Honestly, there are still many formulas and visualization parts that I don't understand,
but I now understand which models would be good to use in certain situations, and how machine learning and deep learning coding is generally done. I also implemented random forest machine learning in my actual work, and although I struggled a lot and it was difficult, it was fun to actually implement it and brought back old memories. I plan to take all the AI-related courses in the instructor's curriculum going forward.

woor0191434

Do you want to take your first step into the world of data? Learn machine learning and deep learning, the core technologies of AI, with Python. This course will guide you step by step, from the basics of machine learning and deep learning to practical applications. Traditional machine learning and deep learning are based on many of the same principles and technical systems. Therefore, this course does not separate the two into separate subjects, but rather organizes them into one connected course so that beginners can increase their understanding of machine learning as a whole.

trimurti

Introduction to Machine Learning and Deep Learning Using Python

Thank you for the good review. I'm especially glad to hear that you've applied it in practice. I hope it was very helpful.

YoungJea Oh

입문

초급

중급이상

Machine Learning(ML)

Deep Learning(DL)

파이썬

Python

넘파이

Numpy

판다스

Pandas

Hands-on Preparation and Machine Learning Overview

Learn ML Basics

Traditional Machine Learning - Supervised Learning Model part 1

Data set splitting and model performance evaluation metrics

Traditional Machine Learning - Supervised Learning Model part 2

Traditional Machine Learning - Unsupervised Learning Model

Neural Network and Deep Learning

CNN (Convolutional Neural Network) - Convolutional Neural Network

RNN (Recurrent Neural Network)

Unsupervised Learning - Autoencoder

GAN(Generative Aniversarial Network) - Generative Adversarial Network

ML Model Server Deployment