Tools in Programming Courses Online

Live Instructor Led Online Training Tools in Programming courses is delivered using an interactive remote desktop! .

During the course each participant will be able to perform Tools in Programming exercises on their remote desktop provided by Qwikcourse.


How do I start learning Tools in Programming?


Select among the courses listed in the category that really interests you.

If you are interested in learning the course under this category, click the "Book" button and purchase the course. Select your preferred schedule at least 5 days ahead. You will receive an email confirmation and we will communicate with trainer of your selected course.

Tools in Programming Training


R Training Program

About

R Training Using Company Data

This training on R uses company data to better learn how R can be used at the company.

I. Content

There are 7 lessons in this training, and 3 problem sets to be completed after the training.

A. Lessons

During lessions, notes are reviewed and the remaining time is used to work on 3 exercises. Solutions to the exercises are handed out at the end of the session. Below times are for groups with no prior R/programming experience. (Times for groups with prior R/programming experience will be about 45 min to an hour less.)

  1. Intro to R (2 hours)
  2. Base R (2 hours) -- includes data extraction (Vertica and flat files) and combining dataframes
  3. dplyr (3 hours) -- data manipulation with dplyr
  4. ggplot2 (3 hours) -- data visualization with ggplot2
  5. Other Packages for Data Processing (3 hours) -- brief introductions to data.table, reshape2, and stringr
  6. R Markdown (2 hours) -- reproducible, formatted analysis
  7. Company R Package (2 hours) -- using the company R package to query databases, format graphs, generate PowerPoints and send emails

    B. Problem Sets (not included here)


7 hours

1,656 €

Ruby Essentials Training

About

Ruby Essentials Training

Ruby is a dynamic, open source programming language with a focus on simplicity and productivity. It has an elegant syntax that is natural to read and easy to write.

Output "I love Ruby"

say = "I love Ruby" puts say

Output "I LOVE RUBY"

say['love'] = "love" puts say.upcase

Output "I love Ruby"

five times

5.times { puts say }

About Ruby

The Ideals of Rubys Creator Ruby is a language of careful balance. Its creator, Yukihiro "Matz" Matsumoto, blended parts of his favorite languages (Perl, Smalltalk, Eiffel, Ada, and Lisp) to form a new language that balanced functional programming with imperative programming. He has often said that he is "trying to make Ruby natural, not simple," in a way that mirrors life.


7 hours

1,656 €

Elevator MARL

About

Elevator-MARL

Asynchronous Elevator Control Simulator + Multi-Agent Reinforcement Learning training algorithms

Simulator

  • Elevators: are modeled as independent agents.

  • Elevator States: Motion state: MOVING, IDLING; Intent state: IDLE_INTENT_IDLE, IDLE_INTENT_UP, IDLE_INTENT_DOWN

  • Observation Space: elevator position, carrying weight, hall call status, and in-elevator requests.

  • Decision Epoch: Two events. Elevator Arrival or Loading Finished. Elevator Arrival is triggered whenever an elevator reaches a floor, or the previous idling event is finished. Loading Finished is triggered when the loading proccess is done and the elevator is ready to take the next action.

  • Action Space: If the elevator is moving when Elevator Arrival is triggered, then it needs to decide if it wants to stop on the next floor.

    If the elevator is IDLING when Elevator Arrival is triggered, then it needs to choose a intended moving direction and then a passenger loading event is queued by the environment (intent needs to be declared so that passengers in the waiting area can decide whether they want to enter the elevator or not). When the Loading Finished event is triggered, the elevator needs to choose an action among actions that correspond to its' declared intent.(For example, if the intent was MOVING_UP, then it choose between IDLE_UP_IDLE, or IDLE_UP_MOVE, basically whether it wants to stop at the next floor up or not. This changes how much time it takes to move to the next floor.)
    Error will be thrown if illegal action is passed into the environment. Otherthan that the environment does not impose any constraint on what the elevators can choose to do.


7 hours

1,656 €

Watson Mini Hack Training

About

Watson Mini Hack Training

This project is used to support Watson training sessions at various clients conducted by IBM Developer Advocates. Because this is a public repo, it contains no client specific information. Note however that the content may be updated over time to support different training sessions. It is therefore recommended that you clone the project at the time of your training session so the material will match the agenda of your session.

Watson Services Guide for Developers

The Watson Services Guide for Developers is a summary of the information presented at the training session. It contains links to relevant information and resources and has a one page summary of each Watson API.

Labs

These are the instructions and all supporting files for the hands-on-lab exercises. The hands on lab exercises are:

Lab 1 - Watson Assistant (formerly Watson Conversation)

In this lab, you will familiarize yourself with the Watson Assistant service tooling by developing a chatbot from scratch and then extending an existing chatbot. Follow the instructions at the link below to complete the lab.

Lab 2 - Watson Visual Recognition


7 hours

1,656 €

Word Training

About

Word training

This application helps to remember foreign language words. All words in the application are divided into dictionaries. The person with admin privileges is allowed to access admin panel and create/edit/delete dictionaries. Dictionary can be filled with words manually, specifying word's spelling, transcription, translation and pronounce audio. There are several ways to specify pronounce: type url to audio file, choose audio via file browsing dialog, record audio on microphone or select AUTO option. When adding word to the dictionary, only spelling attribute is required, others can be loaded automatically via configured loaders. Besides, list of words with spelling can be imported from text file or from text, pasted in the appropriate dialog. In the public area we start by choosing one of the available dictionaries and go to the page with all words in the dictionary. After browsing words and learning them, we can strengthen our knowing by doing several exercises. There are two types of exercises: choosing right answer from available ones or typing answer. Words, which will be used in the exercise depends on the selected package. Packages are divided on local and global packages. Local ones can include words from the current dictionary only. Global packages can include words from all available packages. Initially, for each user, including unauthorized, there are only two preset packages available: "current dictionary words" and "all dictionaries words". They can not be modified. Additionally, there is an option for creating own package to the authorized users.


7 hours

1,656 €

Kerasbestfit

About

Introducing kerasbestfit This is a Python module that I wrote to make training and finding the best Keras model easier. The module uses Keras's EarlyStopping and Checkpoint callbacks so that you just need to call one function and it finds and saves the model that has the best metrics. The advantage to using this module is that you can just let it run and you'll find the best model in your folder where you can later use it for predictions. It can also stop after a certain duration so that you can fit the training session into your provider time limit. There is even a snifftest parameter where you can skip training an iteration that has very poor performance, thus saving training time. Why did I create a new module instead of using Sklearn, T-Pot or something else to find the best fit? Because it made me dig into Keras to understand it better. Simple as that. The result is a pretty nifty function. Installation pip install kerasbestfit Usage

train with 20 epochs with patience of 3, save the model structure and weights, and show the best so far (bsf)

def log_msg(msg=''): print(msg) return

results, log = kbf.find_best_fit(model=model, metric='val_acc', xtrain=train_images, ytrain=train_labels, xval=test_images, yval=test_labels, validation_split=0, batch_size=500, epochs=20, patience=3, snifftest_max_epoch=0, snifftest_metric_val=0, show_progress=True, format_metric_val='{:1.10f}', save_best=True, save_path='', best_metric_val_so_far=0, logmsg_callback=log_msg, finish_by=0)

Output

here we can see that it aborted after 12 epochs since it went 3 epochs without a better result.

best result is 0.9745000005

e0: val_acc=0.9284999937 ! bsf=0.9284999937 Saved e1: val_acc=0.9526999980 ! bsf=0.9526999980 Saved e2: val_acc=0.9618999988 ! bsf=0.9618999988 Saved e3: val_acc=0.9645999998 ! bsf=0.9645999998 Saved e4: val_acc=0.9637999952 bsf=0.9645999998 e5: val_acc=0.9687000036 ! bsf=0.9687000036 Saved e6: val_acc=0.9700999945 ! bsf=0.9700999945 Saved e7: val_acc=0.9718000025 ! bsf=0.9718000025 Saved e8: val_acc=0.9741999984 ! bsf=0.9741999984 Saved e9: val_acc=0.9745000005 *! bsf=0.9745000005 Saved e10: val_acc=0.9736999959 bsf=0.9745000005 e11: val_acc=0.9718000025 bsf=0.9745000005 e12: val_acc=0.9694999963 bsf=0.9745000005


7 hours

1,656 €

Discover Caffe2 Utility

About

Caffe2 Utility Module. This module contains various methods that are generally used for organizing and training and inference.

Content

  • Requirements

  • Usage

  • Example


7 hours

1,656 €

Antinex Datasets

About

Datasets for Training Defensive Neural Networks

These are the Anti-Nex datasets for training deep neural networks to defend against network exploits (only web applications added so far). Under the hood these models are trained and tuned using Keras and Tensorflow. These datasets were created by merging datasets recorded from OWASP ZAP attack simulations and multi-user non-attack simulations with Django. Each model has an associated guide for preparing datasets and training models: This is a 75 MB gif I recorded while capturing the non-attack training data by running the multi-user simulation with the capture tools all active inside a single vm (note: from inside imgur, right click on the gif and select Open in video in new tab to make it larger to read the text):


7 hours

1,656 €

NumberCrunch

About

Number Crunch

Number Crunch is a mental math training program. The program displays three digits with two blanks in between the digits, signifying the missing operators. To the right side of the equals sign is the result. The goal is to type in the first operator followed immediately by the second operator to satisfy the equation.


7 hours

1,656 €

Pixy

About

Pixy is a lightweight application for generating image datasets intended for downstream machine vision applications. With a few image labeling functions, pixy is able to achieve near human accuracy for image turking. 

Requirements

Pixy depends on DeepBox, pycaffe, caffe, snorkel, and vlfeat. Instructions to install each of these libraries can be found online. It is highly recommended to install these libraries on Ubuntu.

TODO:

Automate installation requirements and make a user-abstracted interface


7 hours

1,656 €

Simon Sings

About

SIMON SINGS Simon Sings is a fun music and memory training game for the iPhone. Learn to sing, improve your hearing, and train your memory... at the same time! Simon plays a sequence of musical notes that you have to memorize and then repeat by singing, humming or whistling into the iPhone's microphone. With each new round the sequence becomes longer and harder to remember. Start in Beginner mode with only 3 possible notes, move up to Intermediate with 6 notes, and finally become a Maestro by mastering all 12 notes of the musical alphabet.


7 hours

1,656 €

AdventOfCode2018

About

AdventOfCode2018

Advent of Code is an Advent calendar of small programming puzzles for a variety of skill sets and skill levels that can be solved in any programming language you like. People use them as a speed contest, interview prep, company training, university coursework, practice problems, or to challenge each other. These are my Python code examples solving the daily challenges.


7 hours

1,656 €

TorchRecord

About

TorchRecord

TorchRecord can merge the small files including images and labels into one or multiple big record file to improve the copying and reading performance. TorchRecord use LMDB as the storage database. A specific writer and loader can be used to write and read the record.

Reading Performance Benchmark

Dataset: CUB200 datasets(11788 jpg images)

Load Image and transform them to tensor (batch_size: 32)

Env: Intel(R) Xeon(R) CPU E5-2603 0 @ 1.80GHz 4core with 32 GB Mem Conventional: 100%|| 369/369 [00:42


7 hours

1,656 €

SGD Neural Network

About

Title

Implementation of a fully connected feed-forward neural network using the back propagation algorithm for stochastic batch gradient descent computations. Network size is adoptive and supports MLP.

Features

Activations: softmax, sigmoid
Loss functions: log-likelihood, mean-square, cross-entropy(binary equivalent of log-likelihood) Regularization: L2 Validation - Takes labels and data as input Hyper-parameters: learning-rate(eta), regularization-parameter(lambda), epochs


7 hours

1,656 €

Discover Captcha22

About

About

CAPTCHA22 is a toolset for building, and training, CAPTCHA cracking models using neural networks. These models can then be used to crack CAPTCHAs with a high degree of accuracy. When used in conjunction with other scripts, CAPTCHA22 gives rise to attack automation; subverting the very control that aims to stop it.

Contents

  • Installation

    • Prerequisites

  • Usage: How to crack CAPTCHAs

    • Step 1: Creating training sample data (labelling CAPTCHAs)

    • Step 2: Training a CAPTCHA model

    • Step 3: CAPTCHA Cracking

  • Troubleshooting

  • Contributing

  • License


7 hours

1,656 €

Msgraph Training Dataconnect

About

Microsoft Graph training module - Microsoft Graph data connect

This module introduces you to Microsoft Graph data connect.

Lab - Using Microsoft Graph data connect to analyze to find subject matter experts

In this lab you will use Microsoft Graph data connect to analyze emails from an organization in Office 365 to find subject matter experts on specific topics.


7 hours

1,656 €

Vs Intellicode

About

Automate code completions tailored to your codebase with IntelliCode Team completions

With this GitHub Action, you can keep your Team completion suggestions up-to-date with your repositorys latest commit by automating the Team completions model training.

Requirements

  • The build agent (MSBUILD, CMAKE) has the minimum required Visual Studio version installed: For C# repositories: Visual Studio 2017 or higher For C++ repositories: Visual Studio 2019 Update 4 or higher.


7 hours

1,656 €

Ddhi Encoder

About

A collection of command-line utilities to assist in the creation of TEI-encoded oral history interviews. Part of the Dartmouth Digital History Initiative. DDHI Encoder

The ddhi-encoder package is being developed to assist encoders in the DDHI project in encoding oral history interview transcripts in TEI. At present, it contains two command-line utilities:

. ddhi_convert: convert a Dartmouth DVP transcript from docx to

tei.xml.

. ddhi_tag: perform named-entity tagging on a DDHI TEI

transcription.


7 hours

1,656 €

Simulator

About

Simulator

A distributed systems and infrastructure simulator for attacking and debugging Kubernetes: simulator creates a Kubernetes cluster for you in your AWS account; runs scenarios which misconfigure it and/or leave it vulnerable to compromise and trains you in mitigating against these vulnerabilities. For details on why we created this project and our ultimate goals take a look at the vision statement.


7 hours

1,656 €

NSFW Data Scraper

About

This is a set of scripts that allows for an automatic collection of tens of thousands of images for the following (loosely defined) categories to be later used for training an image classifier: Here is what each script (located under scripts directory) does: Note: I already ran this script for you, and its outputs are located in raw_data directory. No need to rerun unless you edit files under scripts/source_urls.


7 hours

1,656 €

Training Noodles

About

Training Noodles

A simple and powerful tool to help training multiple programs on multiple servers with only one human.

Features

  • Automatically deploys experiments to available servers

  • No need to change any existing code

  • Considers CPU usage, GPU usage, memory usage, disk usage, and more

  • Uses only SSH protocol

  • Relies on minimal dependencies

  • Allows fast prototyping


7 hours

1,656 €

Learn ModelZoo.pytorch

About

ModelZoo for Pytorch - this is a model zoo project under Pytorch. In this course, we will implement some of basic classification models which have good performance on ImageNet. Then I will train them in most fair way as possible and try my best to get SOTA model on ImageNet. 

Content

  • Introduction
  • Usage
  • Requirement
  • LMDB Dataset
  • Baseline Models
  • Ablation study on tricks
  • Citation

7 hours

1,656 €

URI

About

URI Online Judge Solutions

Problems solved in the URI Online Judge platform for training in programming contests.

About

These are my solutions to URI Online Judge problems. Whenever possible I will try to add more solutions to this repository. Most problems are solved in C++, but it is also possible to find solutions in Python and Haskell. The URI Online Judge is a project that is being developed by the Computer Science Department of URI University. The main goal of the project is to provide programming practice and knowledge sharing. It contains more than 1,000 problems divided in 8 big categories. This division help the users to focus on specific programming topics. All problems are available in Portuguese and English. The URI Online Judge website also has public contests on a regular basis.

Why?

This repository is part of my personal studies and I would be very happy to receive feedback on the solutions, code, structure, anything that can make me a better developer! E-mail: gabrielcaetanodm@gmail.com | LinkedIn: gabrielcaetanodm In addition, you can use these solutions however you want, they are free to copy, change and improve. They are available to help the community, especially for beginning programmers and also for those who are training for programming competitions. You can also check out my URI Online Judge profile by clicking here.


7 hours

1,656 €

Workshops

About

Red Hat Ansible Automation Platform Workshops

The Red Hat Ansible Automation Workshops project is intended for effectively demonstrating Ansible's capabilities through instructor-led workshops or self-paced exercises.

Website

Instructor-led Workshops

 

6 hour workshops:

---

---

---

---

90 minute abbreviated versions:

Lab Provisioner

Self Paced Exercises


7 hours

1,656 €

Constellation Training

About

Constellation Training

graph-focused data visualisation and interactive analysis application enabling data access, federation and manipulation capabilities across large and complex data sets.

Analyst Training

The Analyst Training folder contains a series of self paced exercises you can follow to become fluent with using Constellation starting with the basics of "why graphs?" in exercise 1 and move up all the way up to doing network analysis with Constellation and Python in exercise 10. Answers to exercises are also provided to verify your learning.

  • Exercise 1 - Why Graphs

  • Exercise 2 - Introduction to Constellation

  • Exercise 3 - Histogram View

  • Exercise 4 - Selecting in Constellation

  • Exercise 5 - Putting it Together - Checkpoint

  • Exercise 6 - Map View and Location Data

  • Exercise 7 - Conversation View and Content Data

  • Exercise 8 - Analytic View and Social Network Analysis

  • Exercise 9 - Clustering and Similarity

  • Exercise 10 - Network Analysis With Python If you have any feedback or have questions then feel free to submit an issue to to you. We also welcome any contribution to exercises.

    Developer Training

    Constellation provides significant capability out of the box, it has been designed with extensibility and modularity in mind. There are two prominent ways of extending Constellation, namely through its views or by creating plugins. Views appear as windows in the application and provide visualisation of and interaction with the graph. Plugins perform operations on the graph. In addition, there are several other ways that Constellation can be customised, including defining new attributes or even your own custom graph. This guide will introduce you to the most common ways to customise Constellation in order to solve domain-specific problems. Each chapter will introduce a new concept interspersed with a series of practical exercises. These exercises will


7 hours

1,656 €

Sagemaker Experiments

About

Experiment tracking in SageMaker Training Jobs, Processing Jobs, and Notebooks. Overview SageMaker Experiments is an AWS service for tracking machine learning Experiments. The SageMaker Experiments Python SDK is a high-level interface to this service that helps you track Experiment information using Python. Experiment tracking powers the machine learning integrated development environment Amazon SageMaker Studio. For detailed API reference please go to: Read the Docs Concepts For more information see Amazon SageMaker Experiments - Organize, Track, and Compare Your Machine Learning Trainings_


7 hours

1,656 €

SwiftNNTrainer

About

SwiftNNTrainier is a program for training neural networks using Apples' Metal Performance Shader library. The program allows you to define a data source, create a network topology, and test and/or train the network using that data.

Latest Added Features

  • Importing and exporting some models to/from MLModel files. You can now use SwiftNNTrainer to create mlmodel files for use in other applications.
  • Scaling of 3D View X axis
  • Model parameter counting
  • Labels are now stored as part of the document. This allows labels to be loaded/saved from/to MLModel files
  • Document file version is now 2 - so document files written by this version will not be able to be read by older versions.

7 hours

1,656 €


Is learning Tools in Programming hard?


In the field of Tools in Programming 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 Tools in Programming a good field?


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



Tools in Programming Online Courses, Tools in Programming Training, Tools in Programming Instructor-led, Tools in Programming Live Trainer, Tools in Programming Trainer, Tools in Programming Online Lesson, Tools in Programming Education