Machine Learning Developer (SF)
Training Provider: NTUC LEARNINGHUB PTE. LTD.
Course Reference: TGS-2026063080
S$1,750
Original: S$3,500
Save S$1,750
About This Course
This 5-day intensive bootcamp equips learners with practical skills in machine learning using Python. The course begins with ML fundamentals and walks through linear and logistic regression, decision trees, and ensemble methods. It also includes NLP with embeddings, Transformers, and BERT, along with model deployment using Flask APIs. Learners will build pipelines, evaluate models, and complete a capstone project to solidify real-world readiness.
What You'll Learn
Learning Outcomes:
1. Appraise analytical platforms and data models by evaluating their functional capabilities against defined environmental requirements to ensure architectural alignment.
2. Uncover underlying relationships among complex variables by applying advanced statistical and computational modelling techniques to identify significant correlations and causal patterns.
3. Construct advanced predictive models by applying mathematical theories and complex statistical techniques to develop linear, multiple, and logistic regression solutions for data analysis.
4. Develop adaptive algorithms by analysing the root causes of system performance issues and synthesising core algorithmic elements to enable automated learning and improvement of outcomes.
5. Optimise data-driven decision making by developing mathematical models that isolate trends, selecting from various statistical approaches and algorithms based on a critical evaluation of their technical features and limitations.
6. Develop reinforcement learning models and iteratively refine their algorithmic parameters to optimise system performance and ensure the achievement of intended operational outcomes.
7. Develop comprehensive testing procedures to evaluate the performance and accuracy of data models by applying standardised statistical validation techniques.
1. Appraise analytical platforms and data models by evaluating their functional capabilities against defined environmental requirements to ensure architectural alignment.
2. Uncover underlying relationships among complex variables by applying advanced statistical and computational modelling techniques to identify significant correlations and causal patterns.
3. Construct advanced predictive models by applying mathematical theories and complex statistical techniques to develop linear, multiple, and logistic regression solutions for data analysis.
4. Develop adaptive algorithms by analysing the root causes of system performance issues and synthesising core algorithmic elements to enable automated learning and improvement of outcomes.
5. Optimise data-driven decision making by developing mathematical models that isolate trends, selecting from various statistical approaches and algorithms based on a critical evaluation of their technical features and limitations.
6. Develop reinforcement learning models and iteratively refine their algorithmic parameters to optimise system performance and ensure the achievement of intended operational outcomes.
7. Develop comprehensive testing procedures to evaluate the performance and accuracy of data models by applying standardised statistical validation techniques.
Entry Requirements
• The candidate should hold a diploma and have at least 2 years of experience working as a developer/programmer.
• Candidates who do not meet the above requirements will be assessed on case to case basis.
Course Details
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