Machine Learning Training in Kerela

This instructor-led, live training in Kerela (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 Kerela (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 Kerela (online or onsite) is intended for intermediate-level AI developers, machine learning engineers, and system architects who wish to optimize AI models for edge deployment.

Upon completion of this training, participants will be capable of:

• Grasping the challenges and prerequisites associated with deploying AI models on edge devices.
• Applying model compression techniques to decrease the size and complexity of AI models.
• Leveraging quantization methods to boost model efficiency on edge hardware.
• Implementing pruning and other optimization strategies to enhance model performance.
• Deploying optimized AI models across diverse edge devices.

This instructor-led live training in Kerela (online or onsite) targets intermediate-level developers, data scientists, and tech enthusiasts seeking practical skills in deploying AI models on edge devices for diverse applications.

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

• Comprehend the core principles of Edge AI and its advantages.
• Establish and configure the edge computing environment.
• Develop, train, and optimize AI models specifically for edge deployment.
• Deploy practical AI solutions on edge devices.
• Assess and enhance the performance of models deployed on the edge.
• Tackle ethical and security issues inherent in Edge AI applications.

This instructor-led, live training in Kerela (online or on-site) 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 provides a framework for automating the training, validation, packaging, and deployment of machine learning models through containerized pipelines and GitOps workflows.

This instructor-led live training, available online or onsite, targets intermediate-level practitioners looking to build automated, scalable MLOps pipelines on Kubernetes.

Upon completion of this training, participants will be able to:

• Design end-to-end CI/CD pipelines for machine learning.
• Implement GitOps workflows for model deployment and versioning.
• Automate the training, testing, and packaging of ML models.
• Integrate monitoring, alerting, and rollback strategies.

Course Format

• Instructor-guided presentations and technical deep dives.
• Hands-on exercises focused on building real-world CI/CD workflows.
• Live-lab practice for deploying ML workloads to Kubernetes.

Customization Options

• Organizations can 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:

• Explore the Kubeflow ecosystem and its core components.
• Develop reproducible workflows using Kubeflow Pipelines.
• Execute scalable training jobs on Kubernetes.
• Efficiently serve machine learning models via Kubeflow Serving.

Course Format

• Guided presentations coupled with collaborative discussions.
• Hands-on labs utilizing real Kubeflow components.
• Practical exercises to construct end-to-end ML workflows.

Course Customization Options

• Tailored versions of this training can be arranged to align with your team’s technology stack and project requirements.

TinyML involves the deployment of optimized machine learning models onto edge devices with limited resources.

This instructor-led live training, available both online and onsite, targets advanced technical professionals aiming to design, optimize, and implement comprehensive TinyML pipelines.

Upon completing this training, participants will acquire the skills to:

• Gather, prepare, and manage datasets for TinyML use cases.
• Train and fine-tune models for low-power microcontrollers.
• Transform models into lightweight formats compatible with edge devices.
• Deploy, test, and monitor TinyML applications on actual hardware.

Course Delivery Format

• Lectures guided by instructors alongside technical discussions.
• Practical lab sessions and iterative experimentation.
• Hands-on deployment on microcontroller-based platforms.

Customization Options

• For tailored training involving specific toolchains, hardware boards, or internal workflows, please reach out to us to make arrangements.

This instructor-led, live training in Kerela (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 Kerela (online or on-site) is designed for advanced-level professionals aiming to master the technologies underlying autonomous systems.

Upon completion of this training, participants will be equipped to:

• Design and deploy AI models for autonomous decision-making.
• Create control algorithms for autonomous navigation and obstacle avoidance.
• Guarantee safety and reliability in AI-powered autonomous systems.
• Integrate autonomous systems with existing robotics and AI frameworks.

This instructor-led, live training in Kerela (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 involves deploying machine learning models on low-power, resource-constrained devices at the network edge.

This instructor-led live training, available both online and onsite, is designed for advanced professionals aiming to secure TinyML pipelines and integrate privacy-preserving techniques into edge AI applications.

Upon completing this course, participants will be capable of:

• Identifying security risks specific to on-device TinyML inference.
• Implementing privacy-preserving mechanisms for edge AI deployments.
• Strengthening TinyML models and embedded systems against adversarial threats.
• Applying best practices for secure data handling in constrained environments.

Course Format

• Interactive lectures accompanied by expert-led discussions.
• Practical exercises focusing on real-world threat scenarios.
• Hands-on implementation using embedded security tools and TinyML frameworks.

Course Customization Options

• Organizations can request a customized version of this training to meet their specific security and compliance requirements.

This instructor-led live training in Kerela (offered online or onsite) targets advanced-level professionals eager to deepen their understanding of machine learning models, sharpen their hyperparameter tuning capabilities, and learn effective model deployment techniques using Google Colab.

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

• Develop advanced machine learning models using popular frameworks like Scikit-learn and TensorFlow.
• Optimize model performance through advanced hyperparameter tuning.
• Deploy machine learning models for real-world applications using Google Colab.
• Collaborate and oversee large-scale machine learning projects within Google Colab.

This instructor-led, live training in Kerela (online or onsite) is designed for intermediate-level professionals who wish to apply AI techniques to optimize yield management in semiconductor manufacturing.

Upon completing this training, participants will be equipped to:

• Analyze production data to pinpoint factors influencing yield rates.
• Deploy AI algorithms to strengthen yield management processes.
• Optimize production parameters to minimize defects and enhance yields.
• Integrate AI-driven yield management into existing production workflows.

TinyML serves as a framework for deploying machine learning models onto low-power microcontrollers and embedded platforms within robotics and autonomous systems.

This instructor-led live training, available online or onsite, targets advanced professionals seeking to incorporate TinyML-based perception and decision-making capabilities into autonomous robots, drones, and intelligent control systems.

After completing this course, participants will be equipped to:

• Design optimized TinyML models tailored for robotics applications.
• Implement on-device perception pipelines for real-time autonomy.
• Integrate TinyML into existing robotic control frameworks.
• Deploy and test lightweight AI models on embedded hardware platforms.

Format of the Course

• Technical lectures combined with interactive discussions.
• Hands-on labs focusing on embedded robotics tasks.
• Practical exercises simulating real-world autonomous workflows.

Course Customization Options

• For organization-specific robotics environments, customization can be arranged upon request.

This instructor-led, live training in Kerela (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 Kerela (available online or onsite) is targeted at intermediate-level professionals who wish to apply AI-driven predictive maintenance techniques in semiconductor manufacturing to boost production efficiency and reduce unexpected equipment failures.

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

• Implement AI models for predicting equipment failures in semiconductor manufacturing.
• Analyse 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 Kerela (online or onsite) is intended for advanced professionals seeking 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, conducted in Kerela (either online or onsite), targets intermediate-level data scientists and developers who aim 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 Kerela (online or onsite) is tailored for intermediate-level professionals seeking to comprehend and apply AI techniques for optimizing semiconductor fabrication processes.

Upon completing this training, participants will be able to:

• Grasp AI methodologies used for process optimization in chip manufacturing.
• Deploy AI models to improve yield and minimize defects.
• Analyze process data to pinpoint critical parameters for optimization.
• Utilize machine learning techniques to fine-tune semiconductor production workflows.

This instructor-led, live training in Kerela (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 Kerela (online or onsite) targets intermediate-level data scientists and developers aiming to efficiently apply machine learning algorithms using Google Colab.

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 involves the deployment of machine learning models onto hardware with stringent resource limitations.

This instructor-led, live training (available online or onsite) is designed for advanced practitioners seeking to optimize TinyML models for low-latency, memory-efficient deployment on embedded devices.

Upon completing this training, participants will be able to:

• Utilize quantization, pruning, and compression methods to minimize model size while preserving accuracy.
• Benchmark TinyML models to assess latency, memory usage, and energy efficiency.
• Deploy optimized inference pipelines on microcontrollers and edge devices.
• Assess the trade-offs between performance, accuracy, and hardware constraints.

Course Format

• Instructor-led presentations complemented by technical demonstrations.
• Practical optimization exercises and comparative performance testing.
• Hands-on implementation of TinyML pipelines within a controlled lab environment.

Customization Options

• For training tailored to specific hardware platforms or internal workflows, please contact us to customize the program.

This instructor-led, live training in Kerela (online or onsite) targets 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 Kerela (online or on-site) is designed for 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.

Docker serves as a containerization platform designed to create reproducible, portable, and scalable environments for machine learning systems.

This instructor-led live training, available both online and onsite, targets intermediate to advanced technical professionals who aim to containerize and operationalize comprehensive ML pipelines using Docker.

After completing this training, participants will be equipped to:

• Containerize ML training, validation, and inference workloads.
• Design and orchestrate end-to-end ML pipelines utilizing Docker and complementary tools.
• Implement versioning, reproducibility, and CI/CD processes for ML components.
• Deploy, monitor, and scale ML services within containerized environments.

Format of the Course

• Interactive lectures accompanied by practical demonstrations.
• Hands-on exercises centered on constructing real-world ML pipeline components.
• Live-lab implementation for end-to-end containerized workflows.

Course Customization Options

• For customized training tailored to specific ML infrastructure requirements, please contact us to discuss available options.

This instructor-led live training in Kerela (online or onsite) targets data scientists and developers who wish to use ML.NET 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 Kerela (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 modelling 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 modelling tasks.
• Implement predictive analytics for business forecasting, including sales data.
• Evaluate and optimise model performance using visual and statistical techniques.

This instructor-led, live training in Kerela (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 Kerela (online or onsite) is targeted at intermediate to advanced-level cybersecurity professionals who wish to enhance 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 Kerela (online or onsite) is designed for intermediate-level embedded systems engineers and AI developers looking to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

Upon completing this training, participants will be able to:

• Comprehend the core principles of TinyML and its advantages for edge AI applications.
• Configure a development environment suitable for TinyML projects.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Utilize TensorFlow Lite and Edge Impulse to build real-world TinyML applications.
• Enhance AI models for power efficiency and manage memory constraints effectively.

This instructor-led, live training in Kerela (online or onsite) targets entry-level cybersecurity professionals eager to learn how to harness AI to enhance their threat detection and response capabilities.

Upon completing this training, participants will be able to:

• Grasp the applications of AI in cybersecurity.
• Deploy AI algorithms for threat detection.
• Automate incident response using AI tools.
• Integrate AI into current cybersecurity infrastructure.

This instructor-led, live training in Kerela (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 Kerela (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 (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.

This instructor-led, live training in Kerela (online or onsite) is designed for beginner-level engineers and data scientists who want to grasp the fundamentals of TinyML, explore its practical uses, and deploy AI models on microcontrollers.

Upon completion of this training, participants will be able to:

• Grasp the core concepts of TinyML and its importance.
• Deploy lightweight AI models on microcontrollers and edge devices.
• Optimize and fine-tune machine learning models to minimize power usage.
• Implement TinyML in real-world scenarios such as gesture recognition, anomaly detection, and audio processing.

This course aims to build practical proficiency in applying Machine Learning methods. Leveraging the Python programming language and its extensive libraries, alongside numerous hands-on examples, it teaches how to utilise the key building blocks of Machine Learning. Participants will learn to make informed data modelling decisions, interpret algorithm outputs, and validate results effectively.

Our goal is to equip you with the confidence to use fundamental Machine Learning tools and to help you avoid the common pitfalls encountered in Data Science applications.

The course 'Applied AI from Scratch in Python' empowers programmers and data analysts by providing the essential techniques required to construct machine learning solutions entirely from the ground up using Python. It delves into the fundamental principles of supervised learning, including classification and regression, as well as unsupervised learning techniques such as clustering and anomaly detection. Additionally, the course explores advanced neural network architectures. Learners will examine proven methodologies for utilizing tools like scikit-learn, Apache Spark MLlib, and Jupyter notebooks to facilitate hands-on AI development. The program enables professionals to implement practical ML models, assess algorithmic limitations, and execute applied projects designed for real-world problem-solving.

Deep Reinforcement Learning (DRL) merges the principles of reinforcement learning with deep learning architectures, empowering agents to make decisions via interaction with their surroundings. It serves as the foundation for numerous contemporary AI innovations, including self-driving cars, robotics control, algorithmic trading, and adaptive recommendation systems. DRL enables artificial agents to learn strategies, optimize policies, and make autonomous decisions through trial and error using reward-based learning.

This instructor-led live training (available online or onsite) targets intermediate-level developers and data scientists keen on learning and applying Deep Reinforcement Learning techniques to construct intelligent agents capable of autonomous decision-making within complex environments.

Upon completion of this training, participants will be able to:

• Comprehend the theoretical foundations and mathematical principles of Reinforcement Learning.
• Implement core RL algorithms, including Q-Learning, Policy Gradients, and Actor-Critic methods.
• Construct and train Deep Reinforcement Learning agents utilizing TensorFlow or PyTorch.
• Apply DRL to real-world scenarios such as gaming, robotics, and decision optimization.
• Troubleshoot, visualize, and optimize training performance using modern tools.

Format of the Course

• Interactive lectures coupled with guided discussions.
• 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., substituting PyTorch for TensorFlow), please contact us to arrange.

Understanding the fundamentals of artificial intelligence demonstrates how intelligent technology is transforming digital strategy, automation, and decision-making across enterprise operations. This course examines core concepts including the history of AI, problem-solving frameworks, knowledge representation, reasoning under uncertainty, and machine learning paradigms, alongside communication, perception, and autonomous actions. It equips executives and architects to evaluate opportunities for AI-driven transformation, assess emerging technology trends, and implement practical intelligent solutions to enhance business agility.

This course delves into the application of AI—particularly focusing on Machine Learning and Deep Learning—within the automotive industry. It explores which technologies can be potentially deployed across various automotive scenarios, ranging from basic automation and image recognition to complex autonomous decision-making.

Spanning eight days, this intensive programme offers a comprehensive path from solid Python engineering principles to sophisticated AI system architecture. Participants will cultivate disciplined coding habits, gain expertise in statistical and deep learning techniques, and construct production-ready generative AI and agent-based solutions. The curriculum emphasizes reliability, evaluation methodologies, safety protocols, and practical deployment over mere experimentation.

Artificial Neural Networks are computational models employed in the development of Artificial Intelligence (AI) systems capable of executing "intelligent" tasks. These networks are widely utilized in Machine Learning (ML) applications, which serve as a practical implementation of AI. Furthermore, Deep Learning represents a specialized subset of Machine Learning.

Boost your data science capabilities with this extensive Machine Learning training course, which explores core algorithms such as Naive Bayes, Decision Trees, Neural Networks, Support Vector Machines, and Clustering techniques. Acquire practical skills alongside theoretical knowledge through real-world examples. This course is ideal for data analysts, software engineers, AI enthusiasts, and business professionals looking to implement machine learning solutions. Learn to master classification performance metrics, cross-validation, the bias-variance trade-off, and deep learning fundamentals to construct robust predictive models.

This instructor-led, live training in Kerela (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 Kerela (online or onsite) is designed for data scientists who wish to use TensorFlow to analyse 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 analysing fraud data.

Machine learning constitutes a segment of Artificial Intelligence that enables computers to learn from experience without explicit programming.

Deep learning is a specialized area of machine learning that employs data representation and structure methods, such as neural networks.

Python is a prominent high-level programming language renowned for its clear syntax and code readability.

In this instructor-led live training, participants will learn to implement deep learning models for the telecom sector using Python by constructing a deep learning credit risk model step by step.

Upon completing this training, participants will be capable of:

• Grasping the fundamental principles of deep learning.
• Identifying the applications and uses of deep learning within the telecom industry.
• Utilizing Python, Keras, and TensorFlow to develop deep learning models for telecom.
• Building their own deep learning customer churn prediction model using Python.

Course Format

• Interactive lectures and discussions.
• Numerous exercises and practice sessions.
• Hands-on implementation in a live-lab environment.

Customization Options

• To arrange a customized training for this course, please get in touch with us.

This practical, instructor-led session serves as a seamless follow-up to the Python for Data Analysis course.

It provides an introduction to the fundamental concepts of Machine Learning and demonstrates their direct application to data analysis tasks, including prediction, classification, and segmentation.

The course emphasizes practical understanding, utilizing familiar tools like Python, Pandas, and Jupyter Notebook, without necessitating an advanced background in mathematics.

This instructor-led, live training in Kerela (online or onsite) is designed for intermediate-level data analysts, developers, or aspiring data scientists who wish to leverage 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:

• Grasp and distinguish between key machine learning paradigms.
• Explore data preprocessing techniques and model evaluation metrics.
• Apply machine learning algorithms to solve real-world data challenges.
• Utilize Python libraries and Jupyter notebooks for practical development.
• Construct models for prediction, classification, recommendation, and clustering.

This instructor-led, live training in Kerela (online or on-site) is designed for developers and data scientists who intend to use TensorFlow 2.x to build predictors, classifiers, generative models, neural networks, and related applications.

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 course starts by providing conceptual knowledge of neural networks, machine learning algorithms, and deep learning (including algorithms and applications).

Part 1 (40%) of this training focuses more on fundamentals, but will help you choose the right technology: TensorFlow, Caffe, Theano, DeepDrive, Keras, etc.

Part 2 (20%) of this training introduces Theano, a Python library that makes writing deep learning models easy.

Part 3 (40%) of the training would be extensively based on TensorFlow - the API of Google's open-source software library for Deep Learning. The examples and hands-on sessions will all be made in TensorFlow.

Audience

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

After completing this course, delegates will:

• have a good understanding of deep neural networks (DNN), CNN and RNN
• 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