Class introduction

Python machine learning, why do we need it?

Machine learning (machine learning)What is Iran?

Machine learning is a field of artificial intelligence and is a computer algorithm that autonomously learns and improves data. By automating model creation for data analysis, software learns by itself based on data, finds patterns, and improves performance.

✅ Here are some of the amazing things machine learning can do.

• Activate voice-based assistants such as Siri
• Refer friends and groups on social media channels.
• GPS predicts which route will have a lot of traffic and re-routes it.
• Search engines increasingly clarify and optimize search results.
• Streaming services such as Netflix and YouTube automatically introduce content that users might like.

Machine learning is currently having a great impact on our lives, to the extent that it can be said that it is the technology most closely related to the daily lives of modern people! In this class, you'll learn about the overall process of machine learning. If you want to improve work efficiency, automate analysis, and improve your predictive power, take a machine learning class right now!

Machine learning using Python! Start studying right now!

Why a data campus machine learning class?

This course is taught by data analysis experts with more than 20 years of experience based on various experiences and proven expertise. With a curriculum that completes step-by-step processes and masters algorithms, it is possible to master 4 machine learning goals, including prediction, classification, clustering, and related recommendations, and 13 proven core algorithms with high predictive power, and covers the entire process from data normalization, data set segmentation, application of algorithms, and hyperparameter tuning, and application to actual data with practical topics such as election voter prediction and patient prediction.

Don't give up with this class and learn Python deep learning until the end!

Course effect

• Analysis pre-processing procedures such as normalization of collected data, transformation, and partitioning of train/test datasets can be performed.
• You can use 13 core algorithms for the four purposes of machine learning, including prediction, classification, clustering, and recommendation.
• By learning how to tune hyperparameters, you can derive optimal results with the highest predictability.
• By applying ensemble techniques, it is possible to derive higher predictive power by combining multiple machine learning algorithms.
• Improve work efficiency, or automate analysis and improve predictability.

Class recommendation target

• Those who have mastered the basic Python course to some extent
• Those who want to clearly understand the preprocessing process and algorithms
• Business practitioners and job-seekers who want to become artificial intelligence data analysts
• A developer who wants to automate analysis such as predictions, recommendations, and clusters
• Those who want to improve the efficiency and predictability of repetitive tasks by learning machine learning
• Researchers who want to achieve research results using the latest machine learning techniques in addition to traditional statistical analysis

2 reasons why this class is special

① Machine learning courses taught by data analysis experts over 20 years

② Lectures to learn 13 core algorithms of machine learning