Machine Learning Courses Online

Live Instructor Led Online Training Machine Learning courses is delivered using an interactive remote desktop! .

During the course each participant will be able to perform Machine Learning exercises on their remote desktop provided by Qwikcourse.


How do I start learning Machine Learning?


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.

Machine Learning Training


Discover Brainwave

About

Brainwave is a notification system for neural network training. If you ever find yourself having to repeatedly check on model training progress on a (remote) PC, then this may be a useful tool. Brainwave sends you mobile notifications about the training progress, so you may stay informed while enjoying other things.

Backends

Brainwave currently leverages the following service providers, and implements them as backends for sending notifications.

  • Amazon Simple Notification Service (SNS): for sending text messages to phones
  • Amazon Simple Email Service (SES): for sending emails
  • SendGrid: for sending emails

There are free tiers for above services up to certain limits.

Content

  • Installation
  • Usage
  • Examples

7 hours

$1,990

Deep Dashboard

About

Deep Dashboard

A better visualization tool for training machine learning models.

Introduction

Tired of watching un-informative command line console? Tired of the line limit of ssh screen? Deep dashboard helps you visualize the training process better, and provides with more diagnostics.

 


7 hours

$1,990

Discover Tensorfx

About

TensorFX is an end to end application framework to simplifies machine learning with designed from the ground up to make the mainline scenarios simple with higher level building blocks, while ensuring custom or complex scenarios remain possible by preserving the flexibility of TensorFlow APIs. There are some important principles that shape the design of the framework:

  1. Simple, consistent set of usage patterns Local or cloud, single node or distributed execution, in-memory data or big data sharded across files, you should have to write code once, in a single way regardless of how the code executes.
  2. A Toolbox with Useful Abstractions The right entrypoint for the task at hand, starting with off-the-shelf algorithms that let you focus on feature engineering and hyperparam tuning. If you need to solve something unqiue, you can focus on building TensorFlow graphs, rather than infrastructure code (distributed cluster

14 hours

$3,980

Architecting For Machine Learning

About

Architecting For Machine Learning

Welcome to the art and science of machine learning! During this course you will learn about the theory and application of machine learning in industry. This course is designed for architects and developers who did not previously have a background in AI/ML, providing intuition and confidence in designing ML applications. We will cover: As a prerequisite to attending this course, we recommend reviewing Python programming using the statistical package Pandas.


7 hours

$1,990

Discover PytorchDeepML

About

PytorchDeepML - this library is a wrapper around PyTorch and useful for solving image classification and semantic segmentation problems.

Features

  1. Easy to use wrapper around PyTorch so that you can focus on training and validating your model.
  2. Integrates with Tensorboard to use it to monitor metrics while model trains.
  3. Quickly visualize your model's predictions.

Content

  • Installation
  • Usage
  • Examples

7 hours

$1,990

Discover HarborML

About

HarborML is a framework for building, training, and deploying machine learning and AI solutions via containers.

Content

  • Installation
  • Dependencies
  • Basics
  • Sample projects

7 hours

$1,990

Basic Statistics For Machine Learning

About

Knowing statistics helps you build robust Machine Learning models that are optimized for a given drawback statement. this course can teach you all it takes to perform advanced applied math computations needed for Machine Learning. you may gain data on statistics behind supervised learning, unattended learning, reinforcement learning, and more. perceive the real-world examples that debate the applied math aspect of Machine Learning and familiarise yourself with it. you may conjointly style programs for playing tasks like a model, parameter fitting, regression, classification, density assortment, and more.

Content

  • Statistical and Machine Learning basics
  • The Machine Learning method in statistics
  • Machine Learning algorithms
  • Supervised and unsupervised deep learning models
  • Reinforcement learning in artificial intelligence

14 hours

$3,980

Discover Neural Networks With TensorFlow 2.0

About

Tensor processing unit is a Google-developed coprocessor for accelerating neural networks tensor processing unit is developed by Google

Content

  • Machine learning and neural network techniques
  • Features of TensorFlow 2.0 
  • TensorFlow Datasets (tfds) and the tf.data API 
  • TensorFlow Hub
  • Train Generative Adversarial Networks (GANs)
  • SavedModel file format

21 hours

$5,970

Machine Learning with MATLAB

About

MATLAB is Numerical computing environment and programming language It is a application. MATLAB is developed by MathWorks and Cleve Moler. It is designed by Cleve MolerSupported by Microsoft Windows, macOS and GNU/Linux Operating Systems.

Content

  • Introduction to machine learning.
  • SAS XPORT -  import and export tools,
  • Types of regression techniques 
  • Classification methods, Naive Bayes algorithm, and Decision Trees in Matlab environment.
  • Clustering methods
  • MATLAB Neural Network Toolbox.

21 hours

$5,970

Discover Swix (Swift Matrix)

About

Swift Matrix and Machine Learning Library

Apple's Swift is a high level language that's asking for some numerical library to perform computation fast or at the very least easily. This is a bare-bones wrapper for that library.

A way to have iOS run high-level code similar to Python or Matlab is something I've been waiting for, and am incredibly excited to see the results. This will make porting complex signal processing algorithms to C much easier. Porting from Python/MATLAB to C was (and is) a pain in the butt, and this library aims to make the conversion between a Python/Matlab algorithm and a mobile app simple.

In most cases, this library calls [Accelerate][accel] or [OpenCV][opencv]. If you want to speed up some function or add add another feature in those libraries, feel free to file an issue or submit a pull request (preferred!). Currently, this library gives you

  • operators and various functions (sin, etc) that operate on entire arrays

  • helper function (reshape, reverse, delete, repeat, etc)

  • easy initializers for 1D and 2D arrays

  • complex math (dot product, matrix inversion, eigenvalues, etc)

  • machine learning algorithms (SVM, kNN, SVD/PCA, more to come)

  • one dimensional Fourier transforms

  • speed optimization using Accelerate and OpenCV

Content

  • Installation
  • Third-Party Frameworks / Libraries
  • Uses
  • FAQs

 

 

 

 


7 hours

$1,990

TensorFlow Data Validation

About

TensorFlow Data Validation (TFDV) is a library for exploring and validating machine learning data. It is designed to be highly scalable and to work well with TensorFlow and TensorFlow Extended (TFX). TF Data Validation includes:

  • Scalable calculation of summary statistics of training and test data.
  • Integration with a viewer for data distributions and statistics, as well as faceted comparison of pairs of features (Facets)
  • Automated data-schema generation to describe expectations about data like required values, ranges, and vocabularies
  • A schema viewer to help you inspect the schema.
  • Anomaly detection to identify anomalies, such as missing features, out-of-range values, or wrong feature types, to name a few.
  • An anomalies viewer so that you can see what features have anomalies and learn more in order to correct them. For instructions on using TFDV, see the get started guide and try out the example notebook. Some of the techniques implemented in TFDV are described in a Caution: TFDV may be backwards incompatible before version 1.0.

7 hours

$1,990

Discover Xai

About

XAI - An eXplainability toolbox for machine learning. XAI is a Machine Learning library that is designed with AI explainability in its core. XAI contains various tools that enable for analysis and evaluation of data and models. The XAI library is maintained by The Institute for Ethical AI & ML, and it was developed based on the 8 principles for Responsible Machine Learning. 

What do we mean by eXplainable AI?

We see the challenge of explainability as more than just an algorithmic challenge, which requires a combination of data science best practices with domain-specific knowledge. The XAI library is designed to empower machine learning engineers and relevant domain experts to analyse the end-to-end solution and identify discrepancies that may result in sub-optimal performance relative to the objectives required. More broadly, the XAI library is designed using the 3-steps of explainable machine learning, which involve 1) data analysis, 2) model evaluation, and 3) production monitoring.

Content

  • Installation
  • Usage
    • Data Analysis
    • Model Evaluation

7 hours

$1,990

Discover Apache Spark Machine Learning

About

Apache Spark is an open-source unified analytics engine for large-scale data processing. Spark provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. 

Apache Spark has its architectural foundation in the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. The Dataframe API was released as an abstraction on top of the RDD, followed by the Dataset API. In Spark 1.x, the RDD was the primary application programming interface (API), but as of Spark 2.x use of the Dataset, API is encouraged even though the RDD API is not deprecated. The RDD technology still underlies the Dataset API.

Content

  • Overview
  • Spark Core
  • Spark SQL
  • Spark Streaming
  • MLlib Machine Learning Library
  • GraphX
  • Language Support

14 hours

$3,980

Learn Ggplot2

About

ggplot2 is a data visualization package for the statistical programming language R. Created by Hadley Wickham in 2005, ggplot2 is an implementation of Leland Wilkinson's Grammar of Graphics—a general scheme for data visualization which breaks up graphs into semantic components such as scales and layers. ggplot2 can serve as a replacement for the base graphics in R and contains a number of defaults for web and print display of common scales. Since 2005, ggplot2 has grown in use to become one of the most popular R packages. It is licensed under GNU GPL v2.

Content

  • Introduction and Updates
  • Comparison with base graphics and other packages
  • Related projects

7 hours

$1,990

Basics of Unsupervised Learning

About

Unsupervised Learning is a machine learning technique using Python. Also known as unsupervised machine learning.

Content

  • Unsupervised Learning and Deep Learning architecture. 
  • K-Means and Gaussian Mixture Models
  • The bag-of-words model to text conversion
  • Using gensim and sklearn
  • T-SNE and UMAP with PCA and ICA comparison
  • Using sklearn and umap
  • Supervised Learning algorithms on datasets

7 hours

$1,990

Discover Machine Learning With Scikit-Learn

About

Scikit-Learn is a machine learning library for the Python programming language. Also known as scikits.learn, sklearn, and scikit.

Content

  • Scikit-Learn's Machine Learning tools
  • Regression Trees
  • Support Vector Machines
  • K-Means Clustering
  • Use of Customer Segmentation

7 hours

$1,990

Learn Natural Language Processing using NLTK

About

Natural Language Processing is a field of computer science and linguistics. Also known as NLP.

NLP studies lemmatisation, part-of-speech tagging, parsing, sentence boundary disambiguation, stemming, terminology extraction, lexical semantics, machine translation, named-entity recognition, natural language generation, optical character recognition, question answering, textual entailment, relationship extraction, sentiment analysis, text segmentation, word-sense disambiguation, automatic summarization, coreference, discourse analysis, speech recognition, speech segmentation, speech synthesis, word embedding, and decompounding.

Content

  • Scope of NLP
  • Tokenization and Chunking 
  • Text into sentences and sentences into words
  • Sentiment analysis
  • String matching algorithms and normalization techniques
  • Information retrieval and summarization
  • Various NLP in Python

 


7 hours

$1,990

Machine Learning And Tensorflow

About

This course will assist you in learning complex hypothesis, calculations and coding libraries in a straightforward way. You will grow new aptitudes and improve your comprehension of this difficult yet worthwhile field of ML. This course is fun and energizing, and yet we plunge profound into AI.

Content

  • Fundamentals of machine learning
  • Neural Networks
  • Deep Learning
  • Tensors and TensorFlow on the Cloud

 


7 hours

$1,990

Discover Machine Learning With Python

About

AI, the field of building frameworks that gain from information, is detonating on the Web and somewhere else.

Python is a great language wherein to create AI applications. As a dynamic language, it takes into consideration quick investigation and experimentation and an expanding number of AI libraries are created for Python. 

Content

  • Grouping framework that can be applied to text, pictures, or sounds 

  • Scikit-learn, a Python open-source library for AI 

  • The Mahotas library for picture preparing and PC vision 

  • Assemble a point model of the entire Wikipedia 

  • Get to hold with proposals utilizing the container investigation 

  •  The Jug bundle for Data Analysis 

  • Amazon Web Services to run investigations on the cloud 

 

 


14 hours

$3,980

Fundamentals of TensorFlow.js

About

TensorFlow.js is a framework that empowers you to make performant AI (ML) applications that run easily in an internet browser. In this course, you will figure out how to utilize TensorFlow.js to execute different ML models through a model-based methodology.

Content

  • The t-SNE algorithm in TensorFlow.js
  • TensorFlow.js converter
  • Bellman equation
  • k-means algorithm in TensorFlow.js
  • Deploy TensorFlow.js backend frameworks

14 hours

$3,980

Learn Fake News Detection Online

About

Detection of fake news online is important in today's society as fresh news content is rapidly being produced as a result of the abundance of technology that is present. In the world of false news, there are seven main categories and within each category, the piece of fake news content can be visual- and/or linguistic-based. In order to detect fake news, both linguistic and non-linguistic cues can be analyzed using several methods. While many of these methods of detecting fake news are generally successful, they do have some limitations.
 

  • Background and implications of fake news detection
  • Types of fake news
    • The seven types
    • Types of data in fake news
  • 3Features in fake news detection
    • Linguistics cues
    • Non-linguistics cues
      • Visual
      • Network
      • Sentiment
      • Social context features
  • Methods of detection
    • Deep syntax analysis
    • Propagation paths
    • Predictive modeling-based methods
    • Fact-checking
      • Manual 
      • Automatic
    • Deception detection strategies[10]
    • Propagation-based fake news detection[10]
      • Cascade-based fake news detection
      • Network-based fake news detection
    • Credibility-based study of fake news[10]
      • Assessing news headline credibility
      • Assessing news source credibility
      • Assessing news comments credibility
      • Assessing news spreader credibility
    • Account analysis
    • Browser add-ons
  • Limitations of detecting fake news

14 hours

$3,980

Discover AutoML (Automated Machine Learning)

About

Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. AutoML covers the complete pipeline from the raw dataset to the deployable machine learning model. AutoML was proposed as an artificial intelligence-based solution to the ever-growing challenge of applying machine learning. The high degree of automation in AutoML allows non-experts to make use of machine learning models and techniques without requiring them to become experts in the field first.

Automating the process of applying machine learning end-to-end, additionally offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform hand-designed models.

Content

  • Introduction to Machine Learning
  • Comparison to the standard approach
  • Stages of Machine Learning process

21 hours

$5,970


Is learning Machine Learning hard?


In the field of Machine Learning 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 Machine Learning a good field?


For now, there are tremendous work opportunities for various IT fields. Most of the courses in Machine Learning 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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