Certified Deep Learning Specialist (CDLS)
Course Description
The Certified Deep Learning Specialist (CDLS) course is aimed at data scientists to build complex models with human-like intelligence using Machine Learning and Deep Learning techniques. It is designed to provide aspiring data scientists with an overview of concepts, techniques, and algorithms of machine learning using the Python programming language. End-to-end Machine Learning techniques that include various sampling methods will be addressed, ensemble methods such as the Random Forest and variants of Artificial Neural Network (ANN) such as the Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network will be discussed.
Course Objective
The objective of the Certified Deep Learning Specialist (CDLS) course is to enable participants to leverage intuitive approach to build complex models with humans-like intelligence that solves real-world problems using Machine Learning and Deep Learning techniques. In this training, participants will gain a solid understanding and practical skillset of building intelligent computing models using Python Jupyter Notebook platform using packages like Scikit-Learn and TensorFlow.
Skills Acquired
Participants will acquire the skills to develop an end-to-end machine learning model using the Python programming language and model selection method using various open-source Machine Learning tools such as Jupyter notebook, scikit-learn and TensorFlow. Participants will acquire a deep understanding on the theories of artificial neural network, deep learning and examples such as restricted Boltzmann machine and Deep Belief Network (Deep Belief Net).
Course Duration
32 hrs (4 days) inclusive of training and exam
Pre-Requisites
Participants are recommended to have preferably 1 year of experience in software development, business domain or data / business analysis. However, if you do not have any experiences, you can still consider taking up the course and we will advise / assist you accordingly.
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Why Deep Learning Course ?
Many international firms are engaged in the development of technologies categorized under the field of artificial intelligence (AI). The systems equipped with AI do not require constant human intervention to carry out the designated process. Such an intelligent computing system has the ability to learn things on its own according to the situation. Technologies such as deep learning, intelligent robots and neuro-linguistic programming under AI have been aiding in the enhancement of the existing computing systems to produce high-value prediction.
The global AI market is expected to reach USD 35,870.0 million by 2025 from its direct revenue sources, growing at a CAGR of 57.2% from 2017 to 2025. And the Deep Learning market, in particular, is expected to be worth USD 1772.9 Million by 2022, growing at a CAGR of 65.3% between 2016 and 2022. The Asia Pacific regional market is expected to be the fastest-growing market for deep learning, owing to the improvements in information storage capacity, high computing power, and parallel processing.
Course Outline
- Unit 1 : Machine Learning – An overview
- Unit 2 : Model Selection methods in Machine Learning
- Unit 3 : Libraries for scientific computation and data analysis
- Unit 4 : Artificial Neural Network
- Unit 5 : Theories under Deep Learning
- Unit 6 : Types of Neural Network
- Unit 7 : Introduction to TensorFlow
- Unit 8 : Restricted Boltzman Machine and DeepBeliefNet
Course Outcome
- Acquire knowledge of the different model selection methods in Machine Learning
- Acquire knowledge about different types of Deep Neural Networks (MLP, CNN, RNN, LSTM)
- Design and build Machine Learning models in Python Jupyter notebooks using Scikit Learn framework
- Deep understanding of the overview of Machine Learning techniques
- Design and build an end to end model using TensorFlow in Python Jupyter notebook
- Get hands-on experience in Jupyter Notebook, TensorFlow, scikit-learn
Please feel free to contact us if you require further information on our GICT Training and Certification courses.