Data Science: Diabetes Prediction- Model Building Deployment

Data Science: Diabetes Prediction- Model Building Deployment, available at $54.99, has an average rating of 3.83, with 28 lectures, based on 3 reviews, and has 41 subscribers.

You will learn about Data Analysis and Understanding Data Cleaning and Imputation Data Preparation Model Building for Diabetes Prediction Hyperparameter Tuning Classification Metrics Model Evaluation Running the model on a local Streamlit Server Pushing your notebooks and project files to GitHub repository Deploying the project on Heroku Cloud Platform This course is ideal for individuals who are Students and professionals who want to learn Data Analysis, Data Preparation for Model building, Evaluation and Model Deployment on Cloud. or Students and professionals who wants to visually interact with their created models. or Professionals who knows how to create models but wants to deploy their models on cloud platform. It is particularly useful for Students and professionals who want to learn Data Analysis, Data Preparation for Model building, Evaluation and Model Deployment on Cloud. or Students and professionals who wants to visually interact with their created models. or Professionals who knows how to create models but wants to deploy their models on cloud platform.

Enroll now: Data Science: Diabetes Prediction- Model Building Deployment

Summary

Title: Data Science: Diabetes Prediction- Model Building Deployment

Price: $54.99

Average Rating: 3.83

Number of Lectures: 28

Number of Published Lectures: 28

Number of Curriculum Items: 28

Number of Published Curriculum Objects: 28

Original Price: ₹999

Quality Status: approved

Status: Live

What You Will Learn

Data Analysis and Understanding
Data Cleaning and Imputation
Data Preparation
Model Building for Diabetes Prediction
Hyperparameter Tuning
Classification Metrics
Model Evaluation
Running the model on a local Streamlit Server
Pushing your notebooks and project files to GitHub repository
Deploying the project on Heroku Cloud Platform

Who Should Attend

Students and professionals who want to learn Data Analysis, Data Preparation for Model building, Evaluation and Model Deployment on Cloud.
Students and professionals who wants to visually interact with their created models.
Professionals who knows how to create models but wants to deploy their models on cloud platform.

Target Audiences

Students and professionals who want to learn Data Analysis, Data Preparation for Model building, Evaluation and Model Deployment on Cloud.
Students and professionals who wants to visually interact with their created models.
Professionals who knows how to create models but wants to deploy their models on cloud platform.

This course is about predicting whether or not the person has diabetes using Machine Learning Models. This is a hands on project where I will teach you the step by step process in creating and evaluating a machine learning model and finally deploying the same on Cloud platforms to let your customers interact with your model via an user interface.

This course will walk you through the initial data exploration and understanding, data analysis, data preparation, model building, evaluation and deployment techniques. We will explore multiple ML algorithms to create our model and finally zoom into one which performs the best on the given dataset.

At the end we will learn to create an User Interface to interact with our created model and finally deploy the same on Cloud.

I have splitted and segregated the entire course in Tasks below, for ease of understanding of what will be covered.

Task 1 : Installing Packages

Task 2 : Importing Libraries.

Task 3 : Loading the data from source.

Task 4 : Pandas Profiling

Task 5 : Understanding the data

Task 6 : Data Cleaning and Imputation

Task 7 : Train Test Split

Task 8 : Scaling using StandardScaler

Task 9 : About Confusion Matrix

Task 10 : About Classification Report

Task 11 : About AUC-ROC

Task 12 : Checking for model performance across a wide range of models

Task 13 : Creating Random Forest model with default parameters

Task 14 : Model Evaluation – Classification Report,Confusion Matrix,AUC-ROC

Task 15 : Hyperparameter Tuning using RandomizedSearchCV

Task 16 : Building RandomForestClassifier model with the selected hyperparameters

Task 17 : Final Model Evaluation – Classification Report,Confusion Matrix,AUC-ROC

Task 18 : Final Inference

Task 19 : Loading the saved model and scaler objects

Task 20 : Testing the model on random data

Task 21 : What is Streamlit and Installation steps.

Task 22 : Creating an user interface to interact with our created model.

Task 23 : Running your notebook on Streamlit Server in your local machine.

Task 24 : Pushing your project to GitHub repository.

Task 25 : Project Deployment on Heroku Platform for free.

Data Analysis, Model Building and Deployment is one of the most demanded skill of the 21st century. Take the course now, and have a much stronger grasp of data analysis, machine learning and deployment in just a few hours!

You will receive :

1. Certificate of completion from AutomationGig.

2. All the datasets used in the course are in the resources section.

3. The Jupyter notebook and other project files are provided at the end of the course in the resource section.

So what are you waiting for?

Grab a cup of coffee, click on the ENROLL NOW Button and start learning the most demanded skill of the 21st century. We’ll see you inside the course!

Happy Learning !!

[Please note that this course and its related contents are for educational purpose only]

Music : bensound

Course Curriculum

Chapter 1: Introduction and Getting Started

Lecture 1: Project Overview

Lecture 2: Installing Packages

Chapter 2: Data Understanding, Exploration & Cleaning

Lecture 1: Problem Statement overview and Importing Libraries

Lecture 2: Loading the data from source

Lecture 3: Pandas Profiling

Lecture 4: Understanding the data

Lecture 5: Data Cleaning and Imputation

Chapter 3: Data Preparation

Lecture 1: Train Test Split

Lecture 2: Scaling using StandardScaler

Chapter 4: Classification Metrics

Lecture 1: About Confusion Matrix

Lecture 2: About Classification Report

Lecture 3: About AUC-ROC

Chapter 5: Model Building and Evaluation

Lecture 1: Checking for model performance across a wide range of models

Lecture 2: Creating Random Forest model with default parameters

Lecture 3: Model Evaluation – Classification Report,Confusion Matrix,AUC-ROC

Lecture 4: Hyperparameter Tuning using RandomizedSearchCV

Lecture 5: Building RandomForestClassifier model with the selected hyperparameters

Lecture 6: Final Model Evaluation – Classification Report,Confusion Matrix,AUC-ROC

Lecture 7: Final Inference

Chapter 6: Model in Action

Lecture 1: Loading the saved model and scaler objects

Lecture 2: Testing the model on random data

Chapter 7: Running the model on a local Server

Lecture 1: What is Streamlit and Installation steps

Lecture 2: Creating an user interface to interact with our created model.

Lecture 3: Running the model on Local Streamlit Server

Chapter 8: Deploying the project on Heroku Platform

Lecture 1: Updating your Project directory

Lecture 2: Pushing your code to Github repository

Lecture 3: Project deployment on Heroku Platform

Chapter 9: Project Files and Code

Lecture 1: Full Project Code

Instructors

AutomationGig .
ELEARNING HUB

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1 stars: 1 votes
2 stars: 0 votes
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4 stars: 0 votes
5 stars: 2 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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