Explore Our Courses

Machine Learning with Google Colab

 14 Hours
This instructor-led, live training in Turkey (online or onsite) is aimed at intermediate-level data scientists and developers who wish to apply machine learning algorithms efficiently using the Google Colab environment.By the end of this training, participants will be able to:
  • Set up and navigate Google Colab for machine learning projects.
  • Understand and apply various machine learning algorithms.
  • Use libraries like Scikit-learn to analyze and predict data.
  • Implement supervised and unsupervised learning models.
  • Optimize and evaluate machine learning models effectively.
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Edge AI for Financial Services

 14 Hours
This instructor-led, live training in Turkey (online or onsite) is aimed at intermediate-level finance professionals, fintech developers, and AI specialists who wish to implement Edge AI solutions in financial services.By the end of this training, participants will be able to:
  • Understand the role of Edge AI in financial services.
  • Implement fraud detection systems using Edge AI.
  • Enhance customer service through AI-driven solutions.
  • Apply Edge AI for risk management and decision-making.
  • Deploy and manage Edge AI solutions in financial environments.
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Edge AI in Industrial Automation

 14 Hours
This instructor-led, live training in Turkey (online or onsite) is aimed at intermediate-level industrial engineers, manufacturing professionals, and AI developers who wish to implement Edge AI solutions in industrial automation.By the end of this training, participants will be able to:
  • Understand the role of Edge AI in industrial automation.
  • Implement predictive maintenance solutions using Edge AI.
  • Apply AI techniques for quality control in manufacturing processes.
  • Optimize industrial processes using Edge AI.
  • Deploy and manage Edge AI solutions in industrial environments.
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LightGBM for Machine Learning

 21 Hours
This instructor-led, live training in Turkey (online or onsite) is aimed at beginner to intermediate-level developers and data scientists who wish to learn the basics of LightGBM and explore advanced techniques. By the end of this training, participants will be able to:
  • Install and configure LightGBM.
  • Understand the theory behind gradient boosting and decision tree algorithms
  • Use LightGBM for basic and advanced machine learning tasks.
  • Implement advanced techniques such as feature engineering, hyperparameter tuning, and model interpretation.
  • Integrate LightGBM with other machine learning frameworks.
  • Troubleshoot common issues in LightGBM.
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Advanced Stable Diffusion: Deep Learning for Text-to-Image Generation

 21 Hours
This instructor-led, live training in Turkey (online or onsite) is aimed at intermediate to advanced-level data scientists, machine learning engineers, deep learning researchers, and computer vision experts who wish to expand their knowledge and skills in deep learning for text-to-image generation. By the end of this training, participants will be able to:
  • Understand advanced deep learning architectures and techniques for text-to-image generation.
  • Implement complex models and optimizations for high-quality image synthesis.
  • Optimize performance and scalability for large datasets and complex models.
  • Tune hyperparameters for better model performance and generalization.
  • Integrate Stable Diffusion with other deep learning frameworks and tools
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Optimizing AI Models for Edge Devices

 14 Hours
This instructor-led, live training in Turkey (online or onsite) is aimed at intermediate-level AI developers, machine learning engineers, and system architects who wish to optimize AI models for edge deployment.By the end of this training, participants will be able to:
  • Understand the challenges and requirements of deploying AI models on edge devices.
  • Apply model compression techniques to reduce the size and complexity of AI models.
  • Utilize quantization methods to enhance model efficiency on edge hardware.
  • Implement pruning and other optimization techniques to improve model performance.
  • Deploy optimized AI models on various edge devices.
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Introduction to Stable Diffusion for Text-to-Image Generation

 21 Hours
This instructor-led, live training in (online or onsite) is aimed at data scientists, machine learning engineers, and computer vision researchers who wish to leverage Stable Diffusion to generate high-quality images for a variety of use cases. By the end of this training, participants will be able to:
  • Understand the principles of Stable Diffusion and how it works for image generation.
  • Build and train Stable Diffusion models for image generation tasks.
  • Apply Stable Diffusion to various image generation scenarios, such as inpainting, outpainting, and image-to-image translation.
  • Optimize the performance and stability of Stable Diffusion models.
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AlphaFold

 7 Hours
This instructor-led, live training in Turkey (online or onsite) is aimed at biologists who wish to understand how AlphaFold works and use AlphaFold models as guides in their experimental studies. By the end of this training, participants will be able to:
  • Understand the basic principles of AlphaFold.
  • Learn how AlphaFold works.
  • Learn how to interpret AlphaFold predictions and results.
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Edge AI with TensorFlow Lite

 14 Hours
This instructor-led, live training in Turkey (online or onsite) is aimed at intermediate-level developers, data scientists, and AI practitioners who wish to leverage TensorFlow Lite for Edge AI applications.By the end of this training, participants will be able to:
  • Understand the fundamentals of TensorFlow Lite and its role in Edge AI.
  • Develop and optimize AI models using TensorFlow Lite.
  • Deploy TensorFlow Lite models on various edge devices.
  • Utilize tools and techniques for model conversion and optimization.
  • Implement practical Edge AI applications using TensorFlow Lite.
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Machine Learning with Python – 2 Days

 14 Hours
The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
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Machine Learning with Python – 4 Days

 28 Hours
The aim of this course is to provide general proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
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Introduction to Machine Learning

 7 Hours
This training course is for people that would like to apply basic Machine Learning techniques in practical applications. Audience Data scientists and statisticians that have some familiarity with machine learning and know how to program R. The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give a practical introduction to machine learning to participants interested in applying the methods at work Sector specific examples are used to make the training relevant to the audience.
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Applied Machine Learning

 14 Hours
This training course is for people that would like to apply Machine Learning in practical applications. Audience This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work. Sector specific examples are used to make the training relevant to the audience.
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Machine Learning

 21 Hours
This course will be a combination of theory and practical work with specific examples used throughout the event.
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Machine Learning for Robotics

 21 Hours
This course introduces machine learning methods in robotics applications. It is a broad overview of existing methods, motivations and main ideas in the context of pattern recognition. After a short theoretical background, participants will perform simple exercise using open source (usually R) or any other popular software.
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Machine Learning Fundamentals with Scala and Apache Spark

 14 Hours
The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Scala programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
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Machine Learning Concepts for Entrepreneurs and Managers

 21 Hours
This training course is for people that would like to apply Machine Learning in practical applications for their team.  The training will not dive into technicalities and revolve around basic concepts and business/operational applications of the same. Target Audience
  1. Investors and AI entrepreneurs
  2. Managers and Engineers whose company is venturing into AI space
  3. Business Analysts & Investors
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Machine Learning on iOS

 14 Hours
In this instructor-led, live training, participants will learn how to use the iOS Machine Learning (ML) technology stack as they step through the creation and deployment of an iOS mobile app. By the end of this training, participants will be able to:
  • Create a mobile app capable of image processing, text analysis and speech recognition
  • Access pre-trained ML models for integration into iOS apps
  • Create a custom ML model
  • Add Siri Voice support to iOS apps
  • Understand and use frameworks such as coreML, Vision, CoreGraphics, and GamePlayKit
  • Use languages and tools such as Python, Keras, Caffee, Tensorflow, sci-kit learn, libsvm, Anaconda, and Spyder
Audience
  • Developers
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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Machine Learning for Banking (with Python)

 21 Hours
Machine Learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications. In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects. Audience
  • Developers
  • Data scientists
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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Machine Learning for Finance (with Python)

 21 Hours
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications. In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects. By the end of this training, participants will be able to:
  • Understand the fundamental concepts in machine learning
  • Learn the applications and uses of machine learning in finance
  • Develop their own algorithmic trading strategy using machine learning with Python
Audience
  • Developers
  • Data scientists
Format of the course
  • Part lecture, part discussion, exercises and heavy hands-on practice
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Machine Learning in business – AI/Robotics

 14 Hours
This classroom based training session will explore machine learning techniques, with computer based examples and case study solving exercises using a relevant programme languauge  
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Algebra for Machine Learning

 14 Hours
Linear algebra is a branch of mathematics that deals with vectors, matrices, and linear transforms. Knowledge of linear algebra helps engineers and developers improve their machine learning capabilities. Understanding linear algebra concepts allows them to better understand the principles behind machine learning techniques and thus solve problems faster. In this instructor-led, live training, participants will learn the fundamentals of linear algebra as they step through solving a machine learning problem using linear algebra methods. By the end of this training, participants will be able to:
  • Understand fundamental linear algebra concepts
  • Learn the linear algebra skills needed for machine learning
  • Use linear algebra structures and concepts when working with data, images, algorithms, etc.
  • Solve a machine learning problem using linear algebra
Audience
  • Developers
  • Engineers
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.
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Feature Engineering for Machine Learning

 14 Hours
This instructor-led, live training in Turkey (online or onsite) is aimed at engineers who wish to apply feature engineering techniques to better process data and achieve obtain better machine learning models. By the end of this training, participants will be able to:
  • Set up an optimal development environment, including all needed Python packages.
  • Obtain important insights by analyzing the features of a data set.
  • Optimize machine learning models through adaptation of the raw data itself.
  • Clean and transform data sets in preparation for machine learning.
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Machine Learning and Big Data

 7 Hours
This instructor-led, live training in Turkey (online or onsite) is aimed at technical persons who wish to learn how to implement a machine learning strategy while maximizing the use of big data. By the end of this training, participants will:
  • Understand the evolution and trends for machine learning.
  • Know how machine learning is being used across different industries.
  • Become familiar with the tools, skills and services available to implement machine learning within an organization.
  • Understand how machine learning can be used to enhance data mining and analysis.
  • Learn what a data middle backend is, and how it is being used by businesses.
  • Understand the role that big data and intelligent applications are playing across industries.
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Mathematica for Machine Learning

 14 Hours
This instructor-led, live training in Turkey (online or onsite) is aimed at data scientists who wish to use machine learning in Mathematica for data analysis. By the end of this training, participants will be able to:
  • Build and train machine learning models.
  • Import and prepare data for machine learning.
  • Separate training data from test data.
  • Explore deep learning and neural network applications in data analysis.
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XGBoost for Gradient Boosting

 14 Hours
This instructor-led, live training in Turkey (online or onsite) is aimed at data scientists who wish to use XGBoost to build models that efficiently solve regression, classification, ranking, and prediction problems. By the end of this training, participants will be able to:
  • Install and configure XGBoost.
  • Understand the relationship between decision trees and other algorithms such as logistic regression and random forest.
  • Test different libraries to determine the best one for the job.
  • Choose the right configuration for an algorithm.
  • Tune the hyper-parameters of an algorithm for a given dataset.
  • Implement a machine learning solution that balances power with complexity, explainability, and ease of implementation.
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Fundamentals of Artificial Intelligence (AI) and Machine Learning

 28 Hours
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 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.
Format of the Course
  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.
Course Customization Options
  • To request a customized training for this course, please contact us to arrange.
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Dataiku for Enterprise AI and Machine Learning

 21 Hours
This instructor-led, live training in Turkey (online or onsite) is aimed at engineers, data scientists, and data analysts who wish to use Dataiku DSS for the development of machine learning pipelines, and leverage AI processes in generating strategic organizational initiatives. By the end of this training, participants will be able to:
  • Install and configure Dataiku DSS upon their preferred operating system.
  • Understand AI/ML concepts and principles that are fundamental to Dataiku features.
  • Create and implement their own data visualization codes in Dataiku DSS projects.
  • Deploy ML models and pipelines into production environments built around Dataiku DSS.
  • Optimize the agility and flexibility of data analysis methods of their enterprise applications.
  • Utilize Dataiku DSS to secure and manage how data flows through further business systems.
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Machine Learning Algorithms in Julia

 21 Hours
This course is for people that already have a background in data science and statistics. The explanations given are designed to either serve as a reminder to those that are already familiar with the concepts or inform those with a suitable background.
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H2O AutoML

 14 Hours
This instructor-led, live training in Turkey (online or onsite) is aimed at data scientists who wish to use H2O AutoML to automoate the process of building and selecting the best machine learning algorithm and parameters. By the end of this training, participants will be able to:
  • Automate the machine learning workflow.
  • Automatically train and tune many machine learning models within a specified time range.
  • Train stacked ensembles to arrive at highly predictive ensemble models.
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AutoML with Auto-sklearn

 14 Hours
This instructor-led, live training in Turkey (online or onsite) is aimed at machine learning practitioners who wish to use Auto-sklearn to automate the process of selecting and optimizing a machine learning model. By the end of this training, participants will be able to:
  • Automate the process of training highly efficient machine learning models.
  • Build highly accurate machine learning models while bypassing the more tedious tasks of selecting, training and testing different models.
  • Use the power of machine learning to solve real-world business problems.
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AutoML with Auto-Keras

 14 Hours
This instructor-led, live training in Turkey (online or onsite) is aimed at data scientists as well as less technical persons who wish to use Auto-Keras to automate the process of selecting and optimizing a machine learning model. By the end of this training, participants will be able to:
  • Automate the process of training highly efficient machine learning models.
  • Automatically search for the best parameters for deep learning models.
  • Build highly accurate machine learning models.
  • Use the power of machine learning to solve real-world business problems.
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TensorFlow Lite for Embedded Linux

 21 Hours
This instructor-led, live training in Turkey (online or onsite) is aimed at developers who wish to use TensorFlow Lite to deploy deep learning models on embedded devices. By the end of this training, participants will be able to:
  • Install and configure Tensorflow Lite on an embedded device.
  • Understand the concepts and components underlying TensorFlow Lite.
  • Convert existing models to TensorFlow Lite format for execution on embedded devices.
  • Work within the limitations of small devices and TensorFlow Lite, while learning how to expand the scope of operations that can be run.
  • Deploy a deep learning model on an embedded device running Linux.
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TensorFlow Lite for Android

 21 Hours
This instructor-led, live training in Turkey (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop mobile applications with deep learning capabilities. By the end of this training, participants will be able to:
  • Install and configure TensorFlow Lite.
  • Understand the principles behind TensorFlow, machine learning and deep learning.
  • Load TensorFlow Models onto an Android device.
  • Enable deep learning and machine learning functionality such as computer vision and natural language recognition in a mobile application.
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Tensorflow Lite for Microcontrollers

 21 Hours
This instructor-led, live training in Turkey (online or onsite) is aimed at engineers who wish to write, load and run machine learning models on very small embedded devices.By the end of this training, participants will be able to:
  • Install TensorFlow Lite.
  • Load machine learning models onto an embedded device to enable it to detect speech, classify images, etc.
  • Add AI to hardware devices without relying on network connectivity.
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TensorFlow Lite for iOS

 21 Hours
This instructor-led, live training in (online or onsite) is aimed at developers who wish to use TensorFlow Lite to develop iOS mobile applications with deep learning capabilities. By the end of this training, participants will be able to:
  • Install and configure TensorFlow Lite.
  • Understand the principles behind TensorFlow and machine learning on mobile devices.
  • Load TensorFlow Models onto an iOS device.
  • Run an iOS application capable of detecting and classifying an object captured through the device's camera.
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Deep Learning Neural Networks with Chainer

 14 Hours
This instructor-led, live training in Turkey (online or onsite) is aimed at researchers and developers who wish to use Chainer to build and train neural networks in Python while making the code easy to debug. By the end of this training, participants will be able to:
  • Set up the necessary development environment to start developing neural network models.
  • Define and implement neural network models using a comprehensible source code.
  • Execute examples and modify existing algorithms to optimize deep learning training models while leveraging GPUs for high performance.
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Distributed Deep Learning with Horovod

 7 Hours
This instructor-led, live training in Turkey (online or onsite) is aimed at developers or data scientists who wish to use Horovod to run distributed deep learning trainings and scale it up to run across multiple GPUs in parallel. By the end of this training, participants will be able to:
  • Set up the necessary development environment to start running deep learning trainings.
  • Install and configure Horovod to train models with TensorFlow, Keras, PyTorch, and Apache MXNet.
  • Scale deep learning training with Horovod to run on multiple GPUs.
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Accelerating Deep Learning with FPGA and OpenVINO

 35 Hours
This instructor-led, live training in Turkey (online or onsite) is aimed at data scientists who wish to accelerate real-time machine learning applications and deploy them at scale. By the end of this training, participants will be able to:
  • Install the OpenVINO toolkit.
  • Accelerate a computer vision application using an FPGA.
  • Execute different CNN layers on the FPGA.
  • Scale the application across multiple nodes in a Kubernetes cluster.
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Building Deep Learning Models with Apache MXNet

 21 Hours
This instructor-led, live training in (online or onsite) is aimed at data scientists who wish to use Apache MXNet's to build and deploy a deep learning model for image recognition. By the end of this training, participants will be able to:
  • Install and configure Apache MXNet and its components.
  • Understand MXNet's architecture and data structures.
  • Use Apache MXNet's low-level and high-level APIs to efficiently build neural networks.
  • Build a convolutional neural network for image classification.
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Deep Learning with Keras

 21 Hours
This instructor-led, live training in Turkey (online or onsite) is aimed at technical persons who wish to apply deep learning model to image recognition applications. By the end of this training, participants will be able to:
  • Install and configure Keras.
  • Quickly prototype deep learning models.
  • Implement a convolutional network.
  • Implement a recurrent network.
  • Execute a deep learning model on both a CPU and GPU.
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Deep Learning for Self Driving Cars

 21 Hours
This instructor-led, live training in Turkey (online or onsite) is aimed at developers who wish to build a self-driving car using deep learning techniques. By the end of this training, participants will be able to:
  • Use Keras to build and train a convolutional neural network.
  • Use computer vision techniques to identify lanes in an autonomos driving project.
  • Train a deep learning model to differentiate traffic signs.
  • Simulate a fully autonomous car.
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Advanced Deep Learning with Keras and Python

 14 Hours
This instructor-led, live training in Turkey (online or onsite) is aimed at software engineers who wish to develop advanced deep learning neural-networks and model using Keras and Python. By the end of this training, participants will be able to:
  • Apply deep learning with supervised or unsupervised learning methods.
  • Develop, train, and implement concurrent neural networks and recurrent neural networks.
  • Use Keras and Python to build deep learning models to solve problems involving images, text, sound, and more.
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Torch for Machine and Deep Learning

 21 Hours
Torch is an open source machine learning library and a scientific computing framework based on the Lua programming language. It provides a development environment for numerics, machine learning, and computer vision, with a particular emphasis on deep learning and convolutional nets. It is one of the fastest and most flexible frameworks for Machine and Deep Learning and is used by companies such as Facebook, Google, Twitter, NVIDIA, AMD, Intel, and many others. In this instructor-led, live training, we cover the principles of Torch, its unique features, and how it can be applied in real-world applications. We step through numerous hands-on exercises all throughout, demonstrating and practicing the concepts learned. By the end of the course, participants will have a thorough understanding of Torch's underlying features and capabilities as well as its role and contribution within the AI space compared to other frameworks and libraries. Participants will have also received the necessary practice to implement Torch in their own projects.
Format of the Course
  • Overview of Machine and Deep Learning
  • In-class coding and integration exercises
  • Test questions sprinkled along the way to check understanding
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Deep Learning for Vision with Caffe

 21 Hours
Caffe is a deep learning framework made with expression, speed, and modularity in mind. This course explores the application of Caffe as a Deep learning framework for image recognition using MNIST as an example Audience This course is suitable for Deep Learning researchers and engineers interested in utilizing Caffe as a framework. After completing this course, delegates will be able to:
  • understand Caffe’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, implementing layers and logging
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Mastering Apache SINGA

 21 Hours
SINGA is a general distributed deep learning platform for training big deep learning models over large datasets. It is designed with an intuitive programming model based on the layer abstraction. A variety of popular deep learning models are supported, namely feed-forward models including convolutional neural networks (CNN), energy models like restricted Boltzmann machine (RBM), and recurrent neural networks (RNN). Many built-in layers are provided for users. SINGA architecture is sufficiently flexible to run synchronous, asynchronous and hybrid training frameworks. SINGA also supports different neural net partitioning schemes to parallelize the training of large models, namely partitioning on batch dimension, feature dimension or hybrid partitioning. Audience This course is directed at researchers, engineers and developers seeking to utilize Apache SINGA as a deep learning framework. After completing this course, delegates will:
  • understand SINGA’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
 
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NLP with Deeplearning4j

 14 Hours
Deeplearning4j is an open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments on distributed GPUs and CPUs. Word2Vec is a method of computing vector representations of words introduced by a team of researchers at Google led by Tomas Mikolov. Audience This course is directed at researchers, engineers and developers seeking to utilize Deeplearning4J to construct Word2Vec models.
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Mastering Deeplearning4j

 21 Hours
Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments on distributed GPUs and CPUs.   Audience This course is directed at engineers and developers seeking to utilize Deeplearning4j in their projects.   After this course delegates will be able to:
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Using Computer Network ToolKit (CNTK)

 28 Hours
Computer Network ToolKit (CNTK) is Microsoft's Open Source, Multi-machine, Multi-GPU, Highly efficent RNN training machine learning framework for speech, text, and images. Audience This course is directed at engineers and architects aiming to utilize CNTK in their projects.
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Deep Learning with TensorFlow

 21 Hours
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
Read more...

Machine Learning with Python and Pandas

 14 Hours
This instructor-led, live training in Turkey (online or onsite) is aimed at data scientists who wish to use Pandas to preform predictive analysis with machine learning. By the end of this training, participants will be able to:
  • Perform data wrangling in Python.
  • Conduct ETL operations for machine learning.
  • Create data visualizations with Pandas
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Testimonials(22)

the VM is a nice idea

Vincent - REGNOLOGY ROMANIA S.R.L.

Course - Fundamentals of Artificial Intelligence (AI) and Machine Learning

I thought the trainer was very knowledgeable and answered questions with confidence to clarify understanding.

Jenna - TCMT

Course - Machine Learning with Python – 2 Days

The clarity with which it was presented

John McLemore - Motorola Solutions

Course - Deep Learning for Telecom (with Python)

The way of transferring knowledge and the knowledge of the trainer.

Jakub Rękas - Bitcomp Sp. z o.o.

Course - Machine Learning on iOS

The explaination

Wei Yang Teo - Ministry of Defence, Singapore

Course - Machine Learning with Python – 4 Days

The enthusiasm to the topic. The examples he made an he explained it very well. Sympatic. A little to detailed for beginners. For managers, it could be more abstract in fewer days. But it was designed to fit and we had a good alignment in advance.

Benedikt Chiandetti - HDI Deutschland Bancassurance Kundenservice GmbH

Course - Machine Learning Concepts for Entrepreneurs and Managers

The trainer explained the content well and was engaging throughout. He stopped to ask questions and let us come to our own solutions in some practical sessions. He also tailored the course well for our needs.

Robert Baker

Course - Deep Learning with TensorFlow 2.0

Convolution filter

Francesco Ferrara

Course - Introduction to Machine Learning

Tomasz really know the information well and the course was well paced.

Raju Krishnamurthy - Google

Course - TensorFlow Extended (TFX)

Organization, adhering to the proposed agenda, the trainer's vast knowledge in this subject

Ali Kattan - TWPI

Course - Natural Language Processing with TensorFlow

The trainer was a professional in the subject field and related theory with application excellently

Fahad Malalla - Tatweer Petroleum

Course - Applied AI from Scratch in Python

I liked the lab exercises.

Marcell Lorant - L M ERICSSON LIMITED

Course - Machine Learning

In-depth coverage of machine learning topics, particularly neural networks. Demystified a lot of the topic.

Sacha Nandlall

Course - Python for Advanced Machine Learning

Very flexible.

Frank Ueltzhöffer

Course - Artificial Neural Networks, Machine Learning and Deep Thinking

Very updated approach or CPI (tensor flow, era, learn) to do machine learning.

Paul Lee

Course - TensorFlow for Image Recognition

I liked the new insights in deep machine learning.

Josip Arneric

Course - Neural Network in R

We have gotten a lot more insight in to the subject matter. Some nice discussion were made with some real subjects within our company.

Sebastiaan Holman

Course - Machine Learning and Deep Learning

I really appreciated the crystal clear answers of Chris to our questions.

Léo Dubus

Course - Réseau de Neurones, les Fondamentaux en utilisant TensorFlow comme Exemple

The global overview of deep learning.

Bruno Charbonnier

Course - Advanced Deep Learning

The topic is very interesting.

Wojciech Baranowski

Course - Introduction to Deep Learning

Ann created a great environment to ask questions and learn. We had a lot of fun and also learned a lot at the same time.

Gudrun Bickelq

Course - Introduction to the use of neural networks

It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.

Jonathan Blease

Course - Artificial Neural Networks, Machine Learning, Deep Thinking

Upcoming Courses

Machine Learning with Python – 4 Days

2024-08-02 09:30
28 hours
Antalya

Algebra for Machine Learning

2024-08-16 09:30
14 hours
Istanbul, Taksim

Mastering Apache SINGA

2024-08-30 09:30
21 hours
Ankara

AutoML with Auto-sklearn

2024-09-13 09:30
14 hours
Antalya

Accelerating Deep Learning with FPGA and OpenVINO

2024-09-27 09:30
35 hours
Istanbul, Taksim

Distributed Deep Learning with Horovod

2024-10-11 09:30
7 hours
Ankara

Machine Learning Algorithms in Julia

2024-10-25 09:30
21 hours
Antalya

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These courses are also available in other countries
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