Machine Learning Training Courses

Machine Learning Training Courses

Local, instructor-led live Machine Learning 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 "onsite live training" or "remote live training". Onsite live training can be carried out locally on customer premises in Sweden or in NobleProg corporate training centers in Sweden. Remote live training is carried out by way of an interactive, remote desktop. NobleProg -- Your Local Training Provider

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Machine Learning Course Outlines

CodeNameDurationOverview
aiintArtificial Intelligence Overview7 hoursThis 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.
mlintroIntroduction to Machine Learning7 hoursThis training course is for people that would like to apply basic Machine Learning techniques in practical applications.

Audience

Data scientists and statisticians that have some familiarity with machine learning and know how to program R. The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give a practical introduction to machine learning to participants interested in applying the methods at work

Sector specific examples are used to make the training relevant to the audience.
appliedmlApplied Machine Learning14 hoursThis training course is for people that would like to apply Machine Learning in practical applications.

Audience

This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization.

The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work.

Sector specific examples are used to make the training relevant to the audience.
annmldtArtificial Neural Networks, Machine Learning, Deep Thinking21 hoursArtificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML.
mlrobot1Machine Learning for Robotics21 hoursThis course introduces machine learning methods in robotics applications.

It is a broad overview of existing methods, motivations and main ideas in the context of pattern recognition.

After a short theoretical background, participants will perform simple exercise using open source (usually R) or any other popular software.
mlfunpythonMachine Learning Fundamentals with Python14 hoursThe aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.

Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
MLFWR1Machine Learning Fundamentals with R14 hoursThe aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the R programming platform and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.

Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
deeplearning1Introduction to Deep Learning21 hoursThis course is general overview for Deep Learning without going too deep into any specific methods. It is suitable for people who want to start using Deep learning to enhance their accuracy of prediction.
matlabml1Introduction to Machine Learning with MATLAB21 hoursMATLAB is a numerical computing environment and programming language developed by MathWorks.
dladvAdvanced Deep Learning28 hoursMachine 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.
mlfsasMachine Learning Fundamentals with Scala and Apache Spark14 hoursThe aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Scala programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.

Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
dmmlrData Mining & Machine Learning with R14 hoursR is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining.
predioMachine Learning with PredictionIO21 hoursPredictionIO is an open source Machine Learning Server built on top of state-of-the-art open source stack.

Audience

This course is directed at developers and data scientists who want to create predictive engines for any machine learning task.
aiintrozeroFrom Zero to AI35 hoursThis course is created for people who have no previous experience in probability and statistics.
dl4jirDeepLearning4J for Image Recognition21 hoursDeeplearning4j is an Open-Source Deep-Learning Software for Java and Scala on Hadoop and Spark.

Audience

This course is meant for engineers and developers seeking to utilize DeepLearning4J in their image recognition projects.
tf101Deep Learning with TensorFlow21 hoursTensorFlow is a 2nd Generation API of Google's open source software library for Deep Learning. The system is designed to facilitate research in machine learning, and to make it quick and easy to transition from research prototype to production system.

Audience

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

After completing this course, delegates will:

- understand TensorFlow’s structure and deployment mechanisms
- be able to carry out installation / production environment / architecture tasks and configuration
- be able to assess code quality, perform debugging, monitoring
- be able to implement advanced production like training models, building graphs and logging
systemmlApache SystemML for Machine Learning14 hoursApache SystemML is a distributed and declarative machine learning platform.

SystemML provides declarative large-scale machine learning (ML) that aims at flexible specification of ML algorithms and automatic generation of hybrid runtime plans ranging from single node, in-memory computations, to distributed computations on Apache Hadoop and Apache Spark.

Audience

This course is suitable for Machine Learning researchers, developers and engineers seeking to utilize SystemML as a framework for machine learning.
dlvDeep Learning for Vision21 hoursAudience

This course is suitable for Deep Learning researchers and engineers interested in utilizing available tools (mostly open source ) for analyzing computer images

This course provide working examples.
aiautoArtificial Intelligence in Automotive14 hoursThis 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.
tfirTensorFlow for Image Recognition28 hoursThis course explores, with specific examples, the application of Tensor Flow to the purposes of image recognition

Audience

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

After completing this course, delegates will be able to:

- understand TensorFlow’s structure and deployment mechanisms
- carry out installation / production environment / architecture tasks and configuration
- assess code quality, perform debugging, monitoring
- implement advanced production like training models, building graphs and logging
tsflw2vNatural Language Processing with TensorFlow35 hoursTensorFlow™ is an open source software library for numerical computation using data flow graphs.

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

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

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

Audience

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

After completing this course, delegates will:

- understand TensorFlow’s structure and deployment mechanisms
- be able to carry out installation / production environment / architecture tasks and configuration
- be able to assess code quality, perform debugging, monitoring
- be able to implement advanced production like training models, embedding terms, building graphs and logging
NeuralnettfNeural Networks Fundamentals using TensorFlow as Example28 hoursThis course will give you knowledge in neural networks and generally in machine learning algorithm, deep learning (algorithms and applications).

This training is more focus on fundamentals, but will help you choosing the right technology : TensorFlow, Caffe, Teano, DeepDrive, Keras, etc. The examples are made in TensorFlow.
w2vdl4jNLP with Deeplearning4j14 hoursDeeplearning4j is an open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments on distributed GPUs and CPUs.

Word2Vec is a method of computing vector representations of words introduced by a team of researchers at Google led by Tomas Mikolov.

Audience

This course is directed at researchers, engineers and developers seeking to utilize Deeplearning4J to construct Word2Vec models.
dl4jMastering Deeplearning4j21 hoursDeeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments on distributed GPUs and CPUs.

Audience

This course is directed at engineers and developers seeking to utilize Deeplearning4j in their projects.

After this course delegates will be able to:
mldtMachine Learning and Deep Learning21 hoursThis course covers AI (emphasizing Machine Learning and Deep Learning)
aitechArtificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP21 hoursThis course is aimed at developers and data scientists who wish to understand and implement AI within their applications. Special focus is given to Data Analysis, Distributed AI and NLP.
singaMastering Apache SINGA21 hoursSINGA is a general distributed deep learning platform for training big deep learning models over large datasets. It is designed with an intuitive programming model based on the layer abstraction. A variety of popular deep learning models are supported, namely feed-forward models including convolutional neural networks (CNN), energy models like restricted Boltzmann machine (RBM), and recurrent neural networks (RNN). Many built-in layers are provided for users. SINGA architecture is sufficiently flexible to run synchronous, asynchronous and hybrid training frameworks. SINGA also supports different neural net partitioning schemes to parallelize the training of large models, namely partitioning on batch dimension, feature dimension or hybrid partitioning.

Audience

This course is directed at researchers, engineers and developers seeking to utilize Apache SINGA as a deep learning framework.

After completing this course, delegates will:

- understand SINGA’s structure and deployment mechanisms
- be able to carry out installation / production environment / architecture tasks and configuration
- be able to assess code quality, perform debugging, monitoring
- be able to implement advanced production like training models, embedding terms, building graphs and logging
spmllibApache Spark MLlib35 hoursMLlib is Spark’s machine learning (ML) library. Its goal is to make practical machine learning scalable and easy. It consists of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization primitives and higher-level pipeline APIs.

It divides into two packages:

-

spark.mllib contains the original API built on top of RDDs.

-

spark.ml provides higher-level API built on top of DataFrames for constructing ML pipelines.

Audience

This course is directed at engineers and developers seeking to utilize a built in Machine Library for Apache Spark
caffeDeep Learning for Vision with Caffe21 hoursCaffe is a deep learning framework made with expression, speed, and modularity in mind.

This course explores the application of Caffe as a Deep learning framework for image recognition using MNIST as an example

Audience

This course is suitable for Deep Learning researchers and engineers interested in utilizing Caffe as a framework.

After completing this course, delegates will be able to:

- understand Caffe’s structure and deployment mechanisms
- carry out installation / production environment / architecture tasks and configuration
- assess code quality, perform debugging, monitoring
- implement advanced production like training models, implementing layers and logging
datamodelingPattern Recognition35 hoursThis course 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.

The course is interactive and includes plenty of hands-on exercises, instructor feedback, and testing of knowledge and skills acquired.

Audience
Data analysts
PhD students, researchers and practitioners

Upcoming Machine Learning Courses

CourseCourse DateCourse Price [Remote / Classroom]
Understanding Deep Neural Networks - UppsalaMon, 2018-09-03 09:3016520EUR / 18220EUR
Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP - VästeråsWed, 2018-09-05 09:302250EUR / 3050EUR
Introduction to Machine Learning with MATLAB - GöteborgWed, 2018-09-05 09:303900EUR / 4700EUR
Deep Learning for Vision with Caffe - Stockholm, HötorgetWed, 2018-09-12 09:305450EUR / 6250EUR
Introduction to Deep Learning - LinköpingTue, 2018-09-18 09:305450EUR / 6250EUR
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Course Discounts

Course Venue Course Date Course Price [Remote / Classroom]
Haskell Fundamentals Malmö, Stadskärna Mon, 2018-09-10 09:30 2394EUR / 2994EUR
MariaDB Database Administration Stockholm, Hötorget Tue, 2018-09-11 09:30 2673EUR / 3273EUR
Introduction to Recommendation Systems Stockholm, Hötorget Fri, 2018-10-12 09:30 1314EUR / 1714EUR
Statistical Thinking for Decision Makers Malmö, Stadskärna Mon, 2018-11-26 09:30 1503EUR / 1903EUR
Managing Business Rules with PHP Business Rules Malmö, Stadskärna Wed, 2018-12-12 09:30 2430EUR / 3030EUR

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