[Revised Edition] The Complete Guide to Python Machine Learning
We will break away from theory-oriented machine learning courses and provide an easy-to-understand understanding of the core concepts of machine learning while equipping you with the ability to implement practical machine learning applications.
This course is prepared for Basic Learners.
What you will learn!
Learn NumPy, Pandas, and Scikit-learn, the basic packages that make up Python machine learning.
Implement the core concepts of machine learning directly in Python code
In-depth explanation of core machine learning algorithms such as classification, regression, dimensionality reduction, and clustering.
Learn a variety of practical examples to reach a level where you can directly apply machine learning applications in practice.
Optimal machine learning model configuration method including data preprocessing, machine learning algorithm application, hyperparameter tuning, and performance evaluation
Detailed explanations and usage of the latest machine learning techniques such as XGBoost, LightGBM, and stacking. Acquire practical machine learning application development methods by solving difficult Kaggle problems.
Learn how to develop practical machine learning applications by solving difficult Kaggle problems (e.g. Santander Bank customer satisfaction prediction, credit card fraud detection, advanced regression techniques for real estate price prediction, Mercari shopping mall price prediction, etc.)
Learn basic theories and various practical examples for text analysis and NLP (text classification, sentiment analysis, topic modeling, document clustering, document similarity, sentiment analysis of Naver movies using KoNLPy, etc.)
You can build various recommendation systems directly with Python code, and we show how to use the Python recommendation package Surprise.
It is very popular because of its detailed explanations and abundant examples.
'The Complete Guide to Python Machine Learning'
Now you can meet us through video lectures on Infraon.
Meet the latest revised edition
The Complete Guide to Python Machine Learning
Hello, I am Kwon Chul-min, the author of The Complete Guide to Python Machine Learning.
In April 2022, the second revised edition of the book The Complete Guide to Python Machine Learning was published.
As the book is revised, this 'Complete Guide to Python Machine Learning' course has also been newly produced and released.
The revised lectures that are being released this time have made over 70-80% of the existing lectures new lectures (over 90% of the existing lectures from Section 1 to Section 5 (Regression) are new lectures). The lecture videos have increased from 28 hours to 37 hours, and I will explain the improved and added contents.
We have put a lot of effort into the revised lectures to reflect the revised contents of the book while also making them better than the first edition lectures. Based on the feedback you have sent to the lectures, we have filled them with easier and more detailed explanations.
Introduction to the course
The Python Machine Learning Complete Guide course organizes the core theories with detailed explanations and easy diagrams, and allows you to acquire machine learning by solving various practical problems with machine learning. Rather than a theory-oriented machine learning course, we provide a guide that allows you to apply machine learning to real-world tasks using Python libraries. 
To understand this, we have organized the content so that you can maximize your machine learning capabilities by directly performing the entire process of configuring a machine learning model, from data preprocessing to applying machine learning algorithms and tuning hyperparameters, through examples based on difficult practice data from Kaggle and the UCI Machine Learning Repository, rather than well-refined data.
We also provide a very detailed explanation of the latest algorithms and techniques used in many data science fields on Kaggle , such as XGBoost, LightGBM, and stacking techniques .
In addition to the existing content, the revised edition covers the following additional content:
- Implementation of practical code that upgrades all libraries used in the lecture to the latest version, including the latest scikit-learn version (1.0.2).
- Added Bayesian Optimization hands-on course for optimal hyperparameter tuning of XGBoost or LightGBM models with various types of hyperparameters
- Added a 'Visualization' session that covers in detail how to use matplotlib and seaborn, visualization libraries widely used for data analysis related to machine learning.
🤖 We will guide you to an expert level so you can confidently apply machine learning applications in practice.
Implementing machine learning code is not something that can be simply understood with the head or eyes. You can never become a machine learning expert without implementing it yourself. Through well-defined explanations of core concepts and abundant application and practical examples, we will guide you to the level of an expert who can confidently apply machine learning applications in practice.
In this lecture, we have strengthened the detailed explanation of many parts that were difficult to explain in the book due to space constraints, and in particular, we have put a lot of effort into making it as easy to understand as possible by solving the example codes line by line, step by step .
This is not a beginner's course for those who have no knowledge of machine learning at all, but if you learn the basic concepts of machine learning through introductory books or other video lectures and then take this course, you will be able to upgrade your machine learning skills very quickly. If you visit a large bookstore near you and briefly review the book "The Complete Guide to Python Machine Learning," you will be able to easily determine whether this course is suitable for you.
The source code used in the lecture can be downloaded from https://github.com/chulminkw/PerfectGuide .
People met by Infraon
Read the interview with Kwon Chul-min | Go to
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🧗🏻♂️
Knowing the path and walking the path are two different things. This course will be a great guide to help you reach a level where you can apply machine learning in practice .Recommended for
these people!
Who is this course right for?
Anyone interested in machine learning
Those who couldn't overcome the wall of difficult machine learning algorithms
Those who have only been superficially familiar with machine learning based on theory
Anyone who has been thinking about how to apply machine learning in practice
Anyone who wants to try out data analysis/machine learning contests like Kaggle
Anyone who wants to upgrade their machine learning skills to the next level
Need to know before starting?
Experience using the Python language
Thin foundation knowledge of machine learning
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192 lectures ∙ (37hr 38min)