Machine Learning in Data Science Project using Python
Thoroughly updated using the latest Python open-source libraries for data analysis and machine learning, this course offers the practical knowledge and techniques you need to perform predictive analytic project using machine learning.
Duration: 2 days
Skill, or experience, in the following is required for this class:
• Fundamental Python for Data science (Data Analysis) Task
Data Science and Machine Learning Concept
• Data science Overview
• Machine learning concept
• Learning types and Popular Algorithm
• Machine learning approach: sample case
Data Wrangling (Data Preparation)
• Data cleaning
• Handling missing data
• Detecting outlier data
• Data restructuring
• Data frame indexing, Converting data type
• Change categorical data using encoding
Feature Engineering
• Dataset, feature, and class label (target)
• Split dataset into training data and testing data
• Selecting and Scaling Features
Classification
• Classification model approach, Logistic regression
• Evaluating classification model
• Feature analysis for classification model
Other Algorithm and Performance Tuning
• Decision tree and random forest algorithm
• Comparing Performance Result
• Confusion Matrix
Problem in Classification Model
• Imbalance data problem
• Scaling dataset for better classification result
Regression
• Feature analysis: correlation analysis
• Regression model approach, Simple linear regression
• Performance metric for regression model
Performance Tuning for Regression Model
• Polynomial Feature
• Decision tree and random forest algorithm
• Comparing Performance Result
Unsupervised Model: Clustering
• Clustering model approach
• K-means clustering
• Evaluating clustering model
• Choosing the best k-value
• Visualizing clustering model