Building Machine Learning Web Apps with Python

Going Beyond Machine Learning Models

4.70 (194 reviews)
Udemy
platform
English
language
Other
category
instructor
Building Machine Learning Web Apps with Python
2,060
students
26 hours
content
Dec 2022
last update
$54.99
regular price

What you will 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

Why take this course?

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:

  1.     how to setup your Data Science and ML work-space locally.

  2.     how to build machine learning models.

  3.     how to interpret ML models with Eli5.

  4.     how to serialize and save ML models.

  5.     how to build ML web apps using the models we have created.

  6.     how to build packages from your ML Models.

  7.     how to deploy your products.

    etc

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



Reviews

Jagdeesh
November 2, 2022
The course includes thorough explanations and examples of how to apply them to a variety of contexts, from workspace setup through deployment and packaging. Some of the videos' audio levels were incredibly low, making it occasionally difficult to comprehend the language.
Asteway
June 27, 2022
Excellent detailed instructions. I loved how he was coding in a live format to show what type of errors happen and how to resolve them. Most teachers have a perfect code and just talk about that which is good. However, when you code on your own, you will run into problems and will have no clue how to resolve them. He also shows what libraries you need to download.
Mark
June 8, 2022
A nice course, very helpful. I think there could be more details on the actual building of the models, and more attention to details around the presentation of the course. For example, video 70 seems to just stop mid sentence, then the course moves onto video 71.
Sho
January 31, 2022
Talks about a lot of topics I usually don’t see on any other course. I like how he lays it out in a way where the end product is known and breaks it down to understandable pieces.
Joseph
April 8, 2021
Some pretty decent coverage here of all the things you'll need to deploy you ML projects. Nice and concise in a landscape where there are few courses about ML deployment.
Riadh
March 8, 2021
Hello, Very very very interesting course, it is a mine of informations. It is just necessary to improve the organization of the intelligent course so that the readers can easily accept it. For me it is a very good course and I highly recommend Thanks jesse
Thomas
October 8, 2020
Great course, great explanations. This is the best course on udemy to tie in knowledge of flask, streamlit and machine learning to deploy apps. It is not for those who don't know machine learning concepts. If you know HTML or Flask it would definitely be a bonus for understanding. Nevertheless, any gaps in knowledge can be easily understood with a bit of googling
Dhinesh
October 4, 2020
Awesome !!! Such an excellent content from author. Great Work. I love the way you have built and explained the concept. Developing ML products using Web is always fun. Love to listen to your courses more.
Hrishikesh
September 25, 2020
Explanation of each and every small thing is fabulous, along with that course content and examples are really going to help me. Thanks, Jesse E. Agbe Sir for providing such a great knowledgable course, will look forward to such courses from your side. Thank you so much.....!
Shubham
August 23, 2020
dude you really need to break your lectures into smaller lectures. For ex. your stramlit eda shuold be in 10 different videos each covering baby steps. Atleast first project should be like that. Course content is good but really hard to follow.
Liberi
August 18, 2020
Great course and content. In my opinion is an intermediate level course and you should have a fundamental knowledge of data science principles, flask, api's, docker and deployment. I thought the approach was good. Need a better mic and the explanations need to be just a bit more clear. But, I recommended if you want to learn how to deploy your ml models. (Great work!)
Bhakin
August 13, 2020
one of the best course in Udemy on how to deploy machine learning, covering necessary information than many others. However, the voice is quite not loud or clear enough, perhaps because I am not the native English speaker, so I could not hear it clearly.
Rosario
March 31, 2020
Very useful course. Clear explanations and interesting examples. Most of the best tools covered in one course in a quite easy way. Thanks to Jesse!

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2677336
udemy ID
11/28/2019
course created date
2/9/2020
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