Jesse E. Agbe
About the Instructor
Jesse is a developer,a researcher,a MD and an avid reader. He is a student of optimization,productivity,programming languages,multiplication and biblical doctrine.
He is a student of ideas,beliefs and philosophies with a desire to see improvement and development in the lives of everyone.
His driving goal is to help people to optimize standard technologies in solving certain problems as well as to grow their faith.
The contributions he aims at making are in certain areas - such as optimization,vision , health, doctrine, efficiency, technology and future of humanity.
His focus is to know why things are the way they are and how to make the most of them.
Jesse loves questions and he is passionate about why things are the way they are and how to be efficient.
He is a student,an author and a simple programmer.
Building Machine Learning Web Apps
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.
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 learn
- how to setup your Data Science and ML workspace locally
- how to build machine learning models
- how to interpret them
- 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
Join us as we explore the world of building ML Web Apps and Products in python.
See you in the Course,Stay blessed.
This is an ongoing course which will be periodically updated
- Introduction and Course Outline FREE PREVIEW
- Objectives and Where to Find Help FREE PREVIEW
- Types of Machine Learning Apps FREE PREVIEW
- Four Ways To Productionize Machine Learning Models FREE PREVIEW
- How To Set Up Your Workspace -Intro FREE PREVIEW
- Creating a Virtual Environment with Pipenv
- Switching Between Virtual Environment with Pipes
- Python Package and Dependency Management with Poetry
- Where to Get Datasets For Practice.
- List of Datasets(Where To Find Datasets)
- Packages Used In this Course
- 01-Building ML Models - Salary Prediction -Introduction
- 02-Building ML Models-Salary Prediction - Building Our Models with Sklearn
- 03-Building ML Models - Interpreting the Model
- 04-Building ML Models - Bible Passage and Author Prediction
- 05-Building ML Models - How to Save Your ML Models
- 06 - Building ML Models - Gender Classification of Names
- 07 - Building ML Models - Predicting Customer Churning
- 08 - Building ML Models - Predicting Customer Churning - Interpreting the Model
- Evaluating Car Quality with Machine Learning in Python
- Flask Crash Course - Installation and Basic App
- Flask Crash Course - Rendering HTML
- Flask Crash Course -Working with Jinja(Sending Data From Back-End to Front-End)
- Flask Crash Course - Receiving Input From Front End to BackEnd
- Flask Crash Course - Processing Data At Backend
- Flask Crash Course - Working with Databases using SQLAlchemy
- Flask Crash Course - Retrieving Data From Database
- Flask Crash Course - Searching Database
- Streamlit Crash Course 1
- Streamlit Crash Course 2 - Work Arounds and Plots
- FastAPI Tutorial - Basics
- Hug Framework Tutorial
- Building A Simple Blog App with Streamlit and Python
- Adding a Simple Login & Sign Up Section to Streamlit App
- Updating our Streamlit Blog with Login and Sign Up
- Securing the Login Section Against SQL Injection
- Introduction FREE PREVIEW
- CMC Predictor ML App with Streamlit - Demo
- Setting Up Our Workspace For the CMC- Predictor App
- Building the EDA Section of CMC Predictor ML App
- Building the Prediction Section of CMC_ Predictor ML App
- Building ML Flask Apps-News Classifier-App -Demo FREE PREVIEW
- Building ML Flask App-News Classifier- Setting Up and Basic App
- Building ML Flask Apps - News Classifier - Embedding Our ML Models
- Building ML Flask App - News Classifier - Beautifying The Front-End
- Salary Predictor ML App with Streamlit -Demo
- Setting Up Our Workspace ,Installation and App Structure
- Building The Exploratory Data Analysis Section(EDA) of Our Salary Predictor ML App
- Building The EDA Section of Salary Predictor ML App [Part 2]
- Building The Prediction Section Of Our Salary Predictor ML App
- Building NLP Apps - Sentiment Analysis App with Streamlit
- Building NLP Apps - Summarizer and Entity Checker App with Streamlit and SpaCy
- Building NLP Apps -Document Redactor App with SpaCy and Streamlit
- Predicting Customer Churn ML App with Streamlit -Demo
- Building Customer Churn Prediction ML App
- Building A Drag & Drop Semi-Automated ML App
- Password Strength Classifier ML App
- Car Evaluation ML App with Streamlit
- Face Detection App-Demo
- Building Computer Vision ML App - Face Detection App
- Face Detection App - Files and Code
- Emoji Lookup App -Demo
- Simple Streamlit App with Login and Sign-Up Section
- Search Term Trend App For Programming Languages
- Your Opinion About the Course So Far
- How To Deploy Streamlit Apps to Heroku
- Updating An Already Deployed Streamlit App on Heroku
- How To Deploy Streamlit on AWS Ec2
- How To Deploy Streamlit Apps with Docker
- How To Deploy Streamlit Apps to GCP App Engine
- Deploying with Docker and GCP Documents
- Updating and Deleting A Deployed Streamlit App on GCP
- How to Deploy Streamlit OpenCV Face Detection on Heroku
- Using ML Models as Packages-NewsClassifier-Demo
- NewsClassifier ML Package - Designing the Package
- NewsClassifier ML Package - Loading The ML Models
- NewsClassifier ML Package- Classifying News
- NewsClassifier ML Package - Unit Testing Our Package
- NewsClassifier ML Package - Building Our Package with Setuptools
- NewsClassifier ML Package - Publishing to TestPyPI and PyPI
- NewsClassifier ML Package - Building with Poetry
- Using ML Models as Packages - GenderClassifier-Demo FREE PREVIEW
- GenderClassifier ML Package - Creating Package and the Class Object
- GenderClassifier ML Package - Adding the Prediction Function
- GenderClassifier ML Package - Loading Different Models
- GenderClassifier ML Package - Classifying Names
- GenderClassifier ML Package - Unit Testing Our Package
- GenderClassifier ML Package - Building the Package with Setuptools
- GenderClassifier ML Package - Building Our Package with Poetry
- GenderClassifier ML Package - Publish the Package to PyPI with Poetry
- SpamDetector ML Package -Indepth with Poetry