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 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

• 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

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