experts, and academic research. These six chapters follow the product development flow, and each one has a related worksheet to help turn guidance into action. |
| Audio Classification Model | Learnable Audio Frontend (LEAF) is a neural network that can be initialized to approximate mel filterbanks, and then be trained jointly with any audio classifier |
| Combining artificial intelligence and augmented reality in mobile apps | Augmented reality (AR) and artificial intelligence (AI) are two of the most promising technologies available to mobile app developers. Huge hype cycles and rapidly evolving tools, though, have blurred the lines between the two, making it difficult to tell where AI ends and AR begins. This post aims to disambiguate AR and AI. It covers how AR and AI work together, the current state of SDKs and APIs for each, and some practical ways to combine them to build incredible mobile experiences. |
| Two Macro-F1's | The bottom line is: there’s more than one macro-F1 score; and data scientists mostly use whatever is available in their software package without giving it a second thought. |
| SEER: The start of a more powerful, flexible, and accessible era for computer vision | Facebook AI SEER (SElf-supERvised), a new billion-parameter self-supervised computer vision model that can learn from any random group of images on the internet. |
| Introduction to Threshold-Moving for Imbalanced Classification | Classification predictive modeling typically involves predicting a class label. |
| Project management- Google program | Start your path to a career in project management- Google program |
| Calculate Precision, Recall, and F-Measure for Imbalanced Classification | Classification accuracy is the total number of correct predictions divided by the total number of predictions made for a dataset. As a performance measure, accuracy is inappropriate for imbalanced classification problems. |
| 4 Types of Classification Tasks in Machine Learning | Machine learning is a field of study and is concerned with algorithms that learn from examples. Classification is a task that requires the use of machine learning algorithms that learn how to assign a class label to examples from the problem domain. An easy to understand example is classifying emails as “spam” or “not spam.” |
| User Persona Examples | Promoting a product without knowing who your target audience–or what your target audience wants–is an impossible task. You’ll just be making decisions based on what you think they want. That’s not sustainable over the life of any brand. That’s why creating user personas is important for any company that wants to grow. |
| How to Develop Great B2B Buyer Personas (With Templates) | A B2B buyer persona represents your ideal client decision-maker. When developing your buyer persona, address all the issues and aspects that may have an impact on how, when, and why the person will buy. These factors include demographic information, patterns of behavior, motivation and goals. |
| Marketing Plan | What is a Marketing Plan and How to Make One? |
| Growth Strategy | Plan Your Business Goals With These 5 Templates |
| Market Strategy | How does your business connect with its customers? How do you deliver your unique value to your target customers? How do you go from the initial connection with a potential customer to the fulfillment of your brand promise? |
| TARGET MARKET | HOW TO FIND YOUR TARGET MARKET |
| Style classification and prediction of residential buildings | Architectural styles machine learning classification prediction residential buildings |
| Machine Learning, Architectural Styles and Property Values | This paper first introduces an algorithm that collects pictures of individual buildings from Google Street View. Second, it trains a deep convolutional neural network (CNN) to classify residential buildings into architectural styles, taking into account spatial dependencies of these styles. Third, it investigates whether architectural styles influence house prices. For resales , the architectural style is a significant determinant of transaction prices while no such effect is found for new buildings. Additionally, we are able to provide guidance on how to detect and overcome some of the limitations of machine learning methods through a large-scale comparison of predictions and expert classifications. |
| PII | What is personally identifiable information (PII)? How to protect it under GDPR |
| Personally Identifiable Information (PII) | What Is Personally Identifiable Information (PII)? |
| Rethinking Design Tools in the Age of Machine Learning | article about philosophy of AI / ML |
| World Speeches - Youtube channel from one of our study group #sg_ai_world | World Speeches - Youtube channel from one of our study group #sg_ai_world |
| A Unified Tool for the Education of Humans and Machines | This article is about making machine learning tools more accessible. Author: Patrick Hebron |
| Computer Vision & AI | Researchers From Computer Vision Center (CVC) And The University Of Barcelona Conducted A Study That Results In Improved Accuracy On Face Verification Tasks In The Presence Of Other Confounding Attributes |
| Machine learning | Algorithms |
| SQL | Learning SQL |
| Artificial Intelligence | Cheat Sheets for AI Neural Networks, Machine Learning, DeepLearning & Big Data |
| Python | A Simple Walk-through with Matplotlib for Data Science |
| Python | Numpy Tutorial |
| MATLAB | Machine Learning with MATLAB |
| Medical AI TED Talk | Brain scans to get better diagnoses. |
| A real case of solving and automating an issue with AI | A fully AI-based system to automate water meter data collection in Morocco country |
| Deep learning for COVID-19 chest CT (computed tomography) image analysis: a lesson from lung cancer | A real case of what we covered in the Udacity Course for the course/challenge: Bertelsmann Technology Scholarship - AI Track |
| Data Science | Professional Certificate in Data Science |
| Data Science | What is Data Science? |
| AWS | MLOps: Continuous Delivery for Machine Learning on AWS |
| Computer Vision | How Computer Vision Works |
| Data Mining | Data Mining Techniques in Analyzing Process Data: A Didactic |
| Marketing, Digital transformation, Trends | 4 COVID-era trends that will have a lasting impact on the products and experiences people want |
| Consumer Trends after Covid-19 | Pivots from the pandemic that are here to stay |
| concepts and philosophy of AI by Patrick Hebron | work focuses on the emerging intersections between machine learning, design tools, programming languages and operating systems. I have also studied computer graphics, aesthetic philosophy, art and film. I come from a family of progressive educators who have greatly influenced my thinking about intelligence and toolmaking. |
| Bias in AI | When Google forced out two well-known artificial intelligence experts, a long-simmering research controversy burst into the open. |
| AI Crash Course | The topic of AI, the whole field and philosophical questions. |
