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Python, Machine Learning, Flask and Dash

OBJECTIVE

Obtained data, performed exploratory data analysis, built a machine learning model that can accurately predict your target variable? What next? Creating a Dashboard on the web endpoint can be a compelling way of communicating a Data Science project’s results. The goal here is to provide a Machine Learning/Deep Learning algorithm as a service that can be used by your team or other teams at the workplace to derive key business solutions.This Training shows you how to deploy your machine learning model into production and how to make your models available for end-users or systems, by first creating a Flask application (Flask API).

OUTLINE

1. Basic Python Programming and Development

• Introduction to Python
• Instaling Python Package
• Moduls and Functions in Python
• Strings in Python
• Sequences and Slicing in Python
• Object Oriented

2. Intro to Machine Learning

• Applications of Machine Learning
• Prepar and Instalation
• Preprocessing Data
  • Preprocess Case
  • Required Libraries
  • Load Data
  • Imputation
  • Categorical Column Map
  • Split Test Train
  • Preprocess Template
  • Preprocess Template for Classification
• Regression and Classification Intuition
• KNN
• Naive Bayes
• Perceptron
• Linear and Polynomial Regression
• Logistic Regression

3. Building a Machine Learning Web Application Using Flask

• What is a Deployment and Why Do We Deploy Machine Learning Models
• Develop a Machine Learning Pipeline and Train Models Using PyCaret
• Build a Simple Web App Using a Python Framework Called ‘Flask’
• Deploy a Web App on ‘Heroku’ and See Your Model in Action

4. Build a Machine Learning Simulation Tool with Dash

TRAINING METHOD

Presentation

Discussion

Case Study

Evaluation

FACILITIES

Training Kit

Handout

Certificate

Lunch + 2x Coffee Break

Souvenir

Pick Up Participant (Yogyakarta only)

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	Data Materi Training
Topik Training	: Python, Machine Learning, Flask and Dash
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