(PART 3)Introduction to STREAMLIT for beginners | Building Machine Learning App
Learn How To Build Streamlit Apps For Your Machine Learning Projects
Moving your machine learning code from your Jupyter Notebook to deploy for stakeholders to access is a crucial task of every machine learning engineer.
In today's video, we will see a demo of how we will deploy our machine learning code from our jupyter notebook by building an interactive web app that can be accessible by our team managers and every stakeholders.
Code Materials: https://github.com/MrBriit/Streamlit
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In the subsequent videos, you will learn how to build a more robust streamlit App for your Machine Learning models.
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==================SAMPLE COURSE CONTENTS=================
Set Up Your Personal Blog/Website
Set Up Your Linkedin Page Professionally
Git and Github
Professional Resume Sample
Interview Preparations
One-on-One Career Coaching
Model Deployment
Study Group
Extra Resources
Lifetime Access
PROJECTS:
STREAMLIT PROJECTS DEPLOYMENT
Introduction to Streamlit
Building Data App-Project 1
Building Data App-Project 2
Building Data App-Project 3
Streamlit Mini-App Project
Streamli Main-App-Project 1
Streamli Main-App-Project 2
Streamli Main-App-Project 3
Streamli Main-App-Project 4
Streamlit Project Assignment
FLASK DEPLOYMENT
Introduction to Flask
Getting your dataset
Exploratory Data Analysis
Introduction to Pycharm
Creating Folders
Creating Folder Contents
Getting your Model.pkl ready
Final Flask Deployment
Flask Project Assignment
HEROKU DEPLOYMENT
Introduction to Heroku
Data Preparation 1
Data Preparation 2
Exploratory Data Analysis
Data Visualisation
Model Building
Heroku Deployment 1
Heroku Deployment 2
Heroku Project Assignment
DEPLOYMENT ON:
AWS
Google Cloud
Microsoft Azure
Final Project Research
LESSONS
Python For Data Science:
Google Colab/Jupyter notebook: Installation & function
Python Basics
Python functions & packages
Data structures
Arrays
Vectors
Data frames
Pandas
NumPy
Data Visualisation
Matplotlib
Seaborn
STATISTICS FOR DATA SCIENCE:
Descriptive Statistics
Probability & Conditional Probability
Hypothesis Testing
Inferential Statistics
Probability Distributions
MACHINE LEARNING:
Linear regression
Logistic regression
Naive Bayes classifiers
Multiple regression
K-NN classification
K-means clustering
Support vector machines
High-dimensional clustering
Hierarchical clustering
Dimension Reduction (Principal Component Analysis-PCA)
Decision Tree-CART
Random Forest
Bagging
Boosting
Stacking
CatBoost
XGBoost
LightGbm
GET ALL THE MATERIALS and OTHER PROJECTS HERE: https://bit.ly/2LqD8v4
Subscribe For more Tutorials and PROJECTS: https://bit.ly/3xfNeRN