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. Georgia onsite live Machine Learning trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Atlanta, GA – Regus at Colony Squar
1201 Peachtree Street NE, Suite 200, Atlanta, United States, 30361
The venue is centrally located in Midtown Atlanta within the prominent Colony Square complex at 1201 Peachtree Street NE, easily accessed by car via I‑75/85 or GA‑400, with several parking garages nearby. From Hartsfield–Jackson Atlanta International Airport (ATL), around 15 miles south, a taxi or rideshare typically takes 20–30 minutes north along I‑75/85 N. Public transit users can take MARTA Rail to the Arts Center or Midtown stations (0.3–0.5 miles away) and walk easily, and numerous MARTA bus routes along Peachtree Street stop directly outside the entrance.
Atlanta, GA – The Proscenium
1170 Peachtree Street NE, Atlanta, United States, 30309
The venue is located in the heart of Midtown Atlanta in the Proscenium high–rise at 1170 Peachtree Street NE, easily accessible by car via I‑75/85 and GA‑400 with several parking garages nearby. Visitors arriving from Hartsfield–Jackson Atlanta International Airport (ATL), about 15 miles south, can expect a taxi or rideshare ride taking 20–30 minutes via I‑75/85 North. Public transit is seamless with MARTA Rail service; the Arts Center and Midtown stations are within walking distance (approximately 0.3–0.4 miles), and multiple MARTA bus routes also serve Peachtree Street.
Decatur, GA – Regus at One West Court Square
One West Court Square, Suite 750, Decatur, United States, 30030
The venue is located in the heart of downtown Decatur within One West Court Square, easily reached by car via I‑20 and I‑285, with several public parking decks directly adjacent. Travelers from Hartsfield–Jackson Atlanta International Airport (ATL), approximately 17 miles southwest, can expect a taxi or rideshare ride of around 25–30 minutes via I‑20 East. Public transit is particularly convenient: MARTA rail users can disembark at Decatur Station (about 0.15 miles away) and walk a few minutes to the building entrance. Local bus routes also serve Trinity Place and Swanton Way, putting the center within easy reach.
Atlanta, GA – Regus at One Hartsfield
100 Hartsfield Centre Parkway, Suite 500, Atlanta, United States, 30354
The venue is located in the One Hartsfield Center office building, adjacent to Hartsfield–Jackson Atlanta International Airport, easily reached by car via I‑75/I‑85 or GA‑138, with abundant on-site parking. Visitors arriving from ATL airport can walk or take a shuttle to the building, or opt for a quick 2–3‑minute taxi or rideshare ride. Public transit users can board MARTA from the Airport Station and ride one stop to College Park Station, then catch a connecting shuttle or enjoy a brief walk of about half a mile.
Atlanta, GA – Regus at Peachtree
260 Peachtree Street NW, Suite 2200, Atlanta, United States, 30303
The venue is situated in the iconic Coastal States Building at 260 Peachtree Street in downtown Atlanta, accessible by car via I‑75/85 or I‑20 with convenient parking garages nearby. From Hartsfield–Jackson Atlanta International Airport (ATL), about 12 miles south, a taxi or rideshare along I‑75/85 North takes approximately 15–20 minutes. For public transit, MARTA rail users can disembark at Five Points Station and walk 0.5 miles northeast, or exit at Peachtree Center Station and walk two blocks north—both routes offering easy access.
Augusta, GA – At Broad Street
823 Broad Street, Augusta, United States, 3090
The venue is located in the heart of downtown Augusta on Broad Street, easily accessible by car via I‑20 with several public parking garages nearby. From Augusta Regional Airport (AGS), about 9 miles west, taxis or rideshares typically take 15–20 minutes via I‑20. Public transit is available through Augusta Public Transit buses with routes along Broad Street, stopping within a few blocks of the venue, offering a convenient option for attendees without a car.
Savannah, GA – Regus at Bull Street
100 Bull St Downtown, Suite 200, Savannah, United States, 31401
The venue is located in the historic downtown area on Bull Street in the Altmayer Building, easily accessible by car via I‑16 and U.S. 17, with several public garages nearby. From Savannah/Hilton Head International Airport (SAV), about 12 miles west, taxis or rideshares typically take 15–20 minutes via U.S. 17 South. Public transit is available via Chatham Area Transit (CAT) buses, with frequent service along Bull and Broughton Streets; Johnson Square Station is just a couple minutes’ walk from the venue.
This instructor-led, live training in Georgia (online or onsite) is aimed at beginner-level professionals who wish to understand the concept of pre-trained models and learn how to apply them to solve real-world problems without building models from scratch.
By the end of this training, participants will be able to:
Understand the concept and benefits of pre-trained models.
Explore various pre-trained model architectures and their use cases.
Fine-tune a pre-trained model for specific tasks.
Implement pre-trained models in simple machine learning projects.
This instructor-led, live training in Georgia (online or onsite) is aimed at participants with varying levels of expertise who wish to leverage Google's AutoML platform to build customized chatbots for various applications.
By the end of this training, participants will be able to:
Understand the fundamentals of chatbot development.
Navigate the Google Cloud Platform and access AutoML.
Prepare data for training chatbot models.
Train and evaluate custom chatbot models using AutoML.
Deploy and integrate chatbots into various platforms and channels.
Monitor and optimize chatbot performance over time.
This instructor-led, live training in Georgia (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.
This instructor-led, live training in Georgia (online or onsite) is aimed at intermediate-level developers, data scientists, and tech enthusiasts who wish to gain practical skills in deploying AI models on edge devices for various applications.
By the end of this training, participants will be able to:
Understand the principles of Edge AI and its benefits.
Set up and configure the edge computing environment.
Develop, train, and optimize AI models for edge deployment.
Implement practical AI solutions on edge devices.
Evaluate and improve the performance of edge-deployed models.
Address ethical and security considerations in Edge AI applications.
This instructor-led, live training in Georgia (online or onsite) is aimed at advanced-level AI engineers and data scientists with intermediate-to-advanced experience who wish to enhance DeepSeek model performance, minimize latency, and deploy AI solutions efficiently using modern MLOps practices.
By the end of this training, participants will be able to:
Optimize DeepSeek models for efficiency, accuracy, and scalability.
Implement best practices for MLOps and model versioning.
Deploy DeepSeek models on cloud and on-premise infrastructure.
Monitor, maintain, and scale AI solutions effectively.
MLOps on Kubernetes is a framework for automating the training, validation, packaging, and deployment of machine learning models using containerized pipelines and GitOps workflows.
This instructor-led, live training (online or onsite) is aimed at intermediate-level practitioners who wish to build automated, scalable MLOps pipelines on Kubernetes.
After completing this training, participants will be equipped to:
Design end-to-end CI/CD pipelines for machine learning.
Implement GitOps workflows for model deployment and versioning.
Automate training, testing, and packaging of ML models.
Integrate monitoring, alerting, and rollback strategies.
Format of the Course
Instructor-guided presentations and technical deep dives.
Hands-on exercises that build real-world CI/CD workflows.
Live-lab practice deploying ML workloads to Kubernetes.
Course Customization Options
Organizations may request tailored content aligned with their internal MLOps tools and infrastructure.
Kubeflow is an open-source platform designed to streamline building, training, and deploying machine learning workloads on Kubernetes.
This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to build reliable ML workflows using Kubeflow.
Upon completion of this training, attendees will gain the skills to:
Navigate the Kubeflow ecosystem and core components.
Build reproducible workflows with Kubeflow Pipelines.
Run scalable training jobs on Kubernetes.
Serve machine learning models efficiently using Kubeflow Serving.
Format of the Course
Guided presentations and collaborative discussions.
Hands-on labs with real Kubeflow components.
Practical exercises to build end-to-end ML workflows.
Course Customization Options
Customized versions of this training can be arranged to align with your team’s technology stack and project requirements.
TinyML is the practice of deploying optimized machine learning models on resource-constrained edge devices.
This instructor-led, live training (online or onsite) is aimed at advanced-level technical professionals who wish to design, optimize, and deploy complete TinyML pipelines.
By the conclusion of this training, participants will learn how to:
Collect, prepare, and manage datasets for TinyML applications.
Train and optimize models for low-power microcontrollers.
Convert models to lightweight formats suitable for edge devices.
Deploy, test, and monitor TinyML applications in real hardware environments.
Format of the Course
Instructor-guided lectures and technical discussion.
Practical labs and iterative experimentation.
Hands-on deployment on microcontroller-based platforms.
Course Customization Options
To customize the training with specific toolchains, hardware boards, or internal workflows, please contact us to arrange.
This instructor-led, live training in Georgia (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.
This instructor-led, live training in Georgia (online or onsite) is aimed at advanced-level professionals who wish to master the technologies behind autonomous systems.
By the end of this training, participants will be able to:
Design and implement AI models for autonomous decision-making.
Develop control algorithms for autonomous navigation and obstacle avoidance.
Ensure safety and reliability in AI-powered autonomous systems.
Integrate autonomous systems with existing robotics and AI frameworks.
This instructor-led, live training in Georgia (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab.
By the end of this training, participants will be able to:
Build and train convolutional neural networks (CNNs) using TensorFlow.
Leverage Google Colab for scalable and efficient cloud-based model development.
Implement image preprocessing techniques for computer vision tasks.
Deploy computer vision models for real-world applications.
Use transfer learning to enhance the performance of CNN models.
Visualize and interpret the results of image classification models.
TinyML is an approach to deploying machine learning models on low-power, resource-constrained devices operating at the network edge.
This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to secure TinyML pipelines and implement privacy-preserving techniques in edge AI applications.
At the conclusion of this course, participants will be able to:
Identify security risks unique to on-device TinyML inference.
Implement privacy-preserving mechanisms for edge AI deployments.
Harden TinyML models and embedded systems against adversarial threats.
Apply best practices for secure data handling in constrained environments.
Format of the Course
Engaging lectures supported by expert-led discussions.
This instructor-led, live training in Georgia (online or onsite) is aimed at advanced-level professionals who wish to enhance their knowledge of machine learning models, improve their skills in hyperparameter tuning, and learn how to deploy models effectively using Google Colab.
By the end of this training, participants will be able to:
Implement advanced machine learning models using popular frameworks like Scikit-learn and TensorFlow.
Optimize model performance through hyperparameter tuning.
Deploy machine learning models in real-world applications using Google Colab.
Collaborate and manage large-scale machine learning projects in Google Colab.
This instructor-led, live training in Georgia (online or onsite) is aimed at intermediate-level professionals who wish to apply AI techniques to optimize yield management in semiconductor manufacturing.
By the end of this training, participants will be able to:
Analyze production data to identify factors affecting yield rates.
Implement AI algorithms to enhance yield management processes.
Optimize production parameters to reduce defects and improve yields.
Integrate AI-driven yield management into existing production workflows.
This instructor-led, live training in Georgia (online or onsite) is aimed at intermediate-level business and AI professionals who wish to apply machine learning in business, forecasting, and AI-driven systems using real case studies and Python-based tools.
By the end of this training, participants will be able to:
Understand how machine learning fits within AI and business strategy.
Apply supervised and unsupervised learning techniques to structured business problems.
Preprocess and transform data for modeling.
Use neural networks for classification and prediction tasks.
Perform sales forecasting using statistical and ML-based methods.
Implement clustering and association rule mining for customer segmentation and pattern discovery.
This instructor-led, live training in Georgia (online or onsite) is aimed at intermediate-level professionals who wish to apply AI-driven predictive maintenance techniques in semiconductor manufacturing to enhance production efficiency and reduce unexpected equipment failures.
By the end of this training, participants will be able to:
Implement AI models for predicting equipment failures in semiconductor manufacturing.
Analyze maintenance data to identify patterns and trends indicative of potential issues.
Integrate AI-driven predictive maintenance into existing manufacturing workflows.
Reduce downtime and maintenance costs through proactive equipment management.
This instructor-led, live training in Georgia (online or onsite) is aimed at advanced-level professionals who wish to apply cutting-edge AI techniques to semiconductor design automation, improving efficiency, accuracy, and innovation in chip design and verification.
By the end of this training, participants will be able to:
Apply advanced AI techniques to optimize semiconductor design processes.
Integrate machine learning models into EDA tools for enhanced design verification.
Develop AI-driven solutions for complex design challenges in chip fabrication.
Leverage neural networks for improving the accuracy and speed of design automation.
This instructor-led, live training in Georgia (online or onsite) is aimed at intermediate-level data scientists and developers who wish to understand and apply deep learning techniques using the Google Colab environment.
By the end of this training, participants will be able to:
Set up and navigate Google Colab for deep learning projects.
Understand the fundamentals of neural networks.
Implement deep learning models using TensorFlow.
Train and evaluate deep learning models.
Utilize advanced features of TensorFlow for deep learning.
This instructor-led, live training in Georgia (online or onsite) is aimed at intermediate-level professionals who wish to understand and apply AI techniques for optimizing semiconductor fabrication processes.
By the end of this training, participants will be able to:
Understand AI methodologies for process optimization in chip fabrication.
Implement AI models to enhance yield and reduce defects.
Analyze process data to identify key parameters for optimization.
Apply machine learning techniques to fine-tune semiconductor manufacturing processes.
This instructor-led, live training in Georgia (online or onsite) is aimed at intermediate-level participants who wish to automate and manage machine learning workflows, including model training, validation, and deployment using Apache Airflow.
By the end of this training, participants will be able to:
Set up Apache Airflow for machine learning workflow orchestration.
Automate data preprocessing, model training, and validation tasks.
Integrate Airflow with machine learning frameworks and tools.
Deploy machine learning models using automated pipelines.
Monitor and optimize machine learning workflows in production.
This instructor-led, live training in Georgia (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.
TinyML is the practice of deploying machine learning models on highly resource-constrained hardware.
This instructor-led, live training (online or onsite) is aimed at advanced-level practitioners who wish to optimize TinyML models for low-latency, memory-efficient deployment on embedded devices.
Upon completing this training, participants will be able to:
Apply quantization, pruning, and compression techniques to reduce model size without sacrificing accuracy.
Benchmark TinyML models for latency, memory consumption, and energy efficiency.
Implement optimized inference pipelines on microcontrollers and edge devices.
Evaluate trade-offs between performance, accuracy, and hardware constraints.
Format of the Course
Instructor-led presentations supported by technical demonstrations.
Practical optimization exercises and comparative performance testing.
Hands-on implementation of TinyML pipelines in a controlled lab environment.
Course Customization Options
For tailored training aligned with specific hardware platforms or internal workflows, please contact us to customize the program.
This instructor-led, live training in Georgia (online or onsite) is aimed at advanced-level professionals who wish to explore state-of-the-art XAI techniques for deep learning models, with a focus on building interpretable AI systems.
By the end of this training, participants will be able to:
Understand the challenges of explainability in deep learning.
Implement advanced XAI techniques for neural networks.
Interpret decisions made by deep learning models.
Evaluate the trade-offs between performance and transparency.
This instructor-led, live training in Georgia (online or onsite) is aimed at beginner-level professionals who wish to understand and apply AI technologies within the semiconductor manufacturing industry.
By the end of this training, participants will be able to:
Understand the basic principles of AI and how they apply to semiconductor manufacturing.
Identify areas within semiconductor manufacturing where AI can be effectively implemented.
Utilize AI tools and techniques to enhance production efficiency and quality control.
Implement basic AI models to optimize manufacturing processes.
This instructor-led training in Georgia guides professionals through containerizing complete ML pipelines using Docker. Participants will master building reproducible environments, orchestrating training and inference workloads, and implementing CI/CD for scalable MLOps deployments.
This instructor-led, live training in Georgia (online or onsite) is aimed at data scientists and developers who wish to use ML.NET machine learning models to automatically derive projections from executed data analysis for enterprise applications.
By the end of this training, participants will be able to:
Install ML.NET and integrate it into the application development environment.
Understand the machine learning principles behind ML.NET tools and algorithms.
Build and train machine learning models to perform predictions with the provided data smartly.
Evaluate the performance of a machine learning model using the ML.NET metrics.
Optimize the accuracy of the existing machine learning models based on the ML.NET framework.
Apply the machine learning concepts of ML.NET to other data science applications.
This instructor-led, live training in Georgia (online or onsite) is aimed at intermediate-level data professionals who wish to apply machine learning techniques to data-driven business problems, including sales forecasting and predictive modeling using neural networks.
By the end of this training, participants will be able to:
Understand the core concepts and types of machine learning.
Apply key algorithms for classification, regression, clustering, and association analysis.
Perform exploratory data analysis and data preparation using Python.
Use neural networks for nonlinear modeling tasks.
Implement predictive analytics for business forecasting, including sales data.
Evaluate and optimize model performance using visual and statistical techniques.
This instructor-led, live training in Georgia (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
This instructor-led, live training in Georgia (online or onsite) is aimed at intermediate-level to advanced-level cybersecurity professionals who wish to elevate their skills in AI-driven threat detection and incident response.
By the end of this training, participants will be able to:
Implement advanced AI algorithms for real-time threat detection.
Customize AI models for specific cybersecurity challenges.
Develop automation workflows for threat response.
Secure AI-driven security tools against adversarial attacks.
This instructor-led, live training in Georgia (online or onsite) is aimed at intermediate-level embedded systems engineers and AI developers who wish to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.
By the end of this training, participants will be able to:
Understand the fundamentals of TinyML and its benefits for edge AI applications.
Set up a development environment for TinyML projects.
Train, optimize, and deploy AI models on low-power microcontrollers.
Use TensorFlow Lite and Edge Impulse to implement real-world TinyML applications.
Optimize AI models for power efficiency and memory constraints.
This instructor-led, live training in Georgia (online or onsite) is aimed at beginner-level cybersecurity professionals who wish to learn how to leverage AI for improved threat detection and response capabilities.
By the end of this training, participants will be able to:
Understand AI applications in cybersecurity.
Implement AI algorithms for threat detection.
Automate incident response with AI tools.
Integrate AI into existing cybersecurity infrastructure.
This instructor-led, live training in Georgia (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.
This instructor-led, live training in Georgia (online or onsite) is aimed at intermediate-level data analysts who wish to learn how to use RapidMiner to estimate and project values and utilize analytical tools for time series forecasting.
By the end of this training, participants will be able to:
Learn to apply the CRISP-DM methodology, select appropriate machine learning algorithms, and enhance model construction and performance.
Use RapidMiner to estimate and project values, and utilize analytical tools for time series forecasting.
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.
Develop practical proficiency in applying Machine Learning methods with Python at this Georgia training. This course covers core algorithms like regression, classification, and clustering, teaching you to make modeling decisions, interpret outputs, and validate results using real-world examples.
Applied AI from Scratch in Python equips programmers and data analysts with foundational techniques for building machine learning solutions from the ground up using Python. Covers core principles of supervised learning classification and regression, unsupervised learning clustering and anomaly detection, and advanced neural network architectures. Examines proven methods for working with scikit-learn, Apache Spark MLlib, and Jupyter notebooks for hands-on AI development. Helps professionals implement practical ML models, evaluate algorithm limitations, and complete applied projects for real-world problem solving.
Deep Reinforcement Learning (DRL) combines reinforcement learning principles with deep learning architectures to enable agents to make decisions through interaction with their environments. It underpins many modern AI advancements such as self-driving vehicles, robotics control, algorithmic trading, and adaptive recommendation systems. DRL allows an artificial agent to learn strategies, optimize policies, and make autonomous decisions based on trial and error using reward-based learning.
This instructor-led, live training (online or onsite) is aimed at intermediate-level developers and data scientists who wish to learn and apply Deep Reinforcement Learning techniques to build intelligent agents capable of autonomous decision-making in complex environments.
By the end of this training, participants will be able to:
Understand the theoretical foundations and mathematical principles of Reinforcement Learning.
Implement key RL algorithms including Q-Learning, Policy Gradients, and Actor-Critic methods.
Build and train Deep Reinforcement Learning agents using TensorFlow or PyTorch.
Apply DRL to real-world applications such as games, robotics, and decision optimization.
Troubleshoot, visualize, and optimize training performance using modern tools.
Format of the Course
Interactive lecture and guided discussion.
Hands-on exercises and practical implementations.
Live coding demonstrations and project-based applications.
Course Customization Options
To request a customized version of this course (e.g., using PyTorch instead of TensorFlow), please contact us to arrange.
Exploring artificial intelligence fundamentals reveals how intelligent technology reshapes digital strategy, automation, and decision making across enterprise operations. Examines core concepts spanning AI history, problem-solving frameworks, knowledge representation, uncertain reasoning, and machine learning paradigms alongside communication, perception, and autonomous action. Guides executives and architects to evaluate AI-driven transformation opportunities, assess emerging technology trends, and integrate practical intelligent solutions to accelerate business agility.
This course covers AI (emphasizing Machine Learning and Deep Learning) in Automotive Industry. It helps to determine which technology can be (potentially) used in multiple situation in a car: from simple automation, image recognition to autonomous decision making.
This 8-day programme provides a complete journey from strong Python engineering foundations to advanced AI system design. Participants develop disciplined coding practices, master statistical and deep learning methods and build production-ready generative AI and agent-based systems. The focus is on reliability, evaluation, safety and real-world deployment rather than experimentation alone.
This three-day program in Georgia covers the theory and practice of Artificial Neural Networks, Machine Learning, and Deep Learning. Participants will explore network architectures, learning mechanisms, and mathematical foundations, moving from basic perceptrons to advanced deep learning techniques.
Elevate your data science expertise with this comprehensive Machine Learning training course covering core algorithms including Naive Bayes, Decision Trees, Neural Networks, Support Vector Machines, and Clustering techniques. Gain hands-on experience with theoretical foundations and practical application using real-world examples. Ideal for data analysts, software engineers, AI enthusiasts, and business professionals seeking to apply machine learning solutions. Master classification performance metrics, cross-validation, bias-variance trade-off, and deep learning fundamentals to build robust predictive models.
This instructor-led, live training in Georgia (online or onsite) provides an introduction into the field of pattern recognition and machine learning. It touches on practical applications in statistics, computer science, signal processing, computer vision, data mining, and bioinformatics.
By the end of this training, participants will be able to:
Apply core statistical methods to pattern recognition.
Use key models like neural networks and kernel methods for data analysis.
Implement advanced techniques for complex problem-solving.
Improve prediction accuracy by combining different models.
This instructor-led, live training in Georgia (online or onsite) is aimed at data scientists who wish to use TensorFlow to analyze potential fraud data.
By the end of this training, participants will be able to:
Create a fraud detection model in Python and TensorFlow.
Build linear regressions and linear regression models to predict fraud.
Develop an end-to-end AI application for analyzing fraud data.
This instructor-led live training in Georgia covers AI, machine learning, and deep learning fundamentals. Participants will use Python, Keras, and TensorFlow to build practical telecom models, including credit risk and churn prediction, gaining hands-on skills for real-world data science applications.
This practical, instructor-led training is designed as a natural continuation of the Python for Data Analysis course.
It introduces participants to the core concepts of Machine Learning and shows how these can be applied directly to data analysis tasks such as prediction, classification, and segmentation.
The focus is on understanding how Machine Learning works in practice, using familiar tools such as Python, Pandas, and Jupyter Notebook, without requiring an advanced mathematical background.
This instructor-led, live training in Georgia (online or onsite) is aimed at developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes.
By the end of this training, participants will be able to:
Install and configure Kubeflow on premise and in the cloud.
Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
Run entire machine learning pipelines on diverse architectures and cloud environments.
Using Kubeflow to spawn and manage Jupyter notebooks.
Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.
This instructor-led, live training in Georgia (online or onsite) is aimed at engineers who wish to evaluate the approaches and tools available today to make an intelligent decision on the path forward in adopting MLOps within their organization.
By the end of this training, participants will be able to:
Install and configure various MLOps frameworks and tools.
Assemble the right kind of team with the right skills for constructing and supporting an MLOps system.
Prepare, validate and version data for use by ML models.
Understand the components of an ML Pipeline and the tools needed to build one.
Experiment with different machine learning frameworks and servers for deploying to production.
Operationalize the entire Machine Learning process so that it's reproduceable and maintainable.
This instructor-led, live training in Georgia (online or onsite) is aimed at intermediate-level data analysts, developers, or aspiring data scientists who wish to apply machine learning techniques in Python to extract insights, make predictions, and automate data-driven decisions.
By the end of this course, participants will be able to:
Understand and differentiate key machine learning paradigms.
Explore data preprocessing techniques and model evaluation metrics.
Apply machine learning algorithms to solve real-world data problems.
Use Python libraries and Jupyter notebooks for hands-on development.
Build models for prediction, classification, recommendation, and clustering.
This instructor-led, live training in Georgia (online or onsite) is aimed at developers and data scientists who wish to use Tensorflow 2.x to build predictors, classifiers, generative models, neural networks and so on.
By the end of this training, participants will be able to:
Install and configure TensorFlow 2.x.
Understand the benefits of TensorFlow 2.x over previous versions.
Build deep learning models.
Implement an advanced image classifier.
Deploy a deep learning model to the cloud, mobile and IoT devices.
This 35-hour course in Georgia covers deep neural network fundamentals, including CNNs, RNNs, and generative models like GANs. Participants gain hands-on experience with Theano and TensorFlow, learning to build, train, and deploy production-grade deep learning models for real-world applications.
I thoroughly enjoyed the training and appreciated the deeper dive into the subject of Machine Learning. I appreciated the balance between theory and practical applications, especially the hands-on coding sessions. The trainer provided engaging examples and well-designed exercises that enhanced the learning experience. The course covered a wide range of topics, and Abhi demonstrated excellent expertise by answering all questions with clarity and ease.
Valentina
Course - Machine Learning
The training provided an interesting overview of deep learning models and related methods. The topic was quite new to me, but now I feel like I actually have an idea of what AI and ML can involve, what these terms consist of and how they can be used advantageously. In general, I liked the approach of starting with the statistical background and the basic learning models, such as linear regression, especially emphasizing the exercises in between.
Konstantin - REGNOLOGY ROMANIA S.R.L.
Course - Fundamentals of Artificial Intelligence (AI) and Machine Learning
Interesting knowledge
Gabriel - MINDEF
Course - Machine Learning with Python – 4 Days
Even with having to miss a day due to customer meetings, I feel I have a much clearer understanding of the processes and techniques used in Machine Learning and when I would use one approach over another. Our challenge now is to practice what we have learned and start to apply it to our problem domain
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