Build ML App (Streamlit Python) + Top Data Science projects

Build ML App (Streamlit Python) + Top Data Science projects, available at $44.99, has an average rating of 3.95, with 48 lectures, based on 27 reviews, and has 9396 subscribers.

You will learn about How to build AI applications How to use streamlit How to apply the concepts of AI in a real world web application How to host a AI web application This course is ideal for individuals who are Experienced data scientists or College students or Data scientists who are starting their career or Web application developers or IT professionals who want to switch their career to AI. It is particularly useful for Experienced data scientists or College students or Data scientists who are starting their career or Web application developers or IT professionals who want to switch their career to AI.

Enroll now: Build ML App (Streamlit Python) + Top Data Science projects

Summary

Title: Build ML App (Streamlit Python) + Top Data Science projects

Price: $44.99

Average Rating: 3.95

Number of Lectures: 48

Number of Published Lectures: 48

Number of Curriculum Items: 48

Number of Published Curriculum Objects: 48

Original Price: $59.99

Quality Status: approved

Status: Live

What You Will Learn

How to build AI applications
How to use streamlit
How to apply the concepts of AI in a real world web application
How to host a AI web application

Who Should Attend

Experienced data scientists
College students
Data scientists who are starting their career
Web application developers
IT professionals who want to switch their career to AI.

Target Audiences

Experienced data scientists
College students
Data scientists who are starting their career
Web application developers
IT professionals who want to switch their career to AI.

AI landscape is evolving fast, though these are still early days for AI. The focus of AI has been more on building models and analyzing data, while users were asking for crisp outputs and self-use interactive applications. It’s not that the data science and AI community was not aware of these needs. The lack of web skills like javascript, html/css etc., became a roadblock. We can’t blame data scientists too since data science and web technologies are two separate streams of specializations. So, only a large team with a mix of data science and web technology specialists could build an AI app that users were looking for.

In summary, end users wanted a simple web app to view the results of AI algorithms and data scientists wanted a platform to build AI web apps easily & faster. Streamlit addressed both these needs perfectly.

I am going to demonstrate how to build a healthcare AI app (and few other examples) in less than 50 lines of code using streamlit platform. This covers AI/ML code as well as code for the app including the user interface. We will start with the functionalities of streamlit and then cover how to build and host web applications.

For those who are new to AI, Machine Learning, Deep Learning, Natural Language Processing (NLP) and Exploratory Data Analysis (EDA) are included in the program. Python is also covered extensively to assist those who are looking for a refresher on python topics or new to python itself.

In all, this program can be pursued by both experienced professionals as well as those who are new to the world of AI.

Let’s build stunning web based AI apps!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Need for an AI App & Streamlit

Lecture 1: Need for AI Apps

Lecture 2: Need for Streamlit

Chapter 3: Infrastructure

Lecture 1: Required Installs

Chapter 4: Understanding Streamlit's Functionalities

Lecture 1: Creating a very simple web app and Getting started with streamlit

Lecture 2: Header and Sub Header

Lecture 3: Reading and displaying contents of a file

Lecture 4: Creating and displaying graphs

Lecture 5: Adding a logo or image to your app

Lecture 6: Uploading a file

Lecture 7: Submit button, Selection box and Sliders

Lecture 8: Drop down – Single and Multiple Selection Options

Lecture 9: Changing themes

Chapter 5: EDA (Exploratory Data Analysis) App

Lecture 1: Introduction to EDA and Missing Values Treatment

Lecture 2: EDA (Exploratory Data Analysis) App

Chapter 6: Breast cancer Prediction App

Lecture 1: Breast cancer Prediction App

Chapter 7: Word Cloud App

Lecture 1: Word Cloud App

Chapter 8: Time Series Forecasting App

Lecture 1: Time Series Forecasting App

Chapter 9: Customer Lifetime Value

Lecture 1: Customer Lifetime Value (CLV): Understand the concepts

Lecture 2: CLV (Customer Lifetime Value) Code and Demo

Chapter 10: Market Basket Analysis (Retail Analytics)

Lecture 1: Market Basket Analysis

Chapter 11: Deploying the app

Lecture 1: Deploying the app in Heroku

Lecture 2: Deploying Streamlit app using Streamlit platform

Chapter 12: Key Concepts

Lecture 1: Machine Learning Concepts

Lecture 2: Logistic Regression and Introduction to Deep Learning

Lecture 3: Natural Language Processing (NLP)

Chapter 13: Python Programming

Lecture 1: Getting Started with Python

Lecture 2: Variables

Lecture 3: Operators

Lecture 4: Conditions

Lecture 5: Loops

Lecture 6: Functions

Lecture 7: Arrays

Lecture 8: List

Lecture 9: Tuple

Lecture 10: Set

Lecture 11: Dictionary

Lecture 12: Getting Started with NumPy

Lecture 13: Numpy: Shape in Arrays

Lecture 14: Numpy: Joining Arrays

Lecture 15: Numpy: Splitting Arrays

Lecture 16: Numpy: Searching and Sorting Arrays

Lecture 17: Getting Started with Pandas

Lecture 18: Pandas: Dataframe

Lecture 19: Pandas: Descriptive Statistics

Lecture 20: Pandas: Sorting, Slicing, Flipping, Grouping Data

Lecture 21: Data Visualization using Matplotlib

Chapter 14: Bonus Lecture

Lecture 1: Bonus Lecture

Instructors

SeaportAi .
Artificial Intelligence and Business Transformation Experts

Rating Distribution

1 stars: 3 votes
2 stars: 0 votes
3 stars: 1 votes
4 stars: 7 votes
5 stars: 16 votes

Frequently Asked Questions

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