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Machine Learning Training Courses

Machine Learning Training Courses

I corsi di formazione in machine learning (ML) in diretta condotti da istruttori dimostrano attraverso pratiche pratiche come applicare tecniche di apprendimento automatico e strumenti per risolvere problemi reali in vari settori. I corsi NobleProg ML coprono diversi linguaggi di programmazione e framework, inclusi Python, linguaggio R e Matlab. I corsi di Machine Learning sono offerti per una serie di applicazioni industriali, tra cui finanza, servizi bancari e assicurativi e riguardano i fondamenti del Machine Learning e approcci più avanzati come Deep Learning. La formazione Machine Learning è disponibile come "allenamento dal vivo in loco" o "formazione dal vivo a distanza". La formazione on-site in loco può essere svolta localmente presso la sede del cliente in Italia o nei centri di formazione aziendale NobleProg in Italia . La formazione in remoto dal vivo viene effettuata tramite un desktop remoto interattivo. NobleProg: il tuo fornitore di formazione locale

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Sottocategorie Machine Learning

Deep Learning Training CoursesDeep Learning
PredictionIO Training CoursesPredictionIO
Apache SystemML Training CoursesApache SystemML
Pattern Recognition Training CoursesPattern Recognition
RapidMiner Training CoursesRapidMiner

Schema generale del corso Machine Learning

CodiceNomeDurataPanoramica
aiintArtificial Intelligence Overview7 oreThis course has been created for managers, solutions architects, innovation officers, CTOs, software architects and anyone who is interested in an overview of applied artificial intelligence and the nearest forecast for its development.
mliosMachine Learning on iOS14 oreIn 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
textsumText Summarization with Python14 oreIn Python Machine Learning, the Text Summarization feature is able to read the input text and produce a text summary. This capability is available from the command-line or as a Python API/Library. One exciting application is the rapid creation of executive summaries; this is particularly useful for organizations that need to review large bodies of text data before generating reports and presentations.

In this instructor-led, live training, participants will learn to use Python to create a simple application that auto-generates a summary of input text.

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

- Use a command-line tool that summarizes text.
- Design and create Text Summarization code using Python libraries.
- Evaluate three Python summarization libraries: sumy 0.7.0, pysummarization 1.0.4, readless 1.0.17

Audience

- Developers
- Data Scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
undnnUnderstanding Deep Neural Networks35 oreThis course begins with giving you conceptual knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications).

Part-1(40%) of this training is more focus on fundamentals, but will help you choosing 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 - 2nd Generation API of Google's open source software library for Deep Learning. The examples and handson would 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 on 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

Not all the topics would be covered in a public classroom with 35 hours duration due to the vastness of the subject.

The Duration of the complete course will be around 70 hours and not 35 hours.
opennlpOpenNLP for Text Based Machine Learning14 oreThe Apache OpenNLP library is a machine learning based toolkit for processing natural language text. It supports the most common NLP tasks, such as language detection, tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing and coreference resolution.

In this instructor-led, live training, participants will learn how to create models for processing text based data using OpenNLP. Sample training data as well customized data sets will be used as the basis for the lab exercises.

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

- Install and configure OpenNLP
- Download existing models as well as create their own
- Train the models on various sets of sample data
- Integrate OpenNLP with existing Java applications

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
mlbankingpython_Machine Learning for Banking (with Python)21 oreMachine 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
mlbankingrMachine Learning for Banking (with R)28 oreIn 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. R will be used as the programming language.

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 live projects.

Audience

- Developers
- Data scientists
- Banking professionals with a technical background

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
matlabdlMatlab for Deep Learning14 oreIn this instructor-led, live training, participants will learn how to use Matlab to design, build, and visualize a convolutional neural network for image recognition.

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

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

Audience

- Developers
- Engineers
- Domain experts

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
tensorflowservingTensorFlow Serving7 oreTensorFlow Serving is a system for serving machine learning (ML) models to production.

In this instructor-led, live training, participants will learn how to configure and use TensorFlow Serving to deploy and manage ML models in a production environment.

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

- Train, export and serve various TensorFlow models
- Test and deploy algorithms using a single architecture and set of APIs
- Extend TensorFlow Serving to serve other types of models beyond TensorFlow models

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
pythontextmlPython: Machine Learning with Text21 oreIn this instructor-led, live training, participants will learn how to use the right machine learning and NLP (Natural Language Processing) techniques to extract value from text-based data.

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

- Solve text-based data science problems with high-quality, reusable code
- Apply different aspects of scikit-learn (classification, clustering, regression, dimensionality reduction) to solve problems
- Build effective machine learning models using text-based data
- Create a dataset and extract features from unstructured text
- Visualize data with Matplotlib
- Build and evaluate models to gain insight
- Troubleshoot text encoding errors

Audience

- Developers
- Data Scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
mlfinancepythonMachine Learning for Finance (with Python)21 oreMachine 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
encogintroEncog: Introduction to Machine Learning14 oreEncog is an open-source machine learning framework for Java and .Net.

In this instructor-led, live training, participants will learn how to create various neural network components using ENCOG. Real-world case studies will be discussed and machine language based solutions to these problems will be explored.

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

- Prepare data for neural networks using the normalization process
- Implement feed forward networks and propagation training methodologies
- Implement classification and regression tasks
- Model and train neural networks using Encog's GUI based workbench
- Integrate neural network support into real-world applications

Audience

- Developers
- Analysts
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
encogadvEncog: Advanced Machine Learning14 oreEncog is an open-source machine learning framework for Java and .Net.

In this instructor-led, live training, participants will learn advanced machine learning techniques for building accurate neural network predictive models.

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

- Implement different neural networks optimization techniques to resolve underfitting and overfitting
- Understand and choose from a number of neural network architectures
- Implement supervised feed forward and feedback networks

Audience

- Developers
- Analysts
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
radvmlAdvanced Machine Learning with R21 oreIn this instructor-led, live training, participants will learn advanced techniques for Machine Learning with R as they step through the creation of a real-world application.

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

- Use techniques as hyper-parameter tuning and deep learning
- Understand and implement unsupervised learning techniques
- Put a model into production for use in a larger application

Audience

- Developers
- Analysts
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
pythonadvmlPython for Advanced Machine Learning21 oreIn this instructor-led, live training, participants will learn the most relevant and cutting-edge machine learning techniques in Python as they build a series of demo applications involving image, music, text, and financial data.

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

- Implement machine learning algorithms and techniques for solving complex problems
- Apply deep learning and semi-supervised learning to applications involving image, music, text, and financial data
- Push Python algorithms to their maximum potential
- Use libraries and packages such as NumPy and Theano

Audience

- Developers
- Analysts
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
openfaceOpenFace: Creating Facial Recognition Systems14 oreOpenFace is Python and Torch based open-source, real-time facial recognition software based on Google's FaceNet research.

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

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

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

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
embeddingprojectorEmbedding Projector: Visualizing Your Training Data14 oreEmbedding Projector is an open-source web application for visualizing the data used to train machine learning systems. Created by Google, it is part of TensorFlow.

This instructor-led, live training introduces the concepts behind Embedding Projector and walks participants through the setup of a demo project.

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

- Explore how data is being interpreted by machine learning models
- Navigate through 3D and 2D views of data to understand how a machine learning algorithm interprets it
- Understand the concepts behind Embeddings and their role in representing mathematical vectors for images, words and numerals.
- Explore the properties of a specific embedding to understand the behavior of a model
- Apply Embedding Project to real-world use cases such building a song recommendation system for music lovers

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
t2tT2T: Creating Sequence to Sequence Models for Generalized Learning7 oreTensor2Tensor (T2T) is a modular, extensible library for training AI models in different tasks, using different types of training data, for example: image recognition, translation, parsing, image captioning, and speech recognition. It is maintained by the Google Brain team.

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

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

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

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
dlfornlpDeep Learning for NLP (Natural Language Processing)28 oreDeep Learning for NLP allows a machine to learn simple to complex language processing. Among the tasks currently possible are language translation and caption generation for photos. DL (Deep Learning) is a subset of ML (Machine Learning). Python is a popular programming language that contains libraries for Deep Learning for NLP.

In this instructor-led, live training, participants will learn to use Python libraries for NLP (Natural Language Processing) as they create an application that processes a set of pictures and generates captions.

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

- Design and code DL for NLP using Python libraries
- Create Python code that reads a substantially huge collection of pictures and generates keywords
- Create Python Code that generates captions from the detected keywords

Audience

- Programmers with interest in linguistics
- Programmers who seek an understanding of NLP (Natural Language Processing)

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
mlfinancerMachine Learning for Finance (with R)28 oreMachine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.

In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry. R will be used as the programming language.

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 R

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
snorkelSnorkel: Rapidly Process Training Data7 oreSnorkel is a system for rapidly creating, modeling, and managing training data. It focuses on accelerating the development of structured or "dark" data extraction applications for domains in which large labeled training sets are not available or easy to obtain.

In this instructor-led, live training, participants will learn techniques for extracting value from unstructured data such as text, tables, figures, and images through modeling of training data with Snorkel.

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

- Programmatically create training sets to enable the labeling of massive training sets
- Train high-quality end models by first modeling noisy training sets
- Use Snorkel to implement weak supervision techniques and apply data programming to weakly-supervised machine learning systems

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
ML_LBGMachine Learning – Data science21 oreThis classroom based training session will explore machine learning tools with (suggested) Python. Delegates will have computer based examples and case study exercises to undertake.
appaiApplied AI from Scratch28 oreThis is a 4 day course introducing AI and it's application. There is an option to have an additional day to undertake an AI project on completion of this course.
dlfortelecomwithpythonDeep Learning for Telecom (with Python)28 oreMachine 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

Audience

- Developers
- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice
rapidminerRapidMiner for Machine Learning and Predictive Analytics14 oreRapidMiner is an open source data science software platform for rapid application prototyping and development. It includes an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics.

In this instructor-led, live training, participants will learn how to use RapidMiner Studio for data preparation, machine learning, and predictive model deployment.

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

- Install and configure RapidMiner
- Prepare and visualize data with RapidMiner
- Validate machine learning models
- Mashup data and create predictive models
- Operationalize predictive analytics within a business process
- Troubleshoot and optimize RapidMiner

Audience

- Data scientists
- Engineers
- Developers

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.
aicityplanningArtificial Intelligence for City Planning14 oreWhat will cities look like in the future? How can Artificial Intelligence (AI) be used to improve city planning? How can AI be used to make cities more efficient, livable, safer and environmentally friendly?

In this instructor-led, live training (onsite or remote), we examine the various technologies that make up AI, as well as the skill sets and mental framework required to put them to use for city planning. We also cover tools and approaches for gathering and organizing relevant data for use in AI, including data mining.

Audience

- City planners
- Architects
- Developers
- Transportation officials

Format of the Course

- Part lecture, part discussion, and a series of interactive exercises.

Note

- To request a customized training for this course, please contact us to arrange.
dlformedicineDeep Learning for Medicine14 oreMachine Learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep Learning is a subfield of Machine Learning which attempts to mimic the workings of the human brain in making decisions. It is trained with data in order to automatically provide solutions to problems. Deep Learning provides vast opportunities for the medical industry which is sitting on a data goldmine.

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

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

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

Audience

- Managers
- Medical professionals in leadership roles

Format of the course

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

Note

- To request a customized training for this course, please contact us to arrange.
algebraformlAlgebra for Machine Learning14 oreLinear 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.
Nue_LBGNeural computing – Data science14 oreThis classroom based training session will contain presentations and computer based examples and case study exercises to undertake with relevant neural and deep network libraries
mllbgMachine Learning in business – AI/Robotics14 oreThis classroom based training session will explore machine learning techniques, with computer based examples and case study solving exercises using a relevant programme languauge

Prossimi corsi Machine Learning

CorsoCourse DatePrezzo del corso [A distanza / In classe]
DeepLearning4J for Image Recognition - RomaMer, 2018-12-12 09:305250EUR / 6050EUR
DeepLearning4J for Image Recognition - BolognaMer, 2018-12-12 09:305250EUR / 6050EUR
DeepLearning4J for Image Recognition - GenovaMer, 2018-12-12 09:305250EUR / 6050EUR
DeepLearning4J for Image Recognition - NapoliMer, 2018-12-19 09:305250EUR / 6050EUR
DeepLearning4J for Image Recognition - MilanoLun, 2018-12-31 09:305250EUR / 6050EUR

Other regions

RomaMilanoNapoliTorinoGenovaBologna

Other countries

These courses are also available in other countries ››

Consulting

Machine Learning Consulting
Fine settimana Machine Learning corsi, Sera Machine Learning training, Machine Learning centro di addestramento, Machine Learning con istruttore, Fine settimana Machine Learning training, Sera Machine Learning corsi, Machine Learning coaching, Machine Learning istruttore, Machine Learning trainer, Machine Learning corsi di formazione, Machine Learning classi, Machine Learning in loco, Machine Learning corsi privati, Machine Learning training individuale

Corsi scontati

CorsoSedeCourse DatePrezzo del corso [A distanza / In classe]
Drupal and SolrMilanoLun, 2018-11-26 09:302700EUR / 3300EUR
Business Rule Management (BRMS) with DroolsBolognaVen, 2018-12-07 09:301575EUR / 1975EUR
Cloud Computing OverviewRomaGio, 2018-12-13 09:301350EUR / 1750EUR
OCEB2 OMG Certified Expert in BPM - Fundamental Exam PreparationMilanoLun, 2018-12-17 09:306300EUR / 7300EUR
Business Plan building with Business Motivation ModelBolognaGio, 2019-01-24 09:302700EUR / 3300EUR

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