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Un mass scanner facut in 2001 de Bagabontu, ce necesita uid/gid 0 (synscan). La vremea aia era bomboana de pe coliva, insa pustanii nestiind programare sau ce e ala socket, cred ca au descoperit pe dumnezeu cand il vad in 2017. Sunt fel si fel de aratarii de oameni care cred ca daca sparg servere cu oarece tools, sunt si hackeri.

1. Machine Learning (FREE) Andrew Ng First, and arguably the most popular course on this list, Machine Learning provides a broad introduction to machine learning, data mining, and statistical pattern recognition. Topics include: Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas. The course is 11 weeks long and averages a 4.9/5 user rating, currently. It is free to take, but you can pay $79 for a certificate upon course completion. 2. Machine Learning Foundations: A Case Study Approach (FREE) Carlos Guestrin, Emily Fox In Machine Learning Foundations: A Case Study Approach, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of it you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. By the end of this course, you will be able to: Identify potential applications of machine learning in practice. Describe the core differences in analyses enabled by regression, classification, and clustering. Select the appropriate machine learning task for a potential application. Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. Represent your data as features to serve as input to machine learning models. Assess the model quality in terms of relevant error metrics for each task. Utilize a dataset to fit a model to analyze new data. Build an end-to-end application that uses machine learning at its core. Implement these techniques in Python. The course is 6 weeks long and requires about 5-8 hours of commitment per week. It currently averages a 4.6/5 user rating and is free to take, but you can pay $59 for a certificate upon completion. 3. Learning From Data (FREE) Yaser S. Abu-Mostafa Learning From Data is an introductory course in machine learning that will cover basic theory, algorithms, and applications. It balances theory and practice, and covers the mathematical as well as the heuristic aspects. The lectures follow each other in a story-like fashion: What is learning? Can a machine learn? How to do it? How to do it well? Take-home lessons. You’ll learn how to: Identify basic theoretical principles, algorithms, and applications of Machine Learning Elaborate on the connections between theory and practice in Machine Learning Master the mathematical and heuristic aspects of Machine Learning and their applications to real world situations The course is 10 weeks long and requires about 10 – 20 hours per week of commitment. It is free to take, but you can add a verified certificate of completion for $49. 4. Statistical Learning (FREE) Trevor Hastie, Rob Tibshirani This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: Linear and polynomial regression, logistic regression and linear discriminant analysis Cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso) Nonlinear models, splines and generalized additive models Tree-based methods, random forests and boosting; support-vector machines Also, some unsupervised learning methods are discussed like principal components and clustering (k-means and hierarchical). This is not a math-heavy class and all computing is done in R. If you are not familiar with R that is ok. There are lectures devoted to R, giving tutorials from the ground up, and progressing with more detailed sessions that implement the techniques in each chapter. The class is free to take and is expected of you to commit 3 – 5 hours per week to work through the course material. If you complete the course, and achieve a passing grade of 50% on the quizzes. If you get 90% or higher, your statement will be “with distinction”. 5. Machine Learning: Regression (FREE) Carlos Guestrin, Emily Fox In Machine Learning: Regression, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data — such as outliers — on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets. By the end of this course, you will be able to: Describe the input and output of a regression model Compare and contrast bias and variance when modeling data Estimate model parameters using optimization algorithms Tune parameters with cross validation Analyze the performance of the model Describe the notion of sparsity and how LASSO leads to sparse solutions Deploy methods to select between models Exploit the model to form predictions Build a regression model to predict prices using a housing dataset Implement these techniques in Python The course requires 6 weeks of your time and approximately 5 – 8 hours per week to study the material. It’s current user rating averages a 4.8/5. The course is free to take, but you can pay $59 to receive a certificate of completion at the end. 6. Machine Learning: Classification (FREE) Carlos Guestrin, Emily Fox In Machine Learning: Classification, you will create classifiers that provide state-of-the-art performance on a variety of tasks. You will become familiar with the most successful techniques, which are most widely used in practice, including logistic regression, decision trees and boosting. In addition, you will be able to design and implement the underlying algorithms that can learn these models at scale, using stochastic gradient ascent. You will implement these technique on real-world, large-scale machine learning tasks. You will also address significant tasks you will face in real-world applications of ML, including handling missing data and measuring precision and recall to evaluate a classifier. This course is hands-on, action-packed, and full of visualizations and illustrations of how these techniques will behave on real data. By the end of this course, you will be able to: Describe the input and output of a classification model Tackle both binary and multiclass classification problems Implement a logistic regression model for large-scale classification Create a non-linear model using decision trees Improve the performance of any model using boosting Scale your methods with stochastic gradient ascent Describe the underlying decision boundaries Build a classification model to predict sentiment in a product review dataset Analyze financial data to predict loan defaults Use techniques for handling missing data Evaluate your models using precision-recall metrics Implement these techniques in Python (or in the language of your choice, though Python is highly recommended) The course is 7 weeks long and currently averages a 4.6/5 user rating. While the course materials are provided for free, you will need to pay $59 to earn a course completion certificate. 7. Machine Learning: Clustering & Retrieval (FREE) Carlos Guestrin, Emily Fox In Machine Learning: Clustering & Retrieval you will examine similarity-based algorithms for retrieval. You will also examine structured representations for describing the documents in the corpus, including clustering and mixed membership models, such as latent Dirichlet allocation (LDA). You will implement expectation maximization (EM) to learn the document clusterings, and see how to scale the methods using MapReduce. By the end of this course, you will be able to: Create a document retrieval system using k-nearest neighbors Identify various similarity metrics for text data Reduce computations in k-nearest neighbor search by using KD-trees Produce approximate nearest neighbors using locality sensitive hashing Compare and contrast supervised and unsupervised learning tasks Cluster documents by topic using k-means Describe how to parallelize k-means using MapReduce. Examine probabilistic clustering approaches using mixtures models Fit a mixture of Gaussian model using expectation maximization (EM) Perform mixed membership modeling using latent Dirichlet allocation (LDA) Describe the steps of a Gibbs sampler and how to use its output to draw inferences Compare and contrast initialization techniques for non-convex optimization objectives Implement these techniques in Python The course is 6 weeks in length and currently averages a 4.9/5 user rating. The course materials are free, but you’ll need to pay $59 if you want a course completion certificate. 8. Unsupervised Machine Learning Hidden Markov Models in Python ($50) Justin C While the current fad in deep learning is to use recurrent neural networks to model sequences, this course will introduce you to a machine learning algorithm that has been around for several decades now – the Hidden Markov Model. In Unsupervised Machine Learning Hidden Markov Models in Python, you’ll learn to measure the probability distribution of a sequence of random variables. In this course you’ll learn: How to use gradient descent to solve for the optimal parameters of an HMM, as an alternative to the popular expectation-maximization algorithm. How to work with sequences in Theano, a popular library for deep learning How to look at a model of sickness and health, and calculate how to predict how long you’ll stay sick, if you get sick How Markov models can be used to analyze how people interact with your website, and fix problem areas like high bounce rate, which could be affecting your SEO Practical applications of Markov models, including generating images, smartphone autosuggestions, and using HMMs to answer one of the most fundamental questions in biology – how is DNA, the code of life, translated into physical or behavioral attributes of an organism? The course is comprised of 35 videos and runs a total time of 4 hours. It currently averages a 4.7/5 user rating. However, the course is not free, it costs $50. 9. Data Science and Machine Learning with Python – Hands On! ($35) Frank Kane If you’ve got some programming or scripting experience, Data Science and Machine Learning with Python – Hands On! will teach you the techniques used by real data scientists in the tech industry – and prepare you for a move into this hot career path. This comprehensive course includes 68 lectures spanning almost 9 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. It covers the machine learning and data mining techniques real employers are looking for, including: Regression analysis K-Means Clustering Principal Component Analysis Train/Test and cross validation Bayesian Methods Decision Trees and Random Forests Multivariate Regression Multi-Level Models Support Vector Machines Reinforcement Learning Collaborative Filtering K-Nearest Neighbor Bias/Variance Tradeoff Ensemble Learning Term Frequency / Inverse Document Frequency Experimental Design and A/B Tests The course costs $35 and currently has an average user rating of 4.6/5. 10. Machine Learning for Data Science and Analytics (FREE) Ansaf Salleb-Aouissi, Cliff Stein, David Blei, Itsik Peer, Mihalis Yannakakis, Peter Orbanz Machine Learning for Data Science and Analytics is an introduction to machine learning and algorithms. You will develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics. You will also examine why algorithms play an essential role in Big Data analysis. In this course, you’ll learn: What machine learning is and how it is related to statistics and data analysis How machine learning uses computer algorithms to search for patterns in data How to use data patterns to make decisions and predictions with real-world examples from healthcare involving genomics and preterm birth How to uncover hidden themes in large collections of documents using topic modeling How to prepare data, deal with missing data and create custom data analysis solutions for different industries Basic and frequently used algorithmic techniques including sorting, searching, greedy algorithms and dynamic programming The course is 5 weeks and requires a commitment of 7-10 hours per week. It is free, but you have the option of paying $99 for a verified certificate of completion. Sursa: https://hackerlists.com/beginner-ml-courses/

1. Introduction to Autonomous Mobile Robots (edX) The objective of Introduction to Autonomous Mobile Robots is to provide the basic concepts and algorithms required to develop mobile robots that act autonomously in complex environments. The main emphasis is put on mobile robot locomotion and kinematics, environment perception, probabilistic map based localization and mapping, and motion planning. The lectures and exercises of this course introduce several types of robots such as wheeled robots, legged robots and drones. 2. Underactuated Robotics (edX) Underactuated Robotics is taught by Russ Tedrake, Robin Deits and Twan Koolen. It is comprised of 19 lectures covering algorithms for walking, running, swimming, flying, and manipulation and the prerequisites for this course include basic linear algebra and differential equations. 3. Introduction to Robotics (MIT) Introduction to Robotics, taught by Harry Asada and John Leonard, provides an overview of robot mechanisms, dynamics, and intelligent controls. Topics include planar and spatial kinematics, and motion planning; mechanism design for manipulators and mobile robots, multi-rigid-body dynamics, 3D graphic simulation; control design, actuators, and sensors; wireless networking, task modeling, human-machine interface, and embedded software. 4. Control of Mobile Robots (Coursera) Control of Mobile Robots, taught by Magnus Egerstedt, is a course that focuses on the application of modern control theory to the problem of making robots move around in safe and effective ways. The structure of this class is somewhat unusual since it involves many moving parts – to do robotics right, one has to go from basic theory all the way to an actual robot moving around in the real world, which is the challenge this course has set out to address. 5. Robot Mechanics and Control, Part I (edX) Robot Mechanics and Control, Part I provides a mathematical introduction to the mechanics and control of robots that can be modeled as kinematic chains. Topics covered include the concept of a robot’s configuration space and degrees of freedom, static grasp analysis, the description of rigid body motions, kinematics of open and closed chains, and the basics of robot control. 6. Robot Mechanics and Control, Part II (edX) Robot Mechanics and Control, Part II covers screw motions and the product of exponentials kinematics formula, inverse kinematics of open chains, velocity kinematics and statics, closed chain kinematics, and basics of robot control. 7. Autonomous Navigation for Flying Robots (edX) Autonomous Navigation for Flying Robots introduces the basic concepts for autonomous navigation for quadrotors. The following topics will be covered: 3D geometry, probabilistic state estimation, visual odometry, SLAM, 3D mapping, linear control. In particular, you will learn how to infer the position of the quadrotor from its sensor readings and how to navigate it along a trajectory. 8. Artificial Intelligence for Robotics (Udacity) Artificial Intelligence for Robotics teaches you how to program all the major systems of a robotic car from the leader of Google and Stanford’s autonomous driving teams. You will learn basic methods in Artificial Intelligence, including: probabilistic inference, planning and search, localization, tracking and control, all with a focus on robotics. Extensive programming examples and assignments will apply these methods in the context of building self-driving cars. 9. Robotic vision (QUT) Robotic vision introduces you to the field of computer vision and the mathematics and algorithms that underpin it. You’ll learn how to interpret images to determine the color, size, shape and position of objects in the scene, and you’ll build an intelligent vision system that can recognize objects of different colors and shapes. 10. Applied Robot Design for Non-Robot-Designers (Stanford) In Applied Robot Design for Non-Robot-Designers you will learn how to design and build the mechanical hardware of robots. The goal is to take people with no mechanical experience and teach them to build professional-quality robots. The course consists of weekly labs and a final project, each of which will entail building an interesting robotic device. For example, students will build a pantilt camera turret in the belts lab. Topics will include: Electric motors, unusual actuators, sensors, mechanical transmissions, rotary and linear motion, counterbalancing, and standard mechanisms. 11. Introduction to Robotics (Stanford) The purpose of Introduction to Robotics is to introduce you to basics of modeling, design, planning, and control of robot systems. In essence, the material treated in this course is a brief survey of relevant results from geometry, kinematics, statics, dynamics, and control. 12. Introduction to Robotics Specialization (University of Pennsylvania) The Introduction to Robotics Specialization introduces you to the concepts of robot flight and movement, how robots perceive their environment, and how they adjust their movements to avoid obstacles, navigate difficult terrains and accomplish complex tasks such as construction and disaster recovery. You will be exposed to real world examples of how robots have been applied in disaster situations, how they have made advances in human health care and what their future capabilities will be. The courses build towards a capstone in which you will learn how to program a robot to perform a variety of movements such as flying and grasping objects. Sursa: https://hackerlists.com/online-robotics-courses/

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Da-i un mesaj lui @aelius , iți da el ceva bun ( *pe la mustăți )

Ce este ".ss"?

Database Fundamentals Type: Video Lessons Level: Beginner Price: Free Before diving into writing SQL queries, it’s useful to get the 10,000 foot conceptual overview, learn some terminology, and see some examples of relational database tables. Database Fundamentals is a five-part video introduction to core database concepts (by two SQL pros) that explains SQL databases from square one using a mix of lecture content and screencasting. It’s a good place to start for anyone who’s a true beginner or looking to review the fundamental concepts of databases. Stanford’s Self Paced SQL Mini Courses Type: Video Lessons Level: Beginner, Intermediate Price: Free Stanford offers several free SQL mini courses with in-video quizzes and interactive programming exercises that are auto-checked. Every course has a discussion forum and references outside readings & resources. The course material draws from Stanford’s undergraduate courses. essentialSQL Type: Video Lessons, Articles, Community Level: Any Price: Free & Paid Kris Wenzel, creator of essentialSQL, has created a resource rich site. He recommends that you start with his free video course, or dive into some of his beginner text based lessons listed here. There are many thorough text based lessons on the homepage, as well as a learning community. SQL Authority – Video Learning Type: Video Lessons Level: Any, Intermediate Price: Free The owner of SQL Authority, Pinal Dave, is a tech enthusiast and independent consultant that has published 21 courses on Pluralsight and written 11 books on SQL Server. His blog has more articles and videos than you could probably ever get through. Many of the videos are on specific topics that are well beyond beginner level. SQL Server Tutorial for Beginners Type: Video Lesson Level: Beginner, Intermediate Price: Free A treasure trove of 135 short videos showing SQL database concepts using Microsoft SQL Server and SQL Server Management Studio. Pragim Technologies has video lessons of many other languages on their YouTube channel as well. SQL Server Central – Foreign Keys Part 1 & Part 2 Type: Video Lessons Level: Intermediate Price: Free The first big hurdle when learning SQL is understanding the significance of foreign keys and how to use them. These two short videos will give you a background in referential integrity. There are many other videos and resources on SQL Server Central, and although some may look a little dated, the basics of SQL haven’t changed much over the years so they’re still relevant. Microsoft Virtual Academy – SQL Server Courses Type: Video Lessons Level: Any Price: Free Designing Solutions for SQL Server and Developing Microsoft SQL Server Databases are two of the courses offered by Microsoft Virtual Academy. The two courses, as well as others at MVA, offer training on how to implement and manage database solutions, migrate to scalable cloud solutions, use powerful reporting, and integrate SQL with Sharepoint. Sursa: https://hackerlists.com/learn-sql-online/

He,he,he... @oneminute , esti un norocos! Ai dat de @fbob , care mi se pare un baiat competent in domeniul wireless. Daca te pasioneaza acest domeniu , nu-l pierde pe @fbob. Dar fii si tu putin mai inteligent...pune intrebarile tale ceva mai rafinat si o sa-ti raspunda mult mai la subiect.Nu poti sa dai cu parul si sa vrei din prima rezultate . Exerseaza si incearca sa intelegi. Pana una , alta , cateva sfaturi marunte : -wifislax e un instrument perfect pentru wireless. E zglobiu si usor de folosit . Kali e greoi si prea mult de explicat ...e ca si cand ai avea un baros, cand tie iti trebuie doar un ciocanel -in wireless , totul incepe de la handshake...nu vorbim aici de wep , e caduc , vorbim doar de wpa/wpa2 -conteaza foarte mult ce adaptor wireless folosesti si apoi conteaza sa intelegi ce face fiecare aplicatie pe care o folosesti. Chiar nu ma asteptept de la generatia ta sa o ia frumos de la bazele wireless-ului , asa ca , pune draqului un handshake capturat de tine , ca in urma unei analize sumare , sa vedem cam despre ce e vorba.Chestia asta e valabila doar daca ai trecut de un anumit prag

TU. nu vorbim de sistem operativ, ci de cum il folosesti. gandeste'te ca inainte de fluxion/linset/wifiphisher, se lucra cu airbase-ng. invata sa il folosesti, se redirectionezi clientii spre ce pagini vrei. vezi cum functioneaza, da mai ales DACA. si daca nu, de ce. stiu ca e usor sa deschizi un tool, sa pui "./" inainte si sa astepti. dar daca ai impresia ca merge la fel de repede ca in filmatul de pe youtube.. nu e chiar asa. dupa cum vad, timpul nu iti lipseste, asa ca iti dau un pont: foloseste'te de optiunea --berlin in airodump. eu am gasit asa passwords in clar, pe care nici cu cel mai scump rig nu reuseam

Salut, tin sa va anunt ca doar 10 Conturi din toate cele 200 merg asa ca nu va mai obositi sa le verificati.