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br3v1an5 / VtucreatorVTU Creator is an open source project founded by Lajela Technologies, a multi-language, easy-to-use PHP built VTU Content Management System (VTU-CMS) that enables you to create a full functional and automated and instant digital delivery online recharge portal without basic knowledge of programming Languages. Create Airtime Top-Up, Data Subscription, Electricity/Water/Gas Bill, Bulk SMS, Money transfer and Voucher/Recharge Card Printing portal and be connected to any service provider, API and hosted SIM Card.
mark-watson / KgcreatorThe Knowledge Graph Creator (kgcreator) is a tool for automating the generation of RDF data for Knowledge Graphs from raw text data read from an input directory path. (from my book Practical Python Artificial Intelligence Programming https://leanpub.com/pythonai
THU-KEG / KB Plugin[EMNLP2024] KB-Plugin: A Plug-and-play Framework for Large Language Models to Induce Programs over Low-resourced Knowledge Bases
M-Taghizadeh / Python WebinarOur goal in this webinar is to provide a quick and practical training as your first step to becoming a professional Python programmer, so that after watching this training you will be able to gain a very good knowledge of programming with Python and using Python in artificial intelligence, machine learning, deep learning, data mining, and backend programming using Flask and Django
AkobirCoder / Udevs.ioThis project is just my last project, and this website includes several parts. They are direction, command, services, tools, clients, portfolio, and contact sections. Actually, you can receive more information which are interested you about the IT world, and you can also enhance your programming knowledge by this site.
OrysyaStus / UCSD Data Mining CertificateModern databases can contain massive volumes of data. Within this data lies important information that can only be effectively analyzed using data mining. Data mining tools and techniques can be used to predict future trends and behaviors, allowing individuals and organizations to make proactive, knowledge-driven decisions. This expanded Data Mining for Advanced Analytics certificate provides individuals with the skills necessary to design, build, verify, and test predictive data models. Newly updated with added data sets, a robust practicum course, a survey of popular data mining tools, and additional algorithms, this program equips students with the skills to make data-driven decisions in any industry. Students begin by learning foundational data analysis and machine learning techniques for model and knowledge creation. Then students take a deep-dive into the crucial step of cleaning, filtering, and preparing the data for mining and predictive or descriptive modeling. Building upon the skills learned in the previous courses, students will then learn advanced models, machine learning algorithms, methods, and applications. In the practicum course, students will use real-life data sets from various industries to complete data mining projects, planning and executing all the steps of data preparation, analysis, learning and modeling, and identifying the predictive/descriptive model that produces the best evaluation scores. Electives allow students to learn further high-demand techniques, tools, and languages.
Aryia-Behroziuan / Robot LearningIn developmental robotics, robot learning algorithms generate their own sequences of learning experiences, also known as a curriculum, to cumulatively acquire new skills through self-guided exploration and social interaction with humans. These robots use guidance mechanisms such as active learning, maturation, motor synergies and imitation. Association rules Main article: Association rule learning See also: Inductive logic programming Association rule learning is a rule-based machine learning method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness".[60] Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction.[61] Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems. Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets.[62] For example, the rule {\displaystyle \{\mathrm {onions,potatoes} \}\Rightarrow \{\mathrm {burger} \}}\{{\mathrm {onions,potatoes}}\}\Rightarrow \{{\mathrm {burger}}\} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as promotional pricing or product placements. In addition to market basket analysis, association rules are employed today in application areas including Web usage mining, intrusion detection, continuous production, and bioinformatics. In contrast with sequence mining, association rule learning typically does not consider the order of items either within a transaction or across transactions. Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component, typically a genetic algorithm, with a learning component, performing either supervised learning, reinforcement learning, or unsupervised learning. They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions.[63] Inductive logic programming (ILP) is an approach to rule-learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative examples. Inductive programming is a related field that considers any kind of programming language for representing hypotheses (and not only logic programming), such as functional programs. Inductive logic programming is particularly useful in bioinformatics and natural language processing. Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting.[64][65][66] Shapiro built their first implementation (Model Inference System) in 1981: a Prolog program that inductively inferred logic programs from positive and negative examples.[67] The term inductive here refers to philosophical induction, suggesting a theory to explain observed facts, rather than mathematical induction, proving a property for all members of a well-ordered set. Models Performing machine learning involves creating a model, which is trained on some training data and then can process additional data to make predictions. Various types of models have been used and researched for machine learning systems. Artificial neural networks Main article: Artificial neural network See also: Deep learning An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one artificial neuron to the input of another. Artificial neural networks (ANNs), or connectionist systems, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules. An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68]
Shimraz / Deep Learning Specialization By DeepLearning.AI CourseraThe Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more. AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career. Along the way, you will also get career advice from deep learning experts from industry and academia.
graphmd-lpe / GraphmdGraphMD — Literate Programming Environment for Markdown-Based Executable Knowledge Graphs
gshanbhag525 / Programming Knowledge No description available
Elli610 / Blockchain 101A set of workshop to master blockchain knowledge and programming
developershutt / Introduction To Machine Learning With PythonThis course is for absolute beginners who have elementary knowledge of Python Programming Language. So lets get started....
bookug / LinuxProgrammingbasic knowledge and some advanced topics about Linux Programming
flux-doctrine / Awesome Fbp📚 A community knowledge base for awesome flow-based programming (FBP) resources. Contributions most welcome!
jigyasa-grover / PyGame 101 CodelabLearn to make a desktop game in Python from scratch. No high-end programming skills or Python knowledge required, just your laptop, enthusiasm and willingness to learn !
CodeQuestic / OpenPrepComprehensive collection of resources for various programming languages. It includes code samples, best practices, and reference materials to help developers of all levels enhance their skills and knowledge in multiple languages.
ignabelitzky / Tiny ProgramsSimple programs to test different programming knowledge
elzemrany / CS 1103 Programming 2 we are going to be building on knowledge gained in CS 1102, but we are going on to cover some of the more advanced fundamentals of programming including basic data structures (such as lists, stacks and queues, binary trees, and hash tables), recursion, common algorithms (such as searching and sorting), and generic programming. This course also looks more deeply at object-oriented programming, including the use of class hierarchies.
shriyaa01 / Python Quiz GameThis repository contains a simple Python quiz game that tests your knowledge on a variety of topics, including programming, web development, and more. The game randomly selects questions from a predefined set, presents multiple-choice options, and provides immediate feedback on your answers. Challenge yourself, learn, and have fun.
Idealantis / CleverAlgorithmsInPythonClever Algorithms in Python (CAIP) is an extension of the body of knowledge started by Jason Browlee : Clever Algorithms Nature Inspired Programming Recipes.
