Practical Machine Learning for Beginners in 2022
Model Building to Deployment
Note: 4.2/5 (14 notes) 4,052 students
Instructor(s): Olanrewaju Oyinbooke
Last update: 2022-02-01
What you’ll learn
- Understand how to take a model from notebook to deployment
- Ability to use Flask framework for machine learning model deployment
- Ability to Use Postman application to test your model API endpoints
- Understand How to leverage on Datasist ibrary for faster model building and deployment
- Introductory knowledge to Python suffices
This course is for every beginner in the data science space. We have been there before and we understood what your learning challenges are. This short course will focus on showing you end to end what it takes to build and deploy a simple machine learning solution.
We will be building a car pricing prediction engine. This will be purely hands-on.
You will be able to deploy this solution using the flask framework as an API and also as a Platform.
We will also introduce you to libraries that make it easy to quickly explore, build, and deploy a machine learning solution.
This course assumes you do not have any prior knowledge of Machine Learning or that you have taken a couple of courses but still missing the full picture.
Machine learning models are not useful in silos and this free course will show you the full picture and cover the knowledge gaps.
You will also be able to put to use your knowledge of HTML as we build a simple web interface to interact with the solution.
This course will also introduce you to Postman Application. It is a popular use for testing API and solutions. Postman will help us to interact with the API deployment of our model while our browser is sufficient for interacting with the platform deployment.
We will be making use of the following major applications:
1. Jupyter Notebook
2. Visual Studio Code
Who this course is for
- Data Science Enthusiast
- Beginner Python Developers curious about Data Science
- Data Analyst willing to move to the predictive space
- Business People curious about model building and deployment
- Installing the Required Applications
- Installing Applications
- Build and Deploy a Machine Learning Model
- Import Data to MS SQL Database and Connect Using Python
- Exploratory Data Analysis using Datasist Library
- Feature Engineering
- Model Building by Training an Algorithm
- Model Deployment Using Streamlit
- Model Deployment Using FastAPI
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