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Machine Learning in Data Science Project using Python

Duration : 2 Days

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.


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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