Programming XII Courses Online

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Programming XII Training


Know Karrigell

About

Karrigell is an open Source Python web framework written in Python The Python 2 version is the stable release. A version for Python 3.2 and above was released in February 2011 Explains "how to" build web applications. This section only applies to the Python3 version This tutorial explains how to build a simple web based application : the example is a CD collection. The home page will show the list of records, with a counter of visits and a "login" link. People who successfully log in will be able to add / edit / remove records The first step is to install Karrigell. Download the latest version, unzip it in a folder, open a console window and in this folder run python Karrigell.py. This will start the built-in web server on port 80

7 hours

$1,990

Learn Lisp Programming

About

Lisp is a programming language. It is named after the collapsed phrase List Processing. If you have programmed before and would like to see a little bit of how Lisp works and is different from other programming languages, you can get an overview. Because Lisp itself is, technically, just seven operators, to become a useful language, much more needs to be implemented atop it. Common Lisp and Scheme are two such designs to create a useful programming language. Common Lisp is an ANSI standard, and features an extensive array of library functions. It is the more widely used of the two. Scheme is designed in a minimalistic fashion, with a very small amount of built in functions. This is probably true, but Scheme lacks many of the time-saving built-in functions of Common Lisp. Emacs Lisp is an implementation of Lisp in Emacs.

7 hours

$1,990

Know mIRC Scripting

About

mIRC Scripting is a built-in, interpreted scripting language for the mIRC IRC client for Windows. It is an easy-to-use and flexible script for uses ranging from automating and simplifying IRC tasks to making mini-programs such as file servers and away systems. Some people even utilize scripts to completely skin and program the IRC interface. These are usually called "full scripts," while individual, smaller scripting projects are known as "remotes" or "addons," or "snippets" for really small scripts. This course will attempt to explain all the aspects of mIRC Scripting, starting from the very basic parts of the language for beginners, and delving into the more advanced topic for experts. If you're looking for something specific in this course, dive into the table of contents and find what you're looking for. 1. Starting From Scratch 2. Learning the Ropes

7 hours

$1,990

Fundamentals of R Programming

About

This course is designed to be a practical guide to the R programming language[1]. R is free software designed for statistical computing. There is already great documentation for the standard R packages on the Comprehensive R Archive Network (CRAN)[2] and many resources in specialized books, forums such as Stackoverflow[3] and personal blogs[4], but all of these resources are scattered and therefore difficult to find and to compare. The aim of This course is to be the place where anyone can share his or her knowledge and tricks on R. It is supposed to be organized by task but not by discipline[5]. We try to make a cross-disciplinary book, i.e. a book that can be used by all people applying statistics to some specific fields. Some rules : We assume that readers have a background in statistics. This course is not a book about statistics but a book about how to implement statistical methods using R. We try to use terms which are already defined on Wikipedia such that people can refer to the corresponding wikipedia page each time they have some doubts on a notion. We also assume that readers are familiar with computers and that they know how to use software with a command-line interface. There are some graphical user interfaces for R but we are not going to explain how to use them in this course. Beginners should have a look at the Sample session for a first session with R. They can also have a look at the Statistical Analysis: an Introduction using R book.

7 hours

$1,990

Know TI 83 Plus Assembly

About

This course teaches TI-83 Plus Assembly Programming, an advanced programming language for TI-83+ and 84+ calculators. The TI-83 and TI-84 use a ZiLOG Z80 microprocessor. (The TI-89 uses a 68000 family processor and the TI-Nspire uses an ARM9 processor[1]). The TI-Nspire features an emulator by Texas Instruments for the TI-84+ SE. It can be used for Z80 programming, but it does not support undocumented commands and is not recommended. More Z80 assembly: Other TI calculators:

7 hours

$1,990

Scriptol

About

C++ syntax: Scriptol syntax: Scriptol doesn't need for semi-colon et end of statements. The end of line is also the end of statement unless several statements fits on a same line, in this case they are separated by semi-colons. If an instruction fits on two lines, the compiler recognizes the instruction. Unlike C that was designed with limited hardware in mind, Perl that has added features day after day, and other languages that depends upon the fantasy of the author, Scriptol apply objectives rules, and it near the more used syntax in computer world, the xml one... Xml is tagged and has as C one-line syntax. Scriptol has one-line syntax, (see above) and is tagged: Some programming languages use the same operator for unrelated things. For example, The C programming language uses the "*" operator for both "deferencing" and "multiply". The C programming language uses the "&" for both "address-of" and "binary and". Scriptol tries to avoid such confusion by making each operator mean something very similar in all the different ways it is used. For example, the range operator " .. " is used:

7 hours

$1,990

Sed

About

sed ("stream editor") is Unix utility for parsing and transforming text files, with ports available on a variety of operating systems. For many purposes, it has been superseded by perl (or the earlier AWK), but for simple transforms in shell scripts, sed retains some use. Sed is line-oriented – it operates one line at a time – and allows regular expression matching and substitution. The most commonly used feature of sed is the s command (“substitution”, or “the s/// construction”), which replaces one pattern with another; this originates in the earlier ed, and retains use in perl. Simply: will replace “cat” by “dog” in file in and output it to file out; the “g” means “global”: replace all matches, not just the first on a given line. One will often wish to use single quotes (' ') to surround the pattern to avoid the shell misinterpreting it:

7 hours

$1,990

Learn Hilics

About

HILICS - Hardware-in-the-loop industrial control system training platform

The Center for Cyberspace Research (CCR) has conducted extensive research in Industrial Control System (ICS) security. ICS architectures utilize a variety of proprietary hardware and software configurations to control and monitor industrial processes and safety systems. Defenders must be familiar with the functionality, requirements and limitations of control systems in order to successfully defend them from cyber-attacks. Hands-on training and experience is crucial for defenders who must be able to interact safely with a given control system. Without this experience, defenders could mistakenly take actions that cause more harm to the system than a basic cyber-attack.
Given the physical nature of ICS architectures, training platforms can be difficult and costly to develop. To address this, the CCR has developed HILICS - a unique hardware-in-the-loop ICS platform designed to support training, education and research. HILICS utilizes a MicroLogix 1100 Programmable Logic Controller (PLC) to introduce students to PLC operating basics and the associated programming languages. This PLC is a low-cost commercial product that provides a representative set of features to give students exposure to control system functionality. The HILICS platform is a custom hardware-in-the-loop system that enables trainers to incorporate multiple physical process simulations and expose students to a range of control system applications. The platform is designed to be flexible and scalable, allowing for varying class sizes and supporting various control system applications.


7 hours

$1,990

Learn LinearRegression01

About

LinearRegression01

the course discusses linear regression model training for economic income and consumption using sklearn and data visualization with matplotlib.

lr_ml.py

Functions in the code are:

def evaluateModel(self, model, test_data, features, labels) # evaluate model def visualizeModel(self, model, data, feature_names, label_names, error, score) # visualize model def trainModel(self, train_data, feature_names, label_names) # train model def linearModel(self, data, feature_names, label_names, split_ratio) # entry def readData(self, path) # read data from csv file


7 hours

$1,990

Discover Algorithm Training Java

About

algorithm-training-java

Techopedia explains Algorithm: An algorithm is a detailed series of instructions for carrying out an operation or solving a problem. In a non-technical approach, we use algorithms in everyday tasks, such as a recipe to bake a cake or a do-it-yourself handbook. Technically, computers use algorithms to list the detailed instructions for carrying out an operation. For example, to compute an employees paycheck, the computer uses an algorithm. To accomplish this task, appropriate data must be entered into the system. I develop myself to learn simple but important algorithms, I write with java.


7 hours

$1,990

Fundamentals of Git Training Kit

About

Git and GitHub Training Kit

Sourced by Active Specialized Support Group (ASS-G)

Index

  1. What Is Version Control?
  2. What Is Git?
  3. Getting Started
  4. Configure tooling
  5. Create repositories
  6. Make changes
  7. Group changes
  8. Review history
  9. Redo commits
  10. Synchronize changes
  11. Workflow Summary

What Is Version Control?

Version control helps developers track and manage changes to a software projects code. As a software project grows, version control becomes essential. Take WordPress At this point, WordPress is a pretty big project. If a core developer wanted to work on one specific part of the WordPress codebase, it wouldnt be safe or efficient to have them directly edit the official source code. Instead, version control lets developers safely work through branching and merging. With branching, a developer duplicates part of the source code (called the repository). The developer can then safely make changes to that part of the code without affecting the rest of the project. Then, once the developer gets his or her part of the code working properly, he or she can merge that code back into the main source code to make it official. All of these changes are then tracked and can be reverted if need be.

What Is Git?

Git is a specific open-source version control system created by Linus Torvalds in 2005. Specifically, Git is a distributed version control system, which means that the entire codebase and history is available on every developers computer, which allows for easy branching and merging. According to a Stack Overflow developer survey, over 87% of developers use Git.


7 hours

$1,990

Know ClassificationProb

About

ClassificationProb

The task at hand is a multi-class classification problem, for which both a training and a test set are provided as csv files.

Writing out the thought process:

  • The first step is to read through and understand what the problem parameters are pointing towards.
  • With the given instructions we know:
    • The problem is classification and not regression
    • The target is 4 possible outcomes
    • data is split into separate train and test csv
  • Our second step will be to clean the data enough to run it through a model
  • Then we establish a baseline for predictions
  • Next step will be to quickly get a non-engineered Logistic Regression up
  • From there we will begin to examine data to see if feature engineering is a viable strategy
  • Begin to explore data visually

7 hours

$1,990

Learn Open Go Bot

About

A platform for designing, training and sharing AI models

Allows to design deep-learnings models for various problems, training them in the browser and sharing them with others. This project is still work in progress. That means some important features are still missing:

  • Deployment to Cloud (AWS or GCE)
  • OpenAuth integration
  • Sync models (inclusive weights) and share
  • Generic architecture in order to support multiple scenarios Finished features:
  • Mnist scenario
  • Inspecting weights and activations
  • Editing graphs

    Scenarios

    MNIST

    A large database of handwritten digits that is commonly used for training various image processing systems.


7 hours

$1,990

Work around Crowdastro

About

crowdastro

This project aims to develop a machine learned method for cross-identifying radio objects and their host galaxies, using crowdsourced labels from the Radio Galaxy Zoo. |PyPI| |Travis-CI| |Documentation Status| |DOI| For setup details, see the documentation on Read the Docs. For a brief description of each notebook, see the documentation here. The cross-identification dataset is available on Zenodo.


7 hours

$1,990

Learn Api Design

About

Application Programming Interface

Modern applications use web API to communicate together. Web APIs point out a way to communicate through a protocol over a socket (http, websocket, protobuf) synchronously or asynchronously. IT industry uses many standards or protocols : SOAP/XML, XML-RPC, RESTfull/JSON, REST-RPC/JSON, ... The last one is the most used nowadays but there also emerging tehcnologies : Facebook GraphQL, Netflix Falcor or Google grpc.io are gaining adepts since few years During this training we will learn to create a REST/Json API with Node.js, serialiaze data in a database, secure your API and deploy it on a PaaS provider.

Specification :

Our project is to build a simple blog backend.


7 hours

$1,990

Explore Mmtf Workshop 2017

About

Structural Bioinformatics Training Workshop & Hackathon 2017

Application of Big Data Technology and 3D Visualization, San Diego Supercomputer Center, UC San Diego, June 26-28, 2017 This 3-day hands-on workshop introduces participants to the development of fast and scalable structural bioinformatics methods using state-of-the-art Big Data technologies and Web-GL 3D visualization (). The first two days of the workshop combine lectures, hands-on applications, and programming sessions. On the third day participants apply the new technologies to their own projects. This workshop is held at the University of California, San Diego and hosted by the Structural Bioinformatics Laboratory at SDSC in collaboration with the RCSB Protein Data Bank. Workshop registration page:
(registration is closed) A second workshop will be held early 2018. For question about this workshop or to preregister for the second workshop:

Workshop Outcomes

Software Installation


7 hours

$1,990

Work around ANN On Churn Modelling Dataset Using Keras

About

Artificial Neural Network

I implemented a simple ANN on the Churn Modelling Dataset and then after training tested on a single data to predict values The idea of ANNs is based on the belief that working of human brain by making the right connections, can be imitated using silicon and wires as living neurons and dendrites. The human brain is composed of 86 billion nerve cells called neurons. They are connected to other thousand cells by Axons. Stimuli from external environment or inputs from sensory organs are accepted by dendrites. These inputs create electric impulses, which quickly travel through the neural network. A neuron can then send the message to other neuron to handle the issue or does not send it forward. Structure of Neuron ANNs are composed of multiple nodes, which imitate biological neurons of human brain. The neurons are connected by links and they interact with each other. The nodes can take input data and perform simple operations on the data. The result of these operations is passed to other neurons. The output at each node is called its activation or node value. Each link is associated with weight. ANNs are capable of learning, which takes place by altering weight values. The following illustration shows a simple ANN A Typical ANN Types of Artificial Neural Networks There are two Artificial Neural Network topologies FeedForward and Feedback. FeedForward ANN The information flow is unidirectional. A unit sends information to other unit from which it does not receive any information. There are no feedback loops. They are used in pattern generation/recognition/classification. They have fixed inputs and outputs. FeedForward ANN FeedBack ANN Here, feedback loops are allowed. They are used in content addressable memories. FeedBack ANN Working of ANNs In the topology diagrams shown, each arrow represents a connection between two neurons and indicates the pathway for the flow of information. Each connection has a weight, an integer number that controls the signal between the two neurons. If the network generates a good or desired output, there is no need to adjust the weights. However, if the network generates a poor or undesired output or an error, then the system alters the weights in order to improve subsequent results. Machine Learning in ANNs ANNs are capable of learning and they need to be trained. There are several learning strategies Supervised Learning It involves a teacher that is scholar than the ANN itself. For example, the teacher feeds some example data about which the teacher already knows the answers. For example, pattern recognizing. The ANN comes up with guesses while recognizing. Then the teacher provides the ANN with the answers. The network then compares it guesses with the teachers correct answers and makes adjustments according to errors. Unsupervised Learning It is required when there is no example data set with known answers. For example, searching for a hidden pattern. In this case, clustering i.e. dividing a set of elements into groups according to some unknown pattern is carried out based on the existing data sets present. Reinforcement Learning This strategy built on observation. The ANN makes a decision by observing its environment. If the observation is negative, the network adjusts its weights to be able to make a different required decision the next time. Back Propagation Algorithm It is the training or learning algorithm. It learns by example. If you submit to the algorithm the example of what you want the network to do, it changes the networks weights so that it can produce desired output for a particular input on finishing the training. Back Propagation networks are ideal for simple Pattern Recognition and Mapping Tasks.


7 hours

$1,990

Explore FlashCards

About

Flash Cards

A mobile app for long-term memory training using sets of flash cards.

Future Development

  • Create decks based on subject

  • Combine & shuffle multiple decks together for the student to train long-term memory

  • Use text and images on one side of the card to convey an idea to the student, and have the answer waiting for them on the back of the card in plain text

  • Shuffle cards on demand

  • Set up a per-card timer to force the student to answer quickly

  • Track training history

    • correct/incorrect answers

    • avg. card display time

    • card display time as a graph for the entire deck

    • individual card display count


7 hours

$1,990

Basics of Numnormalize

About

Number normalize

Instalation

$ npm i numnormalize --save The need for the normalization of data samples is conditioned by the very nature of the variables used in neural network models. Being different in physical sense, they can often differ greatly in absolute values. So, for example, the sample can contain both concentration, measured in tenths or hundredths of percent, and pressure in hundreds of thousands of pascals. Normalization of data allows you to bring all used numerical values of variables to the same area of their change, which makes it possible to bring them together in one neural network model. In order to normalize the data, you need to know exactly the limits of the changes in the values of the corresponding variables (minimum and maximum theoretically possible values). Then the limits of the normalization interval will correspond to them. When it is impossible to set the limits of variable changes precisely, they are set taking into account the minimum and maximum values in the available data sample.


7 hours

$1,990

Learn Snapshot Training

About

Snapshot Training for Jest

Based on examples provided by the Jest project, this project will teach you about how to use snapshot testing and how we can automate our assertions using it.

Enzyme: Rule of thumb

  • Shallow Rendering: Always start with shallow()
  • Full Rendering: Use mount() for lifecycle methods and optionally children
  • Static Rendering: Use render() to test children with if you don't care about lifecycle methods

    Snapshots can be used to test

  • Text
  • Objects
  • Arrays
  • DOM

7 hours

$1,990

Explore Runner Training

About

Runner-Training

Overview

It is important for runners to get adequate training before taking on long distance running events. Finding a training plan to match your goal distance is a good first step. However, even after youve found a good training plan, it is sometimes difficult to get motivated to run some of the long runs. That is when it can be helpful to find local races or running groups to run with. I am creating an app that will help users find training plans to match their goals. In addition, this app will help users find races and running groups that fit into their training schedule. Each user will have a personal calendar populated with their training runs as well as any race or running group options available. This idea stems from how I build my training schedule for ultramarathons. I have run distances of 50 and 100 miles using this strategy.

Features


7 hours

$1,990

Discover Person Detection

About

Person Identification using Opencv library

Person detection program learns to identify a individual by learning on the training data provided. It then is able to predict the person correctly when a new image of the person is provided as a test data.

Primary libraries used:

  1. opencv2
  2. tkinter
  3. PIL

    Folder structure:

    There are folders:

  4. training-data
  5. test-data Inside training data we need to create folders with prefix "person". Example: "person1" folder can contain my images for it to train. "person2" folder may contain a second person's images and so on.

7 hours

$1,990

Learn pyst Python for Asterisk

About

Pyst consists of a set of interfaces and libraries to allow programming of Asterisk from python. The library currently supports AGI, AMI, and the parsing of Asterisk configuration files. The library also includes debugging facilities for AGI.


7 hours

$1,990

Know HCEnc Provider

About

HCEnc Provider is a program that interacts with the free mpeg2 cutting software Cuttermaran to make it support HCEnc (free mpeg2 encoder) for encoding the cut files.


7 hours

$1,990

Explore Gspoof

About

Gspoof is a GTK+ program written in C language which makes easier and accurate the building and the sending of TCP packet with a data-payload or not. It's possible to modify TCP/IP fields also Ethernet header working to Link Level.


7 hours

$1,990


Is learning Programming XII hard?


In the field of Programming XII learning from a live instructor-led and hand-on training courses would make a big difference as compared with watching a video learning materials. Participants must maintain focus and interact with the trainer for questions and concerns. In Qwikcourse, trainers and participants uses DaDesktop , a cloud desktop environment designed for instructors and students who wish to carry out interactive, hands-on training from distant physical locations.


Is Programming XII a good field?


For now, there are tremendous work opportunities for various IT fields. Most of the courses in Programming XII is a great source of IT learning with hands-on training and experience which could be a great contribution to your portfolio.



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