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Building Machine Learning Web Apps with Python

Name: Building Machine Learning Web Apps with Python
Rating: 4.2 (208 reviews)

Going Beyond Machine Learning Models

Created byJesse E. Agbe

Last updated 9/2024

English

English [Auto],

What you'll learn

Building Machine Learning Models with Python
Build Machine Learning Web Apps
How to Convert ML Models into Simple and Useful Products
How to Use ML Models as Packages
Embedding ML Models into Web Apps [Flask,Streamlit,etc]
How to use Streamlit to build ML apps
How to use Flask to build web applications
Productionize ML Models

Course content

7 sections • 94 lectures • 26h 12m total length

Introduction4:44
Course Introduction and Outline
Objectives2:32
Types of Machine Learning Apps2:21
Explore five categories of machine learning apps—detection and inspection, predictive analytics, pattern recognition, natural language processing apps, and cognitive systems—with examples like diabetes retinopathy, fraud detection, Netflix recommendations, and chatbots.
4 Ways of Productionizing ML Models5:27
Ways To Productionize Your Machine Learning Models
Using Web Apps (Flask,Pyramid,Django,Express,etc)
Using Your ML Models as API
Using Streamlit
Using Your ML Models as a Package
Using Docker
Building 3 ML Products At Once with Hug - Demo4:51
Explore packaging a machine learning model with hug, then deploy it as a package, a rest api, and a cli to build three ml products at once.
How to Setup Your Workspace6:51
Set up your workspace for cross-platform machine learning projects, choosing a suitable virtual environment (venv, virtualenv, or conda) to manage packages and ensure reproducibility.
How to Setup Your Workspace - Using Pipenv21:41
Using Pipenv
How to Install Pipenv on Your System
pip install pipenv
How to Setup Your Workspace - Using Pipes2:56
How to Setup Your Workspace - Using Poetry9:06
Where to Find Datasets & Course Materials & Code4:48
Explore datasets, course materials, and code sources for building machine learning web apps, from the UCI repository and GitHub to data portals like datahub and Microsoft open data.
Building Machine Learning Models - Salary Prediction - Introduction8:17
Building Machine Learning Models - Salary Prediction7:08
Build supervised models by defining features and a target, split data, train using random forest and other algorithms, and improve performance through cross‑validation and hyperparameter tuning for salary prediction.
Building Machine Learning Models - Interpreting ML Models5:07
Learn to interpret machine learning models by analyzing feature contributions to predictions, using top features, feature names, and class labels for clear explanations.
Building Machine Learning Models - Bible Passage Prediction14:25
Building Machine Learning Models - Saving ML Models4:01
Building Machine Learning Models - Gender Classification - Quick Overview5:25
In this lecture we will be going on a fast pace to get an idea of how to build models for gender classification of names. We will be using these saved models in the next sections to build packages and other products.
Building Machine Learning Models - Evaluating Car Quality with ML18:06
Build and evaluate car quality models from a U.S. dataset using Python, applying encoding, scaling, and machine learning methods like logistic regression and MLP, and explain predictions with LIME.

Flask Crash Course - Introduction4:42
Learn to install Flask from pip, create a basic Flask app, enable debug mode for development, and run a simple route locally to view output.
Flask Crash Course - Rendering HTML3:55
Learn how to render html in a Flask app by wiring the front end to the back end, organizing templates in a templates folder, and rendering a simple home page.
Flask Crash Course - Working with Jinja4:21
Flask Crash Course - Receiving Data From Front-End9:16
Learn how to receive data from the front end to the back end with Flask forms. Build first name and last name inputs and post data for processing.
Flask Crash Course - Processing Data at Back-End1:23
Flask Crash Course - Working with Databases10:43
Flask Crash Course - Retrieving Data From Database5:59
Retrieve data from the database using Flask and Flask-SQLAlchemy. Display the users list in a template with a for loop showing first name and last name.
Flask Crash Course - Searching Databases4:33
Learn to search for users in a database using query filters and render results with templates, building and displaying user profiles by first names in a Flask crash course.
Streamlit Crash Course47:32
Streamlit Crash Course
Streamlit - A Machine Learning Framework for building ML Tools
Installation
pip install streamlit
Streamlit Crash Course - Plots and Work Around15:47
Introduction to Hug Framework For API Development9:55
Streamlit- Building A simple CRUD Blog App1:10:51
Streamlit - Adding a Login Section To the Blog39:59
Learn how to add a login section to a blog app in Streamlit, enabling sign up, login, and posting restricted to authenticated users, with hashed passwords and basic database integration.
Streamlit- How to Change Page Name and Icon8:11
Streamlit - How to add Layouts to your App22:15
Explore how to create Streamlit layouts using two or three columns, packing and unpacking variables, and sidebars to organize a movie database app.

Introduction To Building ML Apps0:27
Explore how to build machine learning apps using Python, turning models into APIs and integrating tools to launch web-based ML applications.
Building ML Flask Apps2:02
Building ML Flask Apps - Installation and Basic App4:21
Boot up a Flask app from scratch, setting up templates, static files, and models. Learn to render templates, initialize routes, and run and debug the development server.
Building ML Flask Apps - Embedding ML Into Flask21:24
Building ML Flask Apps - Beautifying the Front-End0:53
Explore various approaches to beautify the front end of ML Flask apps using bootstrap and practical design techniques to improve aesthetics and user experience.
Salary Predictor ML App - Demo10:40
Building ML Web Apps - Setting Up and Exploratory Data Analysis of App11:19
Salary Predictor ML App - EDA Aspect16:10
Salary Predictor ML App - EDA Aspect 25:19
Explore data visualization in the salary predictor app by installing and using matplotlib and seaborn to create correlation plots and heatmaps for eda.
Building ML Apps - Salary Predictor - Prediction Aspect22:51
Develops the prediction aspect by mapping dictionary features and encoding user inputs—education, marital status, occupation, race, native country, gender, and hours per week—into a JSON vector for salary prediction.
Building ML Apps - Salary Predictor - Prediction Aspect 214:25
Building ML Apps - Salary Predictor - Metrics and Monitoring App15:03
Building ML Apps - Salary Predictor - Countries Aspect17:35
CMC Predictor ML App - Demo5:03
Building ML Apps - CMC - Predictor - Setting Up9:07
Building ML Apps - CMC - Predictor - EDA26:57
Building ML Apps - CMC - Predictor - Prediction29:31
Explore building a predictive web app that collects user inputs via sliders and radio buttons, converts them to numeric features, loads models with joblib, and outputs labeled predictions.
Building NLP Apps - Sentiment Analysis and Emoji App33:43
Building NLP Apps - Summary and Entity Checker App35:57
Build a Python NLP web app that summarizes text and checks entities with streamlit, spaCy, and BeautifulSoup, featuring interactive entity extraction and concise result rendering.
Building A Drag a Drop ML App43:50
This utilizes the new feature which is found only version 0.52.1 and upwards.
To install you will need to use this
pip install streamlit==0.52.1
Course Materials and Codes0:12
Building ML Apps - Password Strength Classifier (Password Masking Feature)30:03
Building ML Apps - Car Evaluation ML App32:26
Building Computer Vision ML App - Face Detection App - Demo5:08
Building Computer Vision ML App - Face Detection App - Building the App40:23
Emoji Lookup App with Streamlit - Demo4:23
Trend Analysis App For Programming Languages Search Term -Demo8:05
Trend Analysis App with Streamlit (For Programming Languages)50:55

Building the Model For Gender Classification of Names5:25
Learn to convert names into vectors, train a gender classification model, and save the model for packaging into a machine learning web app.
Using ML Models as Packages - Gender Classifier ML Package Demo2:35
Gender Classifier ML Package - Creating the Class7:29
Build a Python package from scratch and later with Poetry, structure a package with __init__.py, core module, and tests, and implement a simple gender classifier class with initialization.
Gender Classifier ML Package - Adding the Prediction to Package9:17
Gender Classifier ML Package - Loading Different Models5:24
select a model type and load the corresponding model (base, logistic regression, or others) in the gender classifier package to run predictions and add an Indonesian classification option.
Gender Classifier ML Package - Classifying Names5:59
Gender Classifier ML Package - Unit Testing Our Package4:42
Gender Classifier ML Package - Building Our Package with Setuptools8:25
Learn to build a gender classifier package using setuptools and poetry, configuring setup.py and pyproject.toml, managing dependencies, metadata, and package data for distribution.
Gender Classifier ML Package - Building Our Package with Poetry8:19
Build a Python package with poetry for a gender classifier, creating the project, managing dependencies in pyproject.toml, and preparing metadata and tests for publication.
Gender Classifier ML Package - Publishing Our Package3:51
Spam Detector ML Package - In Depth38:09

Introduction to FastAPI8:22
Learn to build a FastAPI server, define routes, run with uvicorn, and explore the automatic docs and simple test examples.
Serving Machine Learning Models As API21:19
In this lecture we will learn how to serve or use our ML models as API using FastAPI, a high performance framework.
Adding Validations To Parameters6:47
Building 3 Machine Learning Products at Once with Hug Framework27:51
So far we have seen how to productionize our ML models in several ways. Another great tool you can use to simplify the building of these ML products is to use Hug.
Hug is a framework that exposes your code in several ways specifically in 3 Main Ways
Local Package
API
CLI
In this section we will learn how to do so.
Building A Simple API,CLI and Package with Hug Framework26:25

How to Deploy Streamlit Apps to Heroku24:38
Updating an Already Deployed App6:04
How to Deploy Streamlit Apps to AWS EC223:15
How to Deploy Streamlit Apps with Docker17:31
Deploy streamlit apps with docker and streamlit deploy for teams. Build a dockerfile, copy requirements, set an entrypoint, run the container, and expose port 8501 for local or team deployment.
How to Deploy Streamlit Apps on Google Cloud Platform (App Engine)15:12
Deploy a streamlit app on google cloud platform app engine by creating a gcp project, preparing a python-based app, and deploying with gcloud app deploy.
Updating and Deleting A Streamlit App on GCP5:26
How to Deploy Streamlit OpenCV app - Face Detection App on Heroku21:53
How to Run Streamlit Apps From Google Colab12:47
Install streamlit in a Google Colab workflow, set up a tunnel to expose a public url, then run the streamlit app in the background to share it with others.
How to Deploy Streamlit Apps with Streamlit Sharing7:17
Adding Streamlit Sharing Badge to GitHub2:46
Managing the Deployed App on Streamlit Sharing1:40
Deploying NLP Flask Apps with Hashicorp's WayPoint14:27
Load Testing FastAPI NLP Apps21:01
Saving and Serving ML Models with MLEM20:00
MLflow - ML Model Experiment Tracking25:13

Data Science Project 1 - Hepatitis Mortality Prediction2:43:20
Follow the data science lifecycle to build a hepatitis mortality prediction model from raw data, including cleaning, preprocessing, feature selection, evaluation, and interpretation in a simple web app.
Data Science Project 1 - Hepatitis Predictor ML App with Streamlit1:10:41
Data Science Project 1 - Hepatitis Predictor ML App with Flask43:11
Develop a flask-based hepatitis predictor web app that collects patient features, encodes inputs for a machine learning model, and presents a prediction and deployability score.

Requirements

Understand the basics of python and machine learning
Basic Knowledge of HTML,CSS
Ability to work around a computer and a terminal
Determination

Description

Course Description

Artificial Intelligence and Machine Learning is affecting every area of our lives and society. Google, Amazon, Netflix, Uber, Facebook and many more industries are using AI and ML models in their products.

The opportunities and advantages of Machine Learning is quite numerous.

What if you could also build your own machine learning models?

What if you can build something useful from the ML model you have spend time creating and make some profit whiles helping people and changing the world?

In this wonderful course, we will be exploring the various ways of converting your machine learning models into useful web applications and products.

We will move beyond just building machine learning models into build products from our ML Models.

Products that you can give to your customers and other users to benefit from. We will be adding simple UI to our AI and ML models.

With every section of the course you will develop new skills and improve your understanding of this challenging yet important sub-field of Data Science and Machine Learning.

This course is unscripted,fun and exciting but at the same time we dive deep into building Machine Learning web applications.

What You will Gain in this Course

In this course you will develop new skills as you learn:

how to setup your Data Science and ML work-space locally.
how to build machine learning models.
how to interpret ML models with Eli5.
how to serialize and save ML models.
how to build ML web apps using the models we have created.
how to build packages from your ML Models.
how to deploy your products.

etc

Join us as we explore the world of building Machine Learning apps and tools.

Who this course is for:

Programmers and Developers
Any one interested in building web apps
ML Engineers and Data Scientist
Beginner Python Developers interested in Machine Learning and Data Science
People curious about how to build and productionize their machine learning models

Building Machine Learning Web Apps with Python

What you'll learn

Explore related topics

Course content

Introduction To Building ML Apps17 lectures • 2hr 8min

Crash Courses On Web Frameworks15 lectures • 4hr 19min

Building ML Apps28 lectures • 8hr 18min

Using ML Models as Packages11 lectures • 1hr 40min

Using ML Models as API5 lectures • 1hr 31min

Deploying Our ML Apps15 lectures • 3hr 39min

Bonus Section - Data Science Project From Scratch3 lectures • 4hr 37min

Requirements

Description

Who this course is for: