Learn Streamlit Python
- Descrição
- Currículo
- FAQ
- Revisões
Are you having difficulties trying to build web applications for your data science projects? Do you spend more time trying to create a simple MVP app with your data to show your clients and others? Then let me introduce you to Streamlit – a python framework for building web apps.
Welcome to the coolest online resource for learning how to create Data Science Apps and Machine Learning Web Apps using the
awesome Streamlit Framework and Python.
This course will teach you Streamlit – the python framework that saves you from spending days and weeks in creating
data science and machine learning web applications.
In this course we will cover everything you need to know concerning streamlit such as
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Fundamentals and the Basics of Streamlit ;
– Working with Text
– Working with Widgets (Buttons,Sliders,
– Displaying Data
– Displaying Charts and Plots
– Working with Media Files (Audio,Images,Video)
– Streamlit Layouts
– File Uploads
– Streamlit Static Components
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Creating cool data visualization apps
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How to Build A Full Web Application with Streamlit
By the end of this exciting course you will be able to
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Build data science apps in hours not days
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Productionized your machine learning models into web apps using streamlit
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Build some cools and fun data apps
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Deploy your streamlit apps using Docker,Heroku,Streamlit Share and more
Join us as we explore the world of building Data and ML Apps.
See you in the Course,Stay blessed.
Tips for getting through the course
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Please write or code along with us do not just watch,this will enhance your understanding.
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You can regulate the speed and audio of the video as you wish,preferably at -0.75x if the speed is too fast for you.
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Suggested Prerequisites is understanding of Python
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This course is about Streamlit an ML Framework to create data apps in hours not weeks. We will try our best to cover some concepts for the beginner and the pro .
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1IntroductionVídeo Aula
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2Where to Get Help and Quick Course Guide and MaterialsVídeo Aula
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3What is Streamlit?Vídeo Aula
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4Why Learn Streamlit?Vídeo Aula
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5Overview of Streamlit Framework APIVídeo Aula
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6Setup & Installation In Virutal EnvironmentVídeo Aula
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7Exploring StreamlitVídeo Aula
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8Displaying Text In StreamlitVídeo Aula
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9Behind the Source Code - Inspecting the TextVídeo Aula
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10Working with Colorful Bootstap-Like TextVídeo Aula
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11Displaying Results with St.write() "Superfunction"Vídeo Aula
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12Displaying Pandas DataFrame,Tables and JSONVídeo Aula
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13Working with Streamlit Widgets - Buttons,Radio Buttons and CheckboxVídeo Aula
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14Working with Streamlit Widgets - Select, Multi-select,Sliders and Select SliderVídeo Aula
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15Displaying and Working with Media Files -Images,Audio and VideoVídeo Aula
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16Working with Text Input - Receiving Input From UserVídeo Aula
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17How to Configure Streamlit PageVídeo Aula
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18How to Update Streamlit & How to work with Beta ChangesVídeo Aula
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19Plotting In Streamlit : Using PlotlyVídeo Aula
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20Working with File Uploads - Indepth TutorialVídeo Aula
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21Saving Uploaded File into A Directory In StreamlitVídeo Aula
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22Working with Multiple File UploadsVídeo Aula
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23Structuring Streamlit AppsVídeo Aula
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24Tracking Visited Sections of Streamlit App Via LoggingVídeo Aula
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25How to Add File Downloads to Streamlit AppsVídeo Aula
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26Working with Streamlit FormsVídeo Aula
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27Streamlit-Forms - How to Reset FormsVídeo Aula
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28Memory Profiling Streamlit AppsVídeo Aula
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29Streamlit Data Editor (New Feature)Vídeo Aula
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30Streamlit Chat Input Widget (New Feature)Vídeo Aula
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31Streamlit Crash Course (All New Features)Vídeo Aula
⏲️===TimeStamps===⏲️
0:01 Introduction
01:30 Streamlit CLI
02:30 Text Elements
06:12 st.write, markdown
09:35 Error Elements
11:02 Input Widgets
13:15 Date & Number Input
14:57 Radio & Checkbox, Toggle
16:17 Sliders & Selectors
22:08 Data Elements
27:20 Media Elements(Img,Audio,Video)
29:35 Camera Input
32:49 File Upload & Download
35:20 Status Elements (spinner,progress)
37:40 St.toast
38:15 Chat Elements for LLM
42:20 Streaming Text- Typewriter Effect
46:27 Layout
47:04 st.tabs
48:37 st.columns
51:30 Containers in Streamlit
53:20 Expander to hide or show
53:50 Popover & Dialog
55:10 Plotting in Streamlit
58:10 Utils
59:10 St Forms
1:00:20 Streamlit Components
1:01:00 Link Button
1:02:01 Streamlit Session State
1:02:40 Streamlit cloud
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36Introduction to Streamlit ComponentsVídeo Aula
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37Working with Static Streamlit Components - HTML and IFrameVídeo Aula
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38Streamlit Themes - How to Customize Streamlit with New ThemesVídeo Aula
Note: Streamlit Themes are available from version 0.79 and upwards so you will have to upgrade or update to get this feature
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39Streamlit Multi-Pages (Native)Vídeo Aula
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40Streamlit Navigation PagesVídeo Aula
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41Streamlitflow - ReactFlow ComponentsVídeo Aula
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42Project - NLP & Summarization AppVídeo Aula
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43Project - Summarization App - Structuring the AppVídeo Aula
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44Project - Summarization App -Adding the Summary Process (LexRank and TextRank)Vídeo Aula
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45Project - Summarization App - Evaluating the Extractive Summary with RougeVídeo Aula
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46Project - Text Analysis & NLP AppVídeo Aula
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47Project -Text Analysis & Spacy App - Structuring the AppVídeo Aula
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48Project - Text Analysis & Spacy App - Adding the Text Analysis ProcessVídeo Aula
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49Project -Text Analysis & Spacy App - Word Statistics and Sentiment AnalysisVídeo Aula
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50Project - Text Analysis & Spacy App - Adding the Plots and VisualizationsVídeo Aula
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51Project - Text Analysis & Spacy App - File Download of ResultsVídeo Aula
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52Project - Text Analysis & Spacy App - File Upload (PDF,Txt and Docx)Vídeo Aula
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53Project - Text Analysis & Spacy App - Refactoring and Modularize The AppVídeo Aula
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54Project - Text Analysis & Spacy App - Fixing Insufficient Data For PlotVídeo Aula
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57Project 01 - MetaData Extracton App - DemoVídeo Aula
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58Project 01 - MetaData Extraction App - Setting Up and Structuring the AppVídeo Aula
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59Project 01 - MetaData Extraction App -Home SectionVídeo Aula
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60Project 01 - Building the File Upload SectionVídeo Aula
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61Project 01 - MetaData Extraction App - Extraction ProcessVídeo Aula
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62Project 01 - MetaData Extraction App - Adding Result DownloadVídeo Aula
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63Project 01 - MetaData Extracton App - Extracting MetaData From Audio filesVídeo Aula
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64Project 01 - MetaData Extraction App - Extracting MetaData From PDF SectionVídeo Aula
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65Project 01 - MetaData Extraction App - Analytics and Monitor SectionVídeo Aula
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66Static Code Analysis & Refactoring Streamlit AppVídeo Aula
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67Project - Machine Learning Web App - Diabetes Prediction App -DemoVídeo Aula
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68Project - Diabetes Prediction App - Structuring the AppVídeo Aula
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69Project -Diabetes Prediction App - Exploratory Data Analysis SectionVídeo Aula
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70Project - Diabetes Prediction App - Plotting and Data VisualizationVídeo Aula
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71Project - Diabetes Prediction App - Machine Learning SectionVídeo Aula
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72Project - Diabetes Prediction App - Applying the Models For PredictionVídeo Aula
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73Building the ML Model For Diabetes Prediction -Full LengthVídeo Aula
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76Simple CRUD App in Streamlit - DemoVídeo Aula
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77TaskList CRUD App - Structuring the AppVídeo Aula
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78TaskList CRUD App - Create (Adding Data To Database)Vídeo Aula
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79TaskList CRUD App - Update(Editing From the Front End)Vídeo Aula
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80TaskList CRUD App - Update the DatabaseVídeo Aula
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81TaskList CRUD App - Deleting DataVídeo Aula
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82TaskList CRUD App - Reading DataVídeo Aula
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83TaskList CRUD App - Analytics & PlotsVídeo Aula