Online or onsite, instructor-led live Machine Learning (ML) training courses demonstrate through hands-on practice how to apply machine learning techniques and tools for solving real-world problems in various industries. NobleProg ML courses cover different programming languages and frameworks, including Python, R language and Matlab. Machine Learning courses are offered for a number of industry applications, including Finance, Banking and Insurance and cover the fundamentals of Machine Learning as well as more advanced approaches such as Deep Learning.
Machine Learning training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Machine Learning training can be carried out locally on customer premises in India or in NobleProg corporate training centers in India.
NobleProg -- Your Local Training Provider
Testimonials
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
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 high knowledge in Python to be done.
Alexandre GIRARD
Course: Advanced Deep Learning
Doing exercises on real examples using Eras. Italy totally understood our expectations about this training.
Paul Kassis
Course: Advanced Deep Learning
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
I generally enjoyed the 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
I really enjoyed the 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 updated approach or CPI (tensor flow, era, learn) to do machine learning.
Paul Lee
Course: TensorFlow for Image Recognition
Very flexible.
Frank Ueltzhöffer
Course: Artificial Neural Networks, Machine Learning and Deep Thinking
I generally enjoyed the flexibility.
Werner Philipp
Course: Artificial Neural Networks, Machine Learning and Deep Thinking
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
I was benefit from 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
I genuinely liked excercises
L M ERICSSON LIMITED
Course: Machine Learning
I liked the lab exercises.
Marcell Lorant - L M ERICSSON LIMITED
Course: Machine Learning
The Jupyter notebook form, in which the training material is available
L M ERICSSON LIMITED
Course: Machine Learning
There were many exercises and interesting topics.
L M ERICSSON LIMITED
Course: Machine Learning
Some great lab exercises analyzed and explained by the trainer in depth (e.g. covariants in linear regression, matching the real function)
L M ERICSSON LIMITED
Course: Machine Learning
It's just great that all material including the exercises is on the same page and then it gets updated on the fly. The solution is revealed at the end. Cool! Also, I do appreciate that Krzysztof took extra effort to understand our problems and suggested us possible techniques.
Attila Nagy - L M ERICSSON LIMITED
Course: Machine Learning
I was benefit from the passion to teach and focusing on making thing sensible.
Zaher Sharifi - GOSI
Course: Advanced Deep Learning
About face area.
中移物联网
Course: Deep Learning for NLP (Natural Language Processing)
The informal exchanges we had during the lectures really helped me deepen my understanding of the subject
Explore
Course: Deep Reinforcement Learning with Python
It is showing many methods with pre prepared scripts- very nicely prepared materials & easy to traceback
Kamila Begej - GE Medical Systems Polska Sp. Zoo
Course: Machine Learning – Data science
I like that training was focused on examples and coding. I thought that it is impossible to pack so much content into three days of training, but I was wrong. Training covered many topics and everything was done in a very detailed manner (especially tuning of model's parameters - I didn't expected that there will be a time for this and I was gratly surprised).
Bartosz Rosiek - GE Medical Systems Polska Sp. Zoo
Course: Machine Learning – Data science
I like that it focuses more on the how-to of the different text summarization methods
Course: Text Summarization with Python
lots of information, all questions ansered, interesting examples
A1 Telekom Austria AG
Course: Deep Learning for Telecom (with Python)
The trainer was a professional in the subject field and related theory with application excellently
Fahad Malalla - Tatweer Petroleum
Course: Applied AI from Scratch in Python
I started with close to zero knowledge, and by the end I was able to build and train my own networks.
Huawei Technologies Duesseldorf GmbH
Course: TensorFlow for Image Recognition
Ewa has a passion for the subject and a huge wealth of knowledge. She impressed all of us with her knowledge and kept us all focused through the day.
Rock Solid Knowledge Ltd
Course: Machine Learning – Data science
Even with having to miss a day due to customer meetings, I feel I have a much clearer understanding of the processes and techniques used in Machine Learning and when I would use one approach over another. Our challenge now is to practice what we have learned and start to apply it to our problem domain
Richard Blewett - Rock Solid Knowledge Ltd
Course: Machine Learning – Data science
So much breadth and topics covered. I felt it was a huge subject to try and cover in 3 days - the trainer did what they could to cover everything almost exactly on time!
Rock Solid Knowledge Ltd
Course: Machine Learning – Data science
Adjusting to our needs
Sumitomo Mitsui Finance and Leasing Company, Limited
Course: Kubeflow
Organization, adhering to the proposed agenda, the trainer's vast knowledge in this subject
Ali Kattan - TWPI
Course: Natural Language Processing with TensorFlow
the way he present everything with examples and training was so useful
Ibrahim Mohammedameen - TWPI
Course: Natural Language Processing with TensorFlow
Very knowledgeable
Usama Adam - TWPI
Course: Natural Language Processing with TensorFlow
The excersise where we should train a network to approximate a function
Nercia Utbildning AB
Course: Deep Learning with TensorFlow 2.0
Tomasz really know the information well and the course was well paced.
Raju Krishnamurthy - Google
Course: TensorFlow Extended (TFX)
having access to the notebooks to work through
Premier Partnership
Course: Python for Advanced Machine Learning
I like that it focuses more on the how-to of the different text summarization methods
Course: Text Summarization with Python
ML (Machine Learning) Subcategories in India
ML (Machine Learning) Course Outlines in India
Course Name
Duration
Overview
Course Name
Duration
Overview
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.
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.
21 hours
In this instructor-led, live training in India, 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.
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.
28 hours
The aim of this course is to provide general 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.
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.
28 hours
This is a 4 day course introducing AI and it's application using the Python programming language. There is an option to have an additional day to undertake an AI project on completion of this course.
21 hours
Deep Reinforcement Learning refers to the ability of an "artificial agent" to learn by trial-and-error and rewards-and-punishments. An artificial agent aims to emulate a human's ability to obtain and construct knowledge on its own, directly from raw inputs such as vision. To realize reinforcement learning, deep learning and neural networks are used. Reinforcement learning is different from machine learning and does not rely on supervised and unsupervised learning approaches.
In this instructor-led, live training, participants will learn the fundamentals of Deep Reinforcement Learning as they step through the creation of a Deep Learning Agent.
By the end of this training, participants will be able to:
- Understand the key concepts behind Deep Reinforcement Learning and be able to distinguish it from Machine Learning
- Apply advanced Reinforcement Learning algorithms to solve real-world problems
- Build a Deep Learning Agent
Audience
- Developers
- Data Scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn the fundamentals of Deep Reinforcement Learning as they step through the creation of a Deep Learning Agent.
By the end of this training, participants will be able to:
- Understand the key concepts behind Deep Reinforcement Learning and be able to distinguish it from Machine Learning
- Apply advanced Reinforcement Learning algorithms to solve real-world problems
- Build a Deep Learning Agent
Audience
- Developers
- Data Scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
28 hours
In this instructor-led, live training in India, 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
21 hours
This course will be a combination of theory and practical work with specific examples used throughout the event.
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.
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.
21 hours
MATLAB is a numerical computing environment and programming language developed by MathWorks.
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.
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.
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.
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.
Audience
This course is directed at developers and data scientists who want to create predictive engines for any machine learning task.
35 hours
This course is created for people who have no previous experience in probability and statistics.
21 hours
Course is dedicated for those who would like to know an alternative program to the commercial MATLAB package. The three-day training provides comprehensive information on moving around the environment and performing the OCTAVE package for data analysis and engineering calculations. The training recipients are beginners but also those who know the program and would like to systematize their knowledge and improve their skills. Knowledge of other programming languages is not required, but it will greatly facilitate the learners' acquisition of knowledge. The course will show you how to use the program in many practical examples.
21 hours
This training course is for people that would like to apply Machine Learning in practical applications for their team. The training will not dive into technicalities and revolve around basic concepts and business/operational applications of the same.
Target Audience
- Investors and AI entrepreneurs
- Managers and Engineers whose company is venturing into AI space
- Business Analysts & Investors
Target Audience
- Investors and AI entrepreneurs
- Managers and Engineers whose company is venturing into AI space
- Business Analysts & Investors
7 hours
Snorkel 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
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
14 hours
Encog 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
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
14 hours
Encog 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
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
28 hours
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. 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
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
14 hours
The 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
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
28 hours
Cortana Intelligence Suite is a bundle of integrated products and services on the Microsoft Azure Cloud that enable entities to transform data into intelligent actions.
In this instructor-led, live training, participants will learn how to use the components that are part of the Cortana Intelligence Suite to build data-driven intelligent applications.
By the end of this training, participants will be able to:
- Learn how to use Cortana Intelligence Suite tools
- Acquire the latest knowledge of data management and analytics
- Use Cortana components to turn data into intelligent action
- Use Cortana to build applications from scratch and launch it on the cloud
Audience
- Data scientists
- Programmers
- Developers
- Managers
- Architects
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to use the components that are part of the Cortana Intelligence Suite to build data-driven intelligent applications.
By the end of this training, participants will be able to:
- Learn how to use Cortana Intelligence Suite tools
- Acquire the latest knowledge of data management and analytics
- Use Cortana components to turn data into intelligent action
- Use Cortana to build applications from scratch and launch it on the cloud
Audience
- Data scientists
- Programmers
- Developers
- Managers
- Architects
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 hours
AI is a collection of technologies for building intelligent systems capable of understanding data and the activities surrounding the data to make "intelligent decisions". For Telecom providers, building applications and services that make use of AI could open the door for improved operations and servicing in areas such as maintenance and network optimization.
In this course we examine the various technologies that make up AI and the skill sets required to put them to use. Throughout the course, we examine AI's specific applications within the Telecom industry.
Audience
- Network engineers
- Network operations personnel
- Telecom technical managers
Format of the course
- Part lecture, part discussion, hands-on exercises
In this course we examine the various technologies that make up AI and the skill sets required to put them to use. Throughout the course, we examine AI's specific applications within the Telecom industry.
Audience
- Network engineers
- Network operations personnel
- Telecom technical managers
Format of the course
- Part lecture, part discussion, hands-on exercises
14 hours
This classroom based training session will explore machine learning techniques, with computer based examples and case study solving exercises using a relevant programme languauge
21 hours
This classroom based training session will explore machine learning tools with (suggested) Python. Delegates will have computer based examples and case study exercises to undertake.
14 hours
Linear 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.
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.
Last Updated:
Upcoming Machine Learning (ML) Courses in India
Machine Learning and Big Data
15-02-2022 09:30
7 hours
Kubeflow
21-02-2022 09:30
35 hours
Artificial Intelligence for Mechatronics
07-03-2022 09:30
21 hours
Applied AI from Scratch in Python
08-03-2022 09:30
28 hours
Feature Engineering for Machine Learning
17-03-2022 09:30
14 hours
Other regions in India
Other countries
Consulting in India
Online ML (Machine Learning) courses, Weekend Machine Learning courses, Evening Machine Learning (ML) training, ML (Machine Learning) boot camp, ML (Machine Learning) instructor-led, Weekend ML (Machine Learning) training, Evening ML (Machine Learning) courses, Machine Learning coaching, Machine Learning (ML) instructor, Machine Learning (ML) trainer, Machine Learning (ML) training courses, Machine Learning classes, Machine Learning on-site, Machine Learning private courses, Machine Learning (ML) one on one training
.jpg "procter & gamble (p&g)")
_ireland.gif "environmental protection agency (epa) ireland")
.png "aeronautical radio, incorporated (arinc)"){kind=small}
