ref material to use later was very good.

PAUL BEALES- Seagate Technology.

Course: Applied Machine Learning

What did you like the most about the training?: Gave me good practice with using R to build machine learning systems for real situations. I can use this in my work straight away. This was an excellent course. One of the best I have had. Matthew Thomas - British Telecom

Course: Applied Machine Learning

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

Jonathan Blease

Course: Artificial Neural Networks, Machine Learning, Deep Thinking

ref material to use later was very good

PAUL BEALES

Course: Applied Machine Learning

The trainer was so knowledgeable and included areas I was interested in

Mohamed Salama

Course: Data Mining & Machine Learning with R

The topic is very interesting

Wojciech Baranowski

Course: Introduction to Deep Learning

Trainers theoretical knowledge and willingness to solve the problems with the participants after the training

Grzegorz Mianowski

Course: Introduction to Deep Learning

Topic. Very interesting!

Piotr

Course: Introduction to Deep Learning

Exercises after each topic were really helpful, despite there were too complicated at the end. In general, the presented material was very interesting and involving! Exercises with image recognition were great.

Dolby Poland Sp. z o.o.

Course: Introduction to Deep Learning

I think that if training would be done in polish it would allow the trainer to share his knowledge more efficient

Radek

Course: Introduction to Deep Learning

The global overview of deep learning

Bruno Charbonnier

Course: Advanced Deep Learning

The exercises are sufficiently practical and do not need a high knowledge in Python to be done.

Alexandre GIRARD

Course: Advanced Deep Learning

Doing exercises on real examples using Keras. Mihaly totally understood our expectations about this training.

Paul Kassis

Course: Advanced Deep Learning

the subject. it seemed interesting, but I left knowing not much more than before.

Radoslaw Labedzki

Course: Introduction to Deep Learning

Interesting subject

Wojciech Wilk

Course: Introduction to Deep Learning

Knowledgeable trainer

Sridhar Voorakkara

Course: Neural Networks Fundamentals using TensorFlow as Example

I was amazed at the standard of this class - I would say that it was university standard.

David Relihan

Course: Neural Networks Fundamentals using TensorFlow as Example

Very good all round overview.Good background into why Tensorflow operates as it does.

Kieran Conboy

Course: Neural Networks Fundamentals using TensorFlow as Example

I liked the opportunities to ask questions and get more in depth explanations of the theory.

Sharon Ruane

Course: Neural Networks Fundamentals using TensorFlow as Example

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

Sebastiaan Holman

Course: Machine Learning and Deep Learning

The training provided the right foundation that allows us to further to expand on, by showing how theory and practice go hand in hand. It actually got me more interested in the subject than I was before.

Jean-Paul van Tillo

Course: Machine Learning and Deep Learning

Coverage and depth of topics

Anirban Basu

Course: Machine Learning and Deep Learning

The deep knowledge of the trainer about the topic.

Sebastian Görg

Course: Introduction to Deep Learning

Very flexible

Frank Ueltzhöffer

Course: Artificial Neural Networks, Machine Learning and Deep Thinking

flexibility

Werner Philipp

Course: Artificial Neural Networks, Machine Learning and Deep Thinking

Henry's presentation and discussion sessions were very dense with valuable information and insights. He adapted quickly to suit the setting and requests. Good knowledge of the area, and very personable.

Desmond Chung

Course: Deep Learning for Vision - customised

Given outlook of the technology: what technology/process might become more important in the future; see, what the technology can be used for

Commerzbank AG

Course: Neural Networks Fundamentals using TensorFlow as Example

Topic selection. Style of training. Practice orientation

Commerzbank AG

Course: Neural Networks Fundamentals using TensorFlow as Example

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

Sacha Nandlall

Course: Python for Advanced Machine Learning

This is one of the best hands-on with exercises programming courses I have ever taken.

Laura Kahn

Course: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP

This is one of the best quality online trainings I have ever taken in my 13 year career. Keep up the great work!

Course: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP

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

Léo Dubus

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

Translated by

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

The trainer very easily explained difficult and advanced topics.

Leszek K

Course: Artificial Intelligence Overview

Translated by

Trener bardzo zrozumiale wytłumaczył trudne i zaawansowane tematy.

All like it

蒙李

Course: Machine Learning Fundamentals with Python

Translated by

都很喜欢

All like it

蒙李

Course: Machine Learning Fundamentals with Python

Translated by

都很喜欢

Paper explanation

Course: Deep Learning with TensorFlow - bespoke

Translated by

论文讲解

way of conducting and example given by the trainer

ORANGE POLSKA S.A.

Course: Machine Learning and Deep Learning

Translated by

sposób prowadzenia i przykładay podawane przez trenera

Possibility to discuss the proposed issues yourself

ORANGE POLSKA S.A.

Course: Machine Learning and Deep Learning

Translated by

Mozliwosc omowienia samemu zaproponowanych zagadnien

Communication with lecturers

文欣张

Course: Artificial Neural Networks, Machine Learning, Deep Thinking

Translated by

与讲师的交流环节

like it all

lisa xie

Course: Artificial Neural Networks, Machine Learning, Deep Thinking

Translated by

都喜欢

Everything I rated on 6

AB S.A.

Course: Machine Learning - szkolenie dedykowane

Translated by

Wszystko co oceniłem na 6.

a lot of information

Tomasz Szumigalski

Course: Machine Learning - szkolenie dedykowane

Translated by

duża ilość informacji

Problems clearly explained

AB S.A.

Course: Machine Learning - szkolenie dedykowane

Translated by

Jasno wyjaśnione zagadnienia

Great knowledge of the trainer and the ability to understand the meaning of our stupid questions;)

AB S.A.

Course: Machine Learning - szkolenie dedykowane

Translated by

Duża wiedza trenera i zdolność do zrozumienia sensu naszych głupich pytań ;)

Code	Name	Duration	Overview
aiint	Artificial Intelligence Overview	7 hours	This 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.
mlintro	Introduction to Machine Learning	7 hours	This training course is for people that would like to apply basic Machine Learning techniques in practical applications. Audience Data scientists and statisticians that have some familiarity with machine learning and know how to program R. The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give a practical introduction to machine learning to participants interested in applying the methods at work Sector specific examples are used to make the training relevant to the audience.
appliedml	Applied Machine Learning	14 hours	This training course is for people that would like to apply Machine Learning in practical applications. Audience This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work. Sector specific examples are used to make the training relevant to the audience.
mlfunpython	Machine Learning Fundamentals with Python	14 hours	The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
MLFWR1	Machine Learning Fundamentals with R	14 hours	The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the 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.
annmldt	Artificial Neural Networks, Machine Learning, Deep Thinking	21 hours	Artificial 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.
bspkaml	Machine Learning	21 hours	This course will be a combination of theory and practical work with specific examples used throughout the event.
mlrobot1	Machine Learning for Robotics	21 hours	This course introduces machine learning methods in robotics applications. It is a broad overview of existing methods, motivations and main ideas in the context of pattern recognition. After a short theoretical background, participants will perform simple exercise using open source (usually R) or any other popular software.
deeplearning1	Introduction to Deep Learning	21 hours	This 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.
dladv	Advanced Deep Learning	28 hours	Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks.
mlfsas	Machine Learning Fundamentals with Scala and Apache Spark	14 hours	The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Scala programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results. Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
dmmlr	Data Mining & Machine Learning with R	14 hours	R 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.
tf101	Deep Learning with TensorFlow	21 hours	TensorFlow is a 2nd Generation API of Google's open source software library for Deep Learning. The system is designed to facilitate research in machine learning, and to make it quick and easy to transition from research prototype to production system. Audience This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects After completing this course, delegates will: understand TensorFlow’s structure and deployment mechanisms be able to carry out installation / production environment / architecture tasks and configuration be able to assess code quality, perform debugging, monitoring be able to implement advanced production like training models, building graphs and logging
predio	Machine Learning with PredictionIO	21 hours	PredictionIO 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.
aiintrozero	From Zero to AI	35 hours	This course is created for people who have no previous experience in probability and statistics.
dlv	Deep Learning for Vision	21 hours	Audience 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.
aiauto	Artificial Intelligence in Automotive	14 hours	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.
Neuralnettf	Neural Networks Fundamentals using TensorFlow as Example	28 hours	This 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.
systemml	Apache SystemML for Machine Learning	14 hours	Apache 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.
tfir	TensorFlow for Image Recognition	28 hours	This 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
dl4j	Mastering Deeplearning4j	21 hours	Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Spark, DL4J is designed to be used in business environments on distributed GPUs and CPUs. Audience This course is directed at engineers and developers seeking to utilize Deeplearning4j in their projects. After this course delegates will be able to:
singa	Mastering Apache SINGA	21 hours	SINGA is a general distributed deep learning platform for training big deep learning models over large datasets. It is designed with an intuitive programming model based on the layer abstraction. A variety of popular deep learning models are supported, namely feed-forward models including convolutional neural networks (CNN), energy models like restricted Boltzmann machine (RBM), and recurrent neural networks (RNN). Many built-in layers are provided for users. SINGA architecture is sufficiently flexible to run synchronous, asynchronous and hybrid training frameworks. SINGA also supports different neural net partitioning schemes to parallelize the training of large models, namely partitioning on batch dimension, feature dimension or hybrid partitioning. Audience This course is directed at researchers, engineers and developers seeking to utilize Apache SINGA as a deep learning framework. After completing this course, delegates will: understand SINGA’s structure and deployment mechanisms be able to carry out installation / production environment / architecture tasks and configuration be able to assess code quality, perform debugging, monitoring be able to implement advanced production like training models, embedding terms, building graphs and logging
mldt	Machine Learning and Deep Learning	21 hours	This course covers AI (emphasizing Machine Learning and Deep Learning)
aitech	Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP	21 hours	This 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.
spmllib	Apache Spark MLlib	35 hours	MLlib 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
caffe	Deep Learning for Vision with Caffe	21 hours	Caffe is a deep learning framework made with expression, speed, and modularity in mind. This course explores the application of Caffe as a Deep learning framework for image recognition using MNIST as an example Audience This course is suitable for Deep Learning researchers and engineers interested in utilizing Caffe as a framework. After completing this course, delegates will be able to: understand Caffe’s structure and deployment mechanisms carry out installation / production environment / architecture tasks and configuration assess code quality, perform debugging, monitoring implement advanced production like training models, implementing layers and logging
datamodeling	Pattern Recognition	35 hours	This 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
patternmatching	Pattern Matching	14 hours	Pattern Matching is a technique used to locate specified patterns within an image. It can be used to determine the existence of specified characteristics within a captured image, for example the expected label on a defective product in a factory line or the specified dimensions of a component. It is different from "Pattern Recognition" (which recognizes general patterns based on larger collections of related samples) in that it specifically dictates what we are looking for, then tells us whether the expected pattern exists or not. Audience Engineers and developers seeking to develop machine vision applications Manufacturing engineers, technicians and managers Format of the course This course introduces the approaches, technologies and algorithms used in the field of pattern matching as it applies to Machine Vision.
Torch	Torch: Getting started with Machine and Deep Learning	21 hours	Torch is an open source machine learning library and a scientific computing framework based on the Lua programming language. It provides a development environment for numerics, machine learning, and computer vision, with a particular emphasis on deep learning and convolutional nets. It is one of the fastest and most flexible frameworks for Machine and Deep Learning and is used by companies such as Facebook, Google, Twitter, NVIDIA, AMD, Intel, and many others. In this course we cover the principles of Torch, its unique features, and how it can be applied in real-world applications. We step through numerous hands-on exercises all throughout, demonstrating and practicing the concepts learned. By the end of the course, participants will have a thorough understanding of Torch's underlying features and capabilities as well as its role and contribution within the AI space compared to other frameworks and libraries. Participants will have also received the necessary practice to implement Torch in their own projects. Audience Software developers and programmers wishing to enable Machine and Deep Learning within their applications Format of the course Overview of Machine and Deep Learning In-class coding and integration exercises Test questions sprinkled along the way to check understanding
OpenNN	OpenNN: Implementing Neural Networks	14 hours	OpenNN is an open-source class library written in C++ which implements neural networks, for use in machine learning. In this course we go over the principles of neural networks and use OpenNN to implement a sample application. Audience Software developers and programmers wishing to create Deep Learning applications. Format of the course Lecture and discussion coupled with hands-on exercises.

Course	Course Date	Course Price [Remote / Classroom]
Machine Learning for Banking (with R) - Classroom - Mansarovar, Jaipur	Mon, 2018-08-06 09:30	202922INR / 241922INR
Machine Learning for Finance (with Python) - Classroom - Meerut Road, Ghaziabad	Mon, 2018-08-06 09:30	152191INR / 178191INR
Machine Learning with PredictionIO - Classroom - Indirapuram, Ghaziabad	Mon, 2018-08-06 09:30	152191INR / 178191INR
Text Summarization with Python - Classroom - Bhavdhan, Pune	Mon, 2018-08-06 09:30	101461INR / 117861INR
Applied Machine Learning - Classroom - Pallav Puram, Meerut	Tue, 2018-08-07 09:30	101461INR / 122461INR

Course	Venue	Course Date	Course Price [Remote / Classroom]
Complete Web Testing Environment with Selenium WebDriver and JMeter	Classroom - Hosur Rd, Kormangla, Bangalore	Mon, 2018-07-30 10:30	N/A / 249620INR
Programming with Big Data in R	Classroom - Pallav Puram, Meerut	Mon, 2018-08-20 09:30	136972INR / 162972INR
Advanced Lua	Classroom - Colombo	Wed, 2018-08-22 09:30	91315INR / 120315INR
Automated Monitoring with Zabbix	Classroom - UB City, Bangalore	Sat, 2018-08-25 09:30	N/A / 145652INR
Automated Monitoring with Zabbix	Classroom - Hosur Rd, Kormangla, Bangalore	Sat, 2018-08-25 09:30	N/A / 124652INR
Automated Monitoring with Zabbix	Classroom - Indiranagar, Bangalore	Sat, 2018-09-08 09:30	N/A / 124652INR
Automated Monitoring with Zabbix	Classroom - HSR Layout, Bangalore	Sat, 2018-09-15 09:30	N/A / 127652INR

Machine Learning Training Courses

Client Testimonials

Subcategories

Course Outlines

Audience

Audience

Audience

Audience

Audience

Audience

After completing this course, delegates will be able to:

Upcoming Courses

Other regions

Other countries

Consulting

Course Discounts

Some of our clients

Course Catalogue

Machine Learning Training Courses

Client Testimonials

Subcategories

Course Outlines

Audience

Audience

Audience

Audience

Audience

Audience

After completing this course, delegates will be able to:

Upcoming Courses

Other regions

Other countries

Consulting

Course Discounts

Course Discounts Newsletter

Some of our clients

Course Catalogue