
Computer Vision All-in-One: 10 Practical Projects Completed in 12 Hours
nomad
Easy, Usable Computer Vision Projects: Step-by-step learning of practical CV projects. Boost skill with real examples.
Basic
Python, OpenCV, Deep Learning(DL)
This is a comprehensive machine learning project course that learns various useful machine learning regression and classification projects along with theory using Python and TensorFlow 2 based on Boston Marathon big data.
383 learners
Level Intermediate
Course period Unlimited


Reviews from Early Learners
5.0
송치훈
I enjoyed trying it as a sample.
5.0
chrischina
Thanks for the great explanation.
5.0
방자만세
thank you
Build machine learning models and programs
Problem Solving with TensorFlow
Predicting machine learning classification results
Predicting machine learning regression results
Understanding artificial intelligence, machine learning, and deep learning
Data processing for machine learning and deep learning
Data processing and analysis with Python Pandas
Data Analysis Using Python
Learn both the concepts and practical techniques of machine learning using Python and TensorFlow2.
We will hone your skills by working together on five different projects based on core topics.
Boston Marathon Big Data with Python and TensorFlow
Along with the basic concepts of machine learning, the core topics of regression and classification are covered.
Develop concepts and practical application skills while learning five projects together.
This is a fun and useful machine learning project course.
Learn the concept of linear regression, the foundation of machine learning.
Understand multi-variable regression problems and learn how to solve them.
Understand multi-variable, output regression problems and learn how to solve them.
Understand the concept of Binary Classification, the basis of Logistic Regression/Classification, and learn how to solve it.
Understand the concept of Multinomial Classification in Logistic Regression/Classification and learn how to solve it.
'Model accuracy over 99% We have added a special lecture titled 'Raising the bar'. This lecture is titled ' [ Raspberry Pi ] IoT Deep Learning Computer Vision Practice'. The project began with a question from students in the MNIST handwriting model: "Why can't the MNIST handwriting model say '7' is '7'?" While the model's accuracy is a factor, as are the program's exception handling and the raw MNIST data, the existing Nueral Network model was too simple for training purposes, so I reconfigured it to increase its accuracy to 99.38%.
Please look forward to future lectures on deep learning, IoT, and other topics utilizing machine learning.
The materials and program sources used in the lecture can be found on the website Creapple (www.creapple.com), a knowledge learning platform I run.
Taking a course on Python fundamentals and data visualization and analysis will be of great help in carrying out your project.
Machine learning using Python's Pandas, Matplotlib, and Seaborn,
It can be used in various projects such as deep learning.
Learn data visualization and analysis techniques all at once.
Who is this course right for?
Those who want to use artificial intelligence in practice
Those who want to develop basic knowledge for deep learning
Anyone who wants to learn data science
For those who want to use TensorFlow directly
Those who want to develop machine learning concepts and practical skills at the same time
Anyone working on a data analysis project
For those preparing for machine learning/deep learning projects
Need to know before starting?
Python Data Processing, Visualization - Python Data Visualization Analysis Practical Project
Python Basics - Python 100-minute core lecture
Willingness to study hard
22,412
Learners
509
Reviews
556
Answers
4.4
Rating
25
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42 lectures ∙ (8hr 51min)
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25 reviews
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2
I thought it was a prerequisite for the course called "Keras, Complete Conquest of Artificial Intelligence", and I was curious which versions of python, keras, and tensorflow are being used in the course, and how to use those versions, but there is no explanation at all. If you had just told me to install tensorflow by saying "pip install tensorflow", I would never have been able to implement the code in this course even after about a year. It is unfortunate that I am not able to follow along when you do not tell me which version of tensorflow you used, and especially which version of python you used.
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Average Rating 4.4
3
If you don't take the prerequisite course, it's very difficult to understand. I applied for the Keras course and there were many parts that I had to take before to understand the previous course, so I applied for the Complete Machine Learning course.... But I also had to take the previous course, so the efficiency of my study was greatly reduced. I think that if taking the prerequisite course becomes mandatory rather than optional, it will be a course that accurately accompanies the output.
Hello. Thank you for your good opinion. The part you mentioned is something that I worry about a lot as an instructor. In the past, when I made an All in One-style lecture, there were opinions that the readers' levels were different and they knew a lot of the content, and if I modularized the lecture, they would only take the parts they wanted and reduce the cost for the students, so I modularized the lecture. So in the future, I plan to modularize deep learning, Tensorflow.js, Tensorflow IOT, etc. into separate lectures. However, as a result, I am worried about the inconvenience of having to take multiple subjects as you mentioned. So I am currently creating a function that allows you to use all the lectures by subscribing to my Creapple (www.creapple.com). It is scheduled to be reorganized and opened this month, so I hope it will be helpful. Thank you.
Nomad Creator Instructor Thank you for your answer. As you said, if it is in the form of a regular subscription, I think it will lead to better output. And the individual contents of the instructor's lecture are really helpful and motivating. However, I feel like I have to watch the back of Avengers without watching the front part.
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