Build & Host AI Apps (Python) + 6 Real World Projects

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Build & Host AI Apps (Python) + 6 Real World Projects [TutsNode.com] - Build & Host AI Apps (Python) + 6 Real World Projects 12 Key Concepts
  • 002 Logistic Regression and Introduction to Deep Learning.mp4 (219.2 MB)
  • 002 Logistic Regression and Introduction to Deep Learning.en.srt (49.5 KB)
  • 001 Machine Learning Concepts.en.srt (37.5 KB)
  • 003 Natural Language Processing (NLP).en.srt (22.5 KB)
  • 001 Machine Learning Concepts.mp4 (149.0 MB)
  • 003 Natural Language Processing (NLP).mp4 (78.8 MB)
01 Introduction
  • 001 Introduction.en.srt (1.9 KB)
  • 001 Introduction.mp4 (14.1 MB)
08 Time Series Forecasting App
  • 018 Prophet model ts data.csv (66.5 KB)
  • 001 Time Series Forecasting App.en.srt (12.8 KB)
  • 018 time series.txt (0.8 KB)
  • 001 Time Series Forecasting App.mp4 (38.6 MB)
02 Need for an AI App & Streamlit
  • 001 Need for AI Apps.en.srt (1.4 KB)
  • 002 Need for Streamlit.en.srt (1.5 KB)
  • 002 Need for Streamlit.mp4 (5.6 MB)
  • 001 Need for AI Apps.mp4 (3.4 MB)
03 Infrastructure
  • 001 Required Installs.en.srt (1.6 KB)
  • 001 Required Installs.mp4 (9.1 MB)
05 EDA (Exploratory Data Analysis) App
  • 002 EDA (Exploratory Data Analysis) App.en.srt (17.5 KB)
  • 001 Introduction to EDA and Missing Values Treatment.en.srt (7.9 KB)
  • 015 EDA app.py (3.3 KB)
  • 002 EDA (Exploratory Data Analysis) App.mp4 (69.7 MB)
  • 001 Introduction to EDA and Missing Values Treatment.mp4 (45.9 MB)
06 Breast cancer Prediction App
  • 001 Breast cancer Prediction App.en.srt (12.7 KB)
  • external-assets-links.txt (0.1 KB)
  • 001 Breast cancer Prediction App.mp4 (56.4 MB)
11 Deploying the app
  • 001 Deploying the app in Heroku.en.srt (11.2 KB)
  • 002 Deploying Streamlit app using Streamlit platform.en.srt (5.1 KB)
  • 001 Deploying the app in Heroku.mp4 (52.7 MB)
  • 002 Deploying Streamlit app using Streamlit platform.mp4 (18.7 MB)
10 Market Basket Analysis (Retail Analytics)
  • 001 Market Basket Analysis.en.srt (10.7 KB)
  • 021 MBA.txt (2.4 KB)
  • external-assets-links.txt (0.1 KB)
  • 001 Market Basket Analysis.mp4 (38.2 MB)
13 Python Programming
  • 012 Getting Started with NumPy.en.srt (9.5 KB)
  • 021 Data Visualization using Matplotlib.en.srt (9.2 KB)
  • 002 Variables.en.srt (8.1 KB)
  • 017 Getting Started with Pandas.en.srt (8.0 KB)
  • 005 Loops.en.srt (8.0 KB)
  • 018 Pandas_ Dataframe.en.srt (7.0 KB)
  • 020 Pandas_ Sorting, Slicing, Flipping, Grouping Data.en.srt (7.9 KB)
  • 004 Conditions.en.srt (7.0 KB)
  • 003 Operators.en.srt (5.9 KB)
  • 009 Tuple.en.srt (5.8 KB)
  • 006 Functions.en.srt (5.7 KB)
  • 010 Set.en.srt (5.2 KB)
  • 001 Getting Started with Python.en.srt (5.0 KB)
  • 008 List.en.srt (4.9 KB)
  • 013 Numpy_ Shape in Arrays.en.srt (4.3 KB)
  • 007 Arrays.en.srt (4.3 KB)
  • 019 Pandas_ Descriptive Statistics.en.srt (3.8 KB)
  • 014 Numpy_ Joining Arrays.en.srt (3.1 KB)
  • 011 Dictionary.en.srt (3.1 KB)
  • 016 Numpy_ Searching and Sorting Arrays.en.srt (2.9 KB)
  • 015 Numpy_ Splitting Arrays.en.srt (1.9 KB)
  • 017 Getting Started with Pandas.mp4 (34.2 MB)
  • 021 Data Visualization using Matplotlib.mp4 (33.0 MB)
  • 012 Getting Started with NumPy.mp4 (28.5 MB)
  • 018 Pandas_ Dataframe.mp4 (28.1 MB)
  • 002 Variables.mp4 (24.6 MB)
  • 020 Pandas_ Sorting, Slicing, Flipping, Grouping Data.mp4 (22.7 MB)
  • 007 Arrays.mp4 (20.1 MB)
  • 005 Loops.mp4 (19.9 MB)
  • 009 Tuple.mp4 (19.8 MB)
  • 010 Set.mp4 (19.6 MB)
  • 003 Operators.mp4 (18.8 MB)
  • 004 Conditions.mp4 (18.3 MB)
  • 008 List.mp4 (15.4 MB)
  • 006 Functions.mp4 (15.3 MB)
  • 001 Getting Started with Python.mp4 (13.7 MB)
  • 014 Numpy_ Joining Arrays.mp4 (13.4 MB)
  • 011 Dictionary.mp4 (13.0 MB)
  • 013 Numpy_ Shape in Arrays.mp4 (12.7 MB)
  • 019 Pandas_ Descriptive Statistics.mp4 (10.8 MB)
  • 016 Numpy_ Searching and Sorting Arrays.mp4 (10.3 MB)
  • 015 Numpy_ Splitting Arrays.mp4 (6.1 MB)
04 Understanding Streamlit's Functionalities
  • 004 Creating and displaying graphs.en.srt (2.2 KB)
  • 005 Adding a logo or image to your app.en.srt (2.8 KB)
  • 006 Uploading a file.en.srt (3.0 KB)
  • 001 Creating a very simple web app and Getting started with streamlit.en.srt (6.7 KB)
  • 007 Submit button, Selection box and Sliders.en.srt (4.8 KB)
  • 002 Header and Sub Header.en.srt (3.8 KB)
  • 008 Drop down - Single and Multiple Selection Options.en.srt (3.6 KB)
  • 003 Reading and displaying contents of a file.en.srt (3.4 KB)
  • 009 Changing themes.en.srt (3.2 KB)
  • 001 Creating a very simple web app and Getting started with streamlit.mp4 (24.6 MB)
  • 007 Submit button, Selection box and Sliders.mp4 (21.3 MB)
  • 009 Changing themes.mp4 (15.1 MB)
  • 005 Adding a logo or image to your app.mp4 (13.5 MB)
  • 002 Header and Sub Header.mp4 (13.5 MB)
  • 008 Drop down - Single and Multiple Selection Options.mp4 (12.0 MB)
  • 003 Reading and displaying contents of a file.mp4 (11.8 MB)
  • 004 Creating and displaying graphs.mp4 (10.6 MB)
  • 006 Uploading a file.mp4 (9.8 MB)
07 Word Cloud App
  • 001 Word Cloud App.en.srt (8.0 KB)
  • 001 Word Cloud App.mp4 (38.1 MB)
09 Customer Lifetime Value
  • external-assets-links.txt (0.1 KB)
  • 002 CLV (Customer Lifetime Value) Code and Demo.en.srt (6.3 KB)
  • 001 Customer Lifetime Value (CLV)_ Understand the concepts.en.srt (4.7 KB)
  • 020 clv.py (2.6

Description


Description

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!
Who this course is for:

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.

Requirements

None. Concepts and Python are covered extensively to assist those who are new to Python & AI.

Last Updated 8/2021



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seeders:27
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Build & Host AI Apps (Python) + 6 Real World Projects

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1.4 GB
seeders:27
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Build & Host AI Apps (Python) + 6 Real World Projects

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