Building Machine Learning Web Apps with Python

Building Machine Learning Web Apps with Python

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

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 DescriptionArtificial 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 CourseIn 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.    etcJoin us as we explore the world of building Machine Learning apps and tools.

Overview

Section 1: Introduction To Building ML Apps

Lecture 1 Introduction

Lecture 2 Objectives

Lecture 3 Types of Machine Learning Apps

Lecture 4 4 Ways of Productionizing ML Models

Lecture 5 Building 3 ML Products At Once with Hug – Demo

Lecture 6 How to Setup Your Workspace

Lecture 7 How to Setup Your Workspace – Using Pipenv

Lecture 8 How to Setup Your Workspace – Using Pipes

Lecture 9 How to Setup Your Workspace – Using Poetry

Lecture 10 Where to Find Datasets & Course Materials & Code

Lecture 11 Building Machine Learning Models – Salary Prediction – Introduction

Lecture 12 Building Machine Learning Models – Salary Prediction

Lecture 13 Building Machine Learning Models – Interpreting ML Models

Lecture 14 Building Machine Learning Models – Bible Passage Prediction

Lecture 15 Building Machine Learning Models – Saving ML Models

Lecture 16 Building Machine Learning Models – Gender Classification – Quick Overview

Lecture 17 Building Machine Learning Models – Evaluating Car Quality with ML

Section 2: Crash Courses On Web Frameworks

Lecture 18 Flask Crash Course – Introduction

Lecture 19 Flask Crash Course – Rendering HTML

Lecture 20 Flask Crash Course – Working with Jinja

Lecture 21 Flask Crash Course – Receiving Data From Front-End

Lecture 22 Flask Crash Course – Processing Data at Back-End

Lecture 23 Flask Crash Course – Working with Databases

Lecture 24 Flask Crash Course – Retrieving Data From Database

Lecture 25 Flask Crash Course – Searching Databases

Lecture 26 Streamlit Crash Course

Lecture 27 Streamlit Crash Course – Plots and Work Around

Lecture 28 Introduction to Hug Framework For API Development

Lecture 29 Streamlit- Building A simple CRUD Blog App

Lecture 30 Streamlit – Adding a Login Section To the Blog

Lecture 31 Streamlit- How to Change Page Name and Icon

Lecture 32 Streamlit – How to add Layouts to your App

Section 3: Building ML Apps

Lecture 33 Introduction To Building ML Apps

Lecture 34 Building ML Flask Apps

Lecture 35 Building ML Flask Apps – Installation and Basic App

Lecture 36 Building ML Flask Apps – Embedding ML Into Flask

Lecture 37 Building ML Flask Apps – Beautifying the Front-End

Lecture 38 Salary Predictor ML App – Demo

Lecture 39 Building ML Web Apps – Setting Up and Exploratory Data Analysis of App

Lecture 40 Salary Predictor ML App – EDA Aspect

Lecture 41 Salary Predictor ML App – EDA Aspect 2

Lecture 42 Building ML Apps – Salary Predictor – Prediction Aspect

Lecture 43 Building ML Apps – Salary Predictor – Prediction Aspect 2

Lecture 44 Building ML Apps – Salary Predictor – Metrics and Monitoring App

Lecture 45 Building ML Apps – Salary Predictor – Countries Aspect

Lecture 46 CMC Predictor ML App – Demo

Lecture 47 Building ML Apps – CMC – Predictor – Setting Up

Lecture 48 Building ML Apps – CMC – Predictor – EDA

Lecture 49 Building ML Apps – CMC – Predictor – Prediction

Lecture 50 Building NLP Apps – Sentiment Analysis and Emoji App

Lecture 51 Building NLP Apps – Summary and Entity Checker App

Lecture 52 Building A Drag a Drop ML App

Lecture 53 Course Materials and Codes

Lecture 54 Building ML Apps – Password Strength Classifier (Password Masking Feature)

Lecture 55 Building ML Apps – Car Evaluation ML App

Lecture 56 Building Computer Vision ML App – Face Detection App – Demo

Lecture 57 Building Computer Vision ML App – Face Detection App – Building the App

Lecture 58 Emoji Lookup App with Streamlit – Demo

Lecture 59 Trend Analysis App For Programming Languages Search Term -Demo

Lecture 60 Trend Analysis App with Streamlit (For Programming Languages)

Section 4: Using ML Models as Packages

Lecture 61 Building the Model For Gender Classification of Names

Lecture 62 Using ML Models as Packages – Gender Classifier ML Package Demo

Lecture 63 Gender Classifier ML Package – Creating the Class

Lecture 64 Gender Classifier ML Package – Adding the Prediction to Package

Lecture 65 Gender Classifier ML Package – Loading Different Models

Lecture 66 Gender Classifier ML Package – Classifying Names

Lecture 67 Gender Classifier ML Package – Unit Testing Our Package

Lecture 68 Gender Classifier ML Package – Building Our Package with Setuptools

Lecture 69 Gender Classifier ML Package – Building Our Package with Poetry

Lecture 70 Gender Classifier ML Package – Publishing Our Package

Lecture 71 Spam Detector ML Package – In Depth

Section 5: Using ML Models as API

Lecture 72 Introduction to FastAPI

Lecture 73 Serving Machine Learning Models As API

Lecture 74 Adding Validations To Parameters

Lecture 75 Building 3 Machine Learning Products at Once with Hug Framework

Lecture 76 Building A Simple API,CLI and Package with Hug Framework

Section 6: Deploying Our ML Apps

Lecture 77 How to Deploy Streamlit Apps to Heroku

Lecture 78 Updating an Already Deployed App

Lecture 79 How to Deploy Streamlit Apps to AWS EC2

Lecture 80 How to Deploy Streamlit Apps with Docker

Lecture 81 How to Deploy Streamlit Apps on Google Cloud Platform (App Engine)

Lecture 82 Updating and Deleting A Streamlit App on GCP

Lecture 83 How to Deploy Streamlit OpenCV app – Face Detection App on Heroku

Lecture 84 How to Run Streamlit Apps From Google Colab

Lecture 85 How to Deploy Streamlit Apps with Streamlit Sharing

Lecture 86 Adding Streamlit Sharing Badge to GitHub

Lecture 87 Managing the Deployed App on Streamlit Sharing

Lecture 88 Deploying NLP Flask Apps with Hashicorp’s WayPoint

Lecture 89 Load Testing FastAPI NLP Apps

Lecture 90 Saving and Serving ML Models with MLEM

Section 7: Bonus Section – Data Science Project From Scratch

Lecture 91 Data Science Project 1 – Hepatitis Mortality Prediction

Lecture 92 Data Science Project 1 – Hepatitis Predictor ML App with Streamlit

Lecture 93 Data Science Project 1 – Hepatitis Predictor ML App with Flask

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

Course Information:

Udemy | English | 25h 47m | 13.46 GB
Created by: Jesse E. Agbe

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