Enter the dynamic realm of machine learning with our specialized Python training module using Scikit-Learn at Tertiary Courses. From understanding the core differences between supervised and unsupervised learning to hands-on engagements with classification model analysis, our curriculum promises in-depth exploration. F1 Score and AUC metrics ensure that participants gain key insights into the accuracy and performance of their ML models.

The course further delves into critical machine learning facets like multivariate linear regression, supplemented by techniques such as Ridge and Lasso regularization to counter overfitting effectively. Participants will also be introduced to Silhouette Analysis and Dendrogram methods, empowering them with clustering skills. Concluding with dimension reduction using PCA, our program ensures that attendees walk away with a well-rounded, practical understanding of Python-powered machine learning using Scikit-Learn.

Certificate

All participants will receive a Certificate of Completion from Tertiary Courses after achieved at least 75% attendance.

Funding and Grant

HRD Corp Claimable Course for Employers Registered with HRD Corp

Course Code: M268

Topic 1 Overview of Machine Learning and Scikit Learn

• Introduction to Machine Learning

• Supervised vs Unsupervised Learnings

• Machine Learning Applications and Case Studies

• What is Scikit Learn

• Installing Scikit-Learn

Topic 2 Classification

• What is Classification

• Classification Algorithms

• Classification Workflow

• Confusion Matrix

• Binary Classification Metrics

• ROC and AUC

Topic 3 Regression

• What is Regression?

• Regression Algorithms

• Regression Workflow

• Regression Metrics

• Overfitting and Regularizations

Topic 4 Clustering

• What is Clustering

• K-Means Clustering

• Silhouette Analysis

• Dendrogram and Hierarchical Clustering

Topic 5 Principal Component Analysis

• Curse of Dimensionality Issue

• What is Principal Component Analysis (PCA)

• Feature Reduction with PCA