Machine Learning Training Courses in Egypt

Machine Learning Training Courses

Online or onsite, instructor-led live Machine Learning (ML) training courses demonstrate through hands-on practice how to apply machine learning techniques and tools for solving real-world problems in various industries. NobleProg ML courses cover different programming languages and frameworks, including Python, R language and Matlab. Machine Learning courses are offered for a number of industry applications, including Finance, Banking and Insurance and cover the fundamentals of Machine Learning as well as more advanced approaches such as Deep Learning.

Machine Learning training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Egypt onsite live Machine Learning trainings can be carried out locally on customer premises or in NobleProg corporate training centers.

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Machine Learning (ML) Course Outlines in Egypt

Course Name
Duration
Overview
Course Name
Duration
Overview
7 hours
Overview
This course has been created for managers, solutions architects, innovation officers, CTOs, software architects and anyone who is interested in an overview of applied artificial intelligence and the nearest forecast for its development.
28 hours
Overview
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability.

In this instructor-led, live training, participants will learn how to implement deep learning models for finance using Python as they step through the creation of a deep learning stock price prediction model.

By the end of this training, participants will be able to:

- Understand the fundamental concepts of deep learning
- Learn the applications and uses of deep learning in finance
- Use Python, Keras, and TensorFlow to create deep learning models for finance
- Build their own deep learning stock price prediction model using Python

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
7 hours
Overview
In this instructor-led, live training in Egypt, participants will learn how to take advantage of the innovations in TPU processors to maximize the performance of their own AI applications.

By the end of the training, participants will be able to:

- Train various types of neural networks on large amounts of data.
- Use TPUs to speed up the inference process by up to two orders of magnitude.
- Utilize TPUs to process intensive applications such as image search, cloud vision and photos.
28 hours
Overview
This course will give you knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications).

This training is more focus on fundamentals, but will help you to choose the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow.
35 hours
Overview
TensorFlow™ is an open source software library for numerical computation using data flow graphs.

SyntaxNet is a neural-network Natural Language Processing framework for TensorFlow.

Word2Vec is used for learning vector representations of words, called "word embeddings". Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model (Chapter 3.1 and 3.2 in Mikolov et al.).

Used in tandem, SyntaxNet and Word2Vec allows users to generate Learned Embedding models from Natural Language input.

Audience

This course is targeted at Developers and engineers who intend to work with SyntaxNet and Word2Vec models in their TensorFlow graphs.

After completing this course, delegates will:

- understand TensorFlow’s structure and deployment mechanisms
- be able to carry out installation / production environment / architecture tasks and configuration
- be able to assess code quality, perform debugging, monitoring
- be able to implement advanced production like training models, embedding terms, building graphs and logging
28 hours
Overview
This course explores, with specific examples, the application of Tensor Flow to the purposes of image recognition

Audience

This course is intended for engineers seeking to utilize TensorFlow for the purposes of Image Recognition

After completing this course, delegates will be able to:

- understand TensorFlow’s structure and deployment mechanisms
- carry out installation / production environment / architecture tasks and configuration
- assess code quality, perform debugging, monitoring
- implement advanced production like training models, building graphs and logging
21 hours
Overview
TensorFlow is a 2nd Generation API of Google's open source software library for Deep Learning. The system is designed to facilitate research in machine learning, and to make it quick and easy to transition from research prototype to production system.

Audience

This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects

After completing this course, delegates will:

- understand TensorFlow’s structure and deployment mechanisms
- be able to carry out installation / production environment / architecture tasks and configuration
- be able to assess code quality, perform debugging, monitoring
- be able to implement advanced production like training models, building graphs and logging
14 hours
Overview
This instructor-led, live training in Egypt (online or onsite) is aimed at developers and data scientists who wish to apply convolutional neural networks (CNNs) to the analysis of MRI scans.

By the end of this training, participants will be able to:

- Install and configure the necessary development environment, software and libraries to begin developing.
- Analyze MRI images using deep learning techniques such as CNNs.
- Detect potential health conditions such as heart disease through MRI scan analysis.
- Apply techniques such as image segmentation and CNN training to identify potential disease.
- Identify the genomics of a disease using radiomics.
- Build and deploy a deep learning application aimed at healthcare image analysis.
14 hours
Overview
This instructor-led, live training in Egypt (online or onsite) is aimed at developers who wish to build hardware-accelerated object detection and tracking models to analyze streaming video data.

By the end of this training, participants will be able to:

- Install and configure the necessary development environment, software and libraries to begin developing.
- Build, train, and deploy deep learning models to analyze live video feeds.
- Identify, track, segment and predict different objects within video frames.
- Optimize object detection and tracking models.
- Deploy an intelligent video analytics (IVA) application.
14 hours
Overview
This instructor-led, live training in Egypt (online or onsite) is aimed at software engineers who wish to program in Python with OpenCV 4 for deep learning.

By the end of this training, participants will be able to:

- View, load, and classify images and videos using OpenCV 4.
- Implement deep learning in OpenCV 4 with TensorFlow and Keras.
- Run deep learning models and generate impactful reports from images and videos.
28 hours
Overview
In this instructor-led, live training in Egypt, participants will learn how to implement deep learning models for telecom using Python as they step through the creation of a deep learning credit risk model.

By the end of this training, participants will be able to:

- Understand the fundamental concepts of deep learning.
- Learn the applications and uses of deep learning in telecom.
- Use Python, Keras, and TensorFlow to create deep learning models for telecom.
- Build their own deep learning customer churn prediction model using Python.
14 hours
Overview
Machine Learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep Learning is a subfield of Machine Learning which attempts to mimic the workings of the human brain in making decisions. It is trained with data in order to automatically provide solutions to problems. Deep Learning provides vast opportunities for the medical industry which is sitting on a data goldmine.

In this instructor-led, live training, participants will take part in a series of discussions, exercises and case-study analysis to understand the fundamentals of Deep Learning. The most important Deep Learning tools and techniques will be evaluated and exercises will be carried out to prepare participants for carrying out their own evaluation and implementation of Deep Learning solutions within their organizations.

By the end of this training, participants will be able to:

- Understand the fundamentals of Deep Learning
- Learn Deep Learning techniques and their applications in the industry
- Examine issues in medicine which can be solved by Deep Learning technologies
- Explore Deep Learning case studies in medicine
- Formulate a strategy for adopting the latest technologies in Deep Learning for solving problems in medicine

Audience

- Managers
- Medical professionals in leadership roles

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

Note

- To request a customized training for this course, please contact us to arrange.
14 hours
Overview
This classroom based training session will contain presentations and computer based examples and case study exercises to undertake with relevant neural and deep network libraries
21 hours
Overview
Introduction:

Deep learning is becoming a principal component of future product design that wants to incorporate artificial intelligence at the heart of their models. Within the next 5 to 10 years, deep learning development tools, libraries, and languages will become standard components of every software development toolkit. So far Google, Sales Force, Facebook, Amazon have been successfully using deep learning AI to boost their business. Applications ranged from automatic machine translation, image analytics, video analytics, motion analytics, generating targeted advertisement and many more.

This coursework is aimed for those organizations who want to incorporate Deep Learning as very important part of their product or service strategy. Below is the outline of the deep learning course which we can customize for different levels of employees/stakeholders in an organization.

Target Audience:

( Depending on target audience, course materials will be customized)

Executives

A general overview of AI and how it fits into corporate strategy, with breakout sessions on strategic planning, technology roadmaps, and resource allocation to ensure maximum value.

Project Managers

How to plan out an AI project, including data gathering and evaluation, data cleanup and verification, development of a proof-of-concept model, integration into business processes, and delivery across the organization.

Developers

In-depth technical trainings, with focus on neural networks and deep learning, image and video analytics (CNNs), sound and text analytics (NLP), and bringing AI into existing applications.

Salespersons

A general overview of AI and how it can satisfy customer needs, value propositions for various products and services, and how to allay fears and promote the benefits of AI.
21 hours
Overview
Deep Reinforcement Learning refers to the ability of an "artificial agent" to learn by trial-and-error and rewards-and-punishments. An artificial agent aims to emulate a human's ability to obtain and construct knowledge on its own, directly from raw inputs such as vision. To realize reinforcement learning, deep learning and neural networks are used. Reinforcement learning is different from machine learning and does not rely on supervised and unsupervised learning approaches.

In this instructor-led, live training, participants will learn the fundamentals of Deep Reinforcement Learning as they step through the creation of a Deep Learning Agent.

By the end of this training, participants will be able to:

- Understand the key concepts behind Deep Reinforcement Learning and be able to distinguish it from Machine Learning
- Apply advanced Reinforcement Learning algorithms to solve real-world problems
- Build a Deep Learning Agent

Audience

- Developers
- Data Scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
28 hours
Overview
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.

In this instructor-led, live training, participants will learn how to implement deep learning models for banking using R as they step through the creation of a deep learning credit risk model.

By the end of this training, participants will be able to:

- Understand the fundamental concepts of deep learning
- Learn the applications and uses of deep learning in banking
- Use R to create deep learning models for banking
- Build their own deep learning credit risk model using R

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
7 hours
Overview
In this instructor-led, live training in Egypt (online or onsite), participants will learn how to configure and use TensorFlow Serving to deploy and manage ML models in a production environment.

By the end of this training, participants will be able to:

- Train, export and serve various TensorFlow models.
- Test and deploy algorithms using a single architecture and set of APIs.
- Extend TensorFlow Serving to serve other types of models beyond TensorFlow models.
28 hours
Overview
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability.

In this instructor-led, live training, participants will learn how to implement deep learning models for banking using Python as they step through the creation of a deep learning credit risk model.

By the end of this training, participants will be able to:

- Understand the fundamental concepts of deep learning
- Learn the applications and uses of deep learning in banking
- Use Python, Keras, and TensorFlow to create deep learning models for banking
- Build their own deep learning credit risk model using Python

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
28 hours
Overview
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.

In this instructor-led, live training, participants will learn how to implement deep learning models for finance using R as they step through the creation of a deep learning stock price prediction model.

By the end of this training, participants will be able to:

- Understand the fundamental concepts of deep learning
- Learn the applications and uses of deep learning in finance
- Use R to create deep learning models for finance
- Build their own deep learning stock price prediction model using R

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
14 hours
Overview
In this instructor-led, live training, participants will learn how to use Matlab to design, build, and visualize a convolutional neural network for image recognition.

By the end of this training, participants will be able to:

- Build a deep learning model
- Automate data labeling
- Work with models from Caffe and TensorFlow-Keras
- Train data using multiple GPUs, the cloud, or clusters

Audience

- Developers
- Engineers
- Domain experts

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
21 hours
Overview
In this instructor-led, live training, participants will learn advanced techniques for Machine Learning with R as they step through the creation of a real-world application.

By the end of this training, participants will be able to:

- Understand and implement unsupervised learning techniques
- Apply clustering and classification to make predictions based on real world data.
- Visualize data to quicly gain insights, make decisions and further refine analysis.
- Improve the performance of a machine learning model using hyper-parameter tuning.
- Put a model into production for use in a larger application.
- Apply advanced machine learning techniques to answer questions involving social network data, big data, and more.
21 hours
Overview
In this instructor-led, live training in Egypt, participants will learn the most relevant and cutting-edge machine learning techniques in Python as they build a series of demo applications involving image, music, text, and financial data.

By the end of this training, participants will be able to:

- Implement machine learning algorithms and techniques for solving complex problems.
- Apply deep learning and semi-supervised learning to applications involving image, music, text, and financial data.
- Push Python algorithms to their maximum potential.
- Use libraries and packages such as NumPy and Theano.
14 hours
Overview
OpenFace is Python and Torch based open-source, real-time facial recognition software based on Google's FaceNet research.

In this instructor-led, live training, participants will learn how to use OpenFace's components to create and deploy a sample facial recognition application.

By the end of this training, participants will be able to:

- Work with OpenFace's components, including dlib, OpenVC, Torch, and nn4 to implement face detection, alignment, and transformation
- Apply OpenFace to real-world applications such as surveillance, identity verification, virtual reality, gaming, and identifying repeat customers, etc.

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
7 hours
Overview
Tensor2Tensor (T2T) is a modular, extensible library for training AI models in different tasks, using different types of training data, for example: image recognition, translation, parsing, image captioning, and speech recognition. It is maintained by the Google Brain team.

In this instructor-led, live training, participants will learn how to prepare a deep-learning model to resolve multiple tasks.

By the end of this training, participants will be able to:

- Install tensor2tensor, select a data set, and train and evaluate an AI model
- Customize a development environment using the tools and components included in Tensor2Tensor
- Create and use a single model to concurrently learn a number of tasks from multiple domains
- Use the model to learn from tasks with a large amount of training data and apply that knowledge to tasks where data is limited
- Obtain satisfactory processing results using a single GPU

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
7 hours
Overview
In this instructor-led, live training, participants will learn how to use DSSTNE to build a recommendation application.

By the end of this training, participants will be able to:

- Train a recommendation model with sparse datasets as input
- Scale training and prediction models over multiple GPUs
- Spread out computation and storage in a model-parallel fashion
- Generate Amazon-like personalized product recommendations
- Deploy a production-ready application that can scale at heavy workloads

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
21 hours
Overview
PaddlePaddle (PArallel Distributed Deep LEarning) is a scalable deep learning platform developed by Baidu.

In this instructor-led, live training, participants will learn how to use PaddlePaddle to enable deep learning in their product and service applications.

By the end of this training, participants will be able to:

- Set up and configure PaddlePaddle
- Set up a Convolutional Neural Network (CNN) for image recognition and object detection
- Set up a Recurrent Neural Network (RNN) for sentiment analysis
- Set up deep learning on recommendation systems to help users find answers
- Predict click-through rates (CTR), classify large-scale image sets, perform optical character recognition(OCR), rank searches, detect computer viruses, and implement a recommendation system.

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
21 hours
Overview
Microsoft Cognitive Toolkit 2.x (previously CNTK) is an open-source, commercial-grade toolkit that trains deep learning algorithms to learn like the human brain. According to Microsoft, CNTK can be 5-10x faster than TensorFlow on recurrent networks, and 2 to 3 times faster than TensorFlow for image-related tasks.

In this instructor-led, live training, participants will learn how to use Microsoft Cognitive Toolkit to create, train and evaluate deep learning algorithms for use in commercial-grade AI applications involving multiple types of data such as data, speech, text, and images.

By the end of this training, participants will be able to:

- Access CNTK as a library from within a Python, C#, or C++ program
- Use CNTK as a standalone machine learning tool through its own model description language (BrainScript)
- Use the CNTK model evaluation functionality from a Java program
- Combine feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs)
- Scale computation capacity on CPUs, GPUs and multiple machines
- Access massive datasets using existing programming languages and algorithms

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

Note

- If you wish to customize any part of this training, including the programming language of choice, please contact us to arrange.
7 hours
Overview
In this instructor-led, live training, participants will learn how to use Facebook NMT (Fairseq) to carry out translation of sample content.

By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution.

Format of the course

- Part lecture, part discussion, heavy hands-on practice

Note

- If you wish to use specific source and target language content, please contact us to arrange.
21 hours
Overview
Artificial intelligence has revolutionized a large number of economic sectors (industry, medicine, communication, etc.) after having upset many scientific fields. Nevertheless, his presentation in the major media is often a fantasy, far removed from what really are the fields of Machine Learning or Deep Learning. The aim of this course is to provide engineers who already have a master's degree in computer tools (including a software programming base) an introduction to Deep Learning as well as to its various fields of specialization and therefore to the main existing network architectures today. If the mathematical bases are recalled during the course, a level of mathematics of type BAC + 2 is recommended for more comfort. It is absolutely possible to ignore the mathematical axis in order to maintain only a "system" vision, but this approach will greatly limit your understanding of the subject.
7 hours
Overview
In this instructor-led, live training, participants will learn how to set up and use OpenNMT to carry out translation of various sample data sets. The course starts with an overview of neural networks as they apply to machine translation. Participants will carry out live exercises throughout the course to demonstrate their understanding of the concepts learned and get feedback from the instructor.

By the end of this training, participants will have the knowledge and practice needed to implement a live OpenNMT solution.

Source and target language samples will be pre-arranged per the audience's requirements.

Format of the Course

- Part lecture, part discussion, heavy hands-on practice

Upcoming ML (Machine Learning) Courses in Egypt

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