Certified Machine Learning Specialist (CMLS) Training


Certification in
AI/ML, Big Data, Cloud Computing & IoT

Up to 90% funding for SMEs and professionals (conditions apply)
Special course fee for Tertiary Student/NSF

Register Now More Info
SkillsFuture Course in Singapore

Up to 90% funding for
SMEs and professionals (conditions apply)
Special course fee for Tertiary Student/NSF

Certification in
AI/ML, Big Data, Cloud Computing & IoT

Register Now

Objective of the Machine Learning Training

The Machine Learning training will provide participants with an in-depth knowledge of ML algorithms, techniques and its applications. Real-cases and industry scenarios on implementation techniques for ML will also provide participants with the necessary skills to apply ML methods. 

Machine Learning is moving past the “hype cycle”, with enterprises looking to automate analytics processes in areas like business intelligence and cyber security. In 2020, nearly 8 billion jobs will be created for the Machine Learning experts, generating USD $3 billion in the field of Machine Learning.

Machine Learning Specialist course

What exactly is Machine Learning?

Machine Learning are composed of multiple technologies and techniques, such as deep learning, neural networks, natural-language processing, which will trigger autonomous systems that can be programmed to operate independently of humans, allowing IT to go deeper and do more. In other words, machine learning is an application of artificial intelligence (AI) that allows systems the ability to automatically learn and improve from experience without being programmed. Machine learning focuses on the development of computer programs that can access data and use it as learning material for themselves.

The process of learning begins with observations or data, such as direct experience, examples or instructions This is to understand and look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

“Modern enterprises are drowning in data and starving information”

Course Outline
  • Unit 1: Introduction and basic concepts in Machine learning
  • Unit 2: Introduction to theories used in Machine Learning
  • Unit 3: Supervised learning vs. Unsupervised learning
  • Unit 4: Model selection in Machine learning
  • Unit 5: Role of Weka in Machine Learning
  • Unit 6: Decision Tree and Rule mining using Weka
  • Unit 7: A Brief review on SciPy
  • Unit 8: Random Forest and Markov Decision Process algorithm
  • Unit 9: Google's Go Programming with k-nearest neighbors algorithm
  • Unit 10: C 5.0 based decision tree algorithm

Tools/Software used: RapidMiner, SCIPY, Spark MLlib, GO, TensorFlow

Course Outcome
  • Learn the fundamentals of AI and machine learning and how it could impact your work through several real-life use case
  • Acquire knowledge about Machine Learning techniques/method: Supervised, Unsupervised & Reinforcement Learning through hands-on examples
  • Learn key ML concepts like Principle Component Analysis (PCA), Hyperparameter tuning, Clustering, Classification, Regression, Neural Network etc.
  • Get skilled in popular machine learning algorithms using Python Programming ( SckitLearn, TensorFlow ), Weka, RapidMiner
  • 32 Hours (4 Days)
  • 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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