NUS Certificate in Digital Solutions Development – Machine Learning Applications

Key Takeaways

• Understand Python and Data Manipulation
  • Foundational concepts of Python programming language
  • Data manipulation with Numpy and Pandas
  • Data visualisation with Matplotlib and Seaborn
• Understand Feature Engineering
  • Data processing using binning, one-hot encoding and features scaling
  • Dimension Reduction with Principal Component Analysis (PCA)
  • Select the best features of a dataset with Pearson Correlation
  • Generate new features from a dataset using PCA
• Understand and build Regression and Classification
  • Build Linear and Logistic Regression models
  • Build classification models with Decision Trees
  • Build Time-Series models for forecasting
• Understand and perform Clustering
  • Perform clustering with K-Means, Hierarchical and DBSCAN
  • Understand the mechanics behind various clustering algorithms
  • Evaluate the effectiveness of a clustering result
• Understand Text Processing
  • Text Featurisation via Bag of Words, TF-IDF and Word Embeddings
  • Understand the mechanics behind Bag of Words and TF-IDF
  • Feature Vectors comparison with Cosine Similarity
• Understand Neural Networks
  • Data Regression and Classification using Neural Networks
  • Image Classification with CNN
  • Image Processing with Pillow
• Understand and build Intelligent Systems
  • Build Recommendation systems using Collaborative Filtering
  • Build Spam Filtering Systems and Sentiment Analysis systems using Naive Bayes
  • Understand the mechanics behind Collaborative Filtering and Naive Bayes
• Understand Generative AI
  • Introduction to various Generative Models (e.g. GANs) and their architectures
  • Understand their training processes
  • Applications for Generative AI
• Understand Deployment
  • Save trained models for deployment
  • Publish services as REST API with Python

Who Should Attend

This course is targeted at individuals who are interested in developing machine learning application for enterprise environments

• Preferably have some understanding of how IT solutions can be used in the real world
• Completed the Graduate Certificate in Digital Solutions Development - Web Applications

What Will Be Covered

• Understand Python and Data Manipulation
  • Foundational concepts of Python programming language
  • Data manipulation with Numpy and Pandas
  • Data visualisation with Matplotlib and Seaborn
• Understand Feature Engineering
  • Data processing using binning, one-hot encoding and features scaling
  • Dimension Reduction with Principal Component Analysis (PCA)
  • Select the best features of a dataset with Pearson Correlation
  • Generate new features from a dataset using PCA
• Understand and build Regression and Classification
  • Build Linear and Logistic Regression models
  • Build classification models with Decision Trees
  • Build Time-Series models for forecasting
• Understand and perform Clustering
  • Perform clustering with K-Means, Hierarchical and DBSCAN
  • Understand the mechanics behind various clustering algorithms
  • Evaluate the effectiveness of a clustering result
• Understand Text Processing
  • Text Featurisation via Bag of Words, TF-IDF and Word Embeddings
  • Understand the mechanics behind Bag of Words and TF-IDF
  • Feature Vectors comparison with Cosine Similarity
• Understand Neural Networks
  • Data Regression and Classification using Neural Networks
  • Image Classification with CNN
  • Image Processing with Pillow
• Understand and build Intelligent Systems
  • Build Recommendation systems using Collaborative Filtering
  • Build Spam Filtering Systems and Sentiment Analysis systems using Naive Bayes
  • Understand the mechanics behind Collaborative Filtering and Naive Bayes
• Understand Generative AI
  • Introduction to various Generative Models (e.g. GANs) and their architectures
  • Understand their training processes
  • Applications for Generative AI
• Understand Deployment
  • Save trained models for deployment
  • Publish services as REST API with Python

Format
Lectures and workshops

Certificate of Completion
The NUS-ISS Certificate of Completion will be issued to participants who have attended at least 75% of the course.

Assessment

• Broad Schedule of Assessment: During and end of course
• Passing Criteria: Generally achieve above 50% marks for workshop, course assignments and written examination.
• Assessment Method: Workshop, course assignments and written examination

Fees

Preparing for Your Course

ISS and Learner’s Commitment and Responsibilities

Find out more.

WIFI Access

WIFI access will be made available. User ID and password will be provided in the participant’s kit.

Venue

NUS-ISS
25 Heng Mui Keng Terrace
Singapore 119615

In the event of a change of venue, participants are advised to refer to the acceptance email sent one week prior to the commencement date.

Course Confirmation

All classes are subject to confirmation and ISS will send an acceptance email to participants one week prior to the commencement date. Confirmed registrants are to attend and complete all lectures, class exercises, workshops and assessments (where applicable). Additionally, all responses to feedbacks and surveys conducted by ISS and its partners must be submitted. All training and assessments will be delivered as described in the course webpage.

General Enquiry

Please feel free to write to ask-iss@nus.edu.sg if you have any enquiry or feedback.