Data Mining and Machine Learning Fundamentals for Beginners
About This Course
Upgrade your data skills with our cutting-edge WSQ Data Mining and Machine This course introduces the fundamentals of Data Mining and Machine Learning, equipping beginners with the necessary skills to apply these principles in assessing business insights and integrating information from datasets for informed decision-making. Participants will gain a solid understanding of the data mining process, the impact of machine learning, and how to leverage these for accessing business insights. Through hands-on learning, the course covers data preparation techniques including import/export, filtering, cleaning, and joining data to ensure quality inputs for analysis.
Delving deeper, the course explores advanced techniques such as predictive data modeling to identify trends, machine learning classification to uncover insights, and clustering techniques for pattern discovery. Participants will also learn about dimension reduction to develop prototype algorithms and construct association rules for pattern identification across multiple datasets. By the end of the course, learners will have the ability to apply these techniques effectively to solve real-world business problems, paving the way for innovative solutions and strategic decisions.
What You'll Learn
- apply data mining and machine learning principles to assess business insights
- integrate information from datasets
- apply predictive data modelling techniques to identify underlying trends in data
apply machine learning classification techniques to gain new insights from data
- apply clustering techniques to discover data pattern and make decision
- develop prototype algorithms with dimension reduction techniques
- construct association rules to Identify patterns across multiple data sets to derive insights
Topics Covered
Topic 1 Overview of Data Mining and Machine Learning
Data Mining Process
Overview of Machine Learning
Impact of Data Mining and ML to Access Business Insights
Topic 2: Data Preparation
Import/Export Data
Filter Data
Join Data
Clean Data
Topic 3: Regression
What is Regression
Linear Regression
Underfitting and Overfitting
Regularization Techniques
Topic 4: Classification
What is Classification
Classification Algorithms
K-Fold Cross Validation
Model Evaluation Metrics
Confusion Matrix
Topic 5: Clustering
What is Clustering
K-Means Clustering
Silhouette Analysis
Hierarchical Clustering
Topic 6: Dimension Reduction
Principal Component Analysis (PCA)
Feature Ranking
Topic 7: Association Analysis
Association Rules
Constructing Rules
Entry Requirements
Knowledge and Skills
• Able to operate computer functions with minimum Computer Literacy Level 2 based on ICAS Computer Skills Assessment Framework
• Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)
Attitude
• Positive Learning Attitude
• Enthusiastic Learner
Experience
• Minimum of 1 year of working experience.
• Minimum 18 years old