We will help you easily understand the core concepts of machine learning and acquire the ability to implement practical machine learning applications by moving away from theory-based machine learning courses.
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Introduction video
00:44
Introduction to the revised lecture
07:49
Concept of machine learning
07:49
Types of machine learning and the importance of data
11:45
Features, advantages, and components of Python-based machine learning
10:57
Installation of SW for Python-based machine learning
10:34
Perform scikit-learn upgrade and install XGBoost and LightGBM
12:32
How to use Jupyter Notebook and the need for NumPy/Pandas
19:45
Download example source code to be used in the lecture
02:42
Introduction to numpy array ndarray
10:58
Practice creating numpy array ndarray, shape, dimension, type
08:04
Understanding the axis of numpy ndarray
07:17
Understanding how to initialize numpy array ndarray and reshape() that changes ndarray dimensions and size - 01
07:59
Understanding how to initialize numpy array ndarray and reshape() that changes ndarray dimensions and size - 02
07:55
Understanding NumPy ndarray Indexing - Single Value and Slicing Indexing
11:21
Understanding NumPy ndarray Indexing - Fancy and Boolean Indexing
08:18
Numpy ndarray indexing practice - 01
11:05
Numpy ndarray indexing practice - 02
07:35
Sort and argsort of numpy ndarray
14:06
Inner product and transpose of numpy ndarray
05:09
Pandas Overview and Basic API - 01
12:22
Pandas Overview and Basic API - 02
15:36
Introducing the Pandas value_counts method
06:03
Transform Pandas DataFrame, create/modify column sets
12:22
Deleting data from a Pandas DataFrame
10:57
Understanding the Pandas Index Object
16:25
Pandas Data Indexing and Filtering - 01
08:29
Pandas Data Indexing and Filtering - 02
15:06
Sorting Pandas DataFrame and performing Aggregation function and Group by
17:30
Applying Named Aggregation when Grouping DataFrame
06:56
Handling missing data in Pandas
05:17
Utilization of pandas nunique and replace
04:28
Processing data using pandas lambda expressions
09:27
Understanding Python-based Machine Learning and Ecosystem Summary
04:04
Introduction to scikit-learn and overview of machine learning classification prediction models
11:00
Introduction to classification performance evaluation index and accuracy
13:45
Introduction to Classification and Decision Trees
12:22
Classification Exercise 2: Credit Card Fraud Prediction Exercise - 01
08:56
Understanding Regression
13:15
Predicting Boston House Prices Using Scikit-Learn LinearRegression
19:29
Clustering Overview and Understanding K-Means Clustering
11:20
Overview of Text Analysis
15:04
Understanding Document Similarity Overview and Cosine Similarity
10:05
Understanding Recommender Systems
11:58
An overview of collaborative filtering and understanding nearest neighbor collaborative filtering
15:24
Learn NumPy, Pandas, and Scikit-Learn, the basic packages that make up Python machine learning
Implement the core concepts of machine learning directly with Python code
In-depth explanations of key machine learning algorithms such as classification, regression, dimensionality reduction, and clustering
Learn various practical examples to the point where you can directly apply machine learning applications in practice
Optimal machine learning model composition method including data preprocessing, application of machine learning algorithms, hyperparameter tuning, and performance evaluation
Detailed explanation and usage of the latest machine learning techniques such as XGBoost, LightGBM, and stacking. Learn how to develop practical machine learning applications by following difficult Kaggle problems.
Learn how to develop practical machine learning applications by following difficult Kaggle problems (Santander Bank customer satisfaction prediction, credit card fraud detection, advanced regression techniques for real estate price prediction, Mercari shopping mall price prediction, etc.)
Learn the basic theories and various practical examples for text analysis and NLP (text classification, sentiment analysis, topic modeling, document clustering, document similarity, Naver movie sentiment analysis using KoNLPy, etc.)
You can build various recommendation systems directly with Python code and learn how to use Surprise, a Python recommendation package.
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.
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:
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 right for you.
The source code used in the lecture can be downloaded from https://github.com/chulminkw/PerfectGuide .
People met by Inflearn
Read the interview with Kwon Chul-min | Go to
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Who is this course right for?
Anyone interested in machine learning
Those who have not been able to overcome the difficult algorithms of machine learning
Those who have been stuck on the surface with theory-based machine learning
Those who have been thinking about how to apply machine learning to practical work
Anyone who wants to try out data analysis/machine learning contests like Kaggle
If you want to upgrade your machine learning skills to the next level
Need to know before starting?
Experience using the Python language
Thin foundation knowledge of machine learning
24,660
Students
1,121
Reviews
3,907
Answers
4.9
Rating
13
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All
192 lectures ∙ (37hr 38min)
All
388 reviews
4.9
388 reviews
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Reviews 1
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Average Rating 5.0
5
I have taken the lectures up to Chapter 7 Clustering. It took about 3 weeks to study after work. I purchased both the lectures and the textbook. First of all, it was great to be able to systematically review machine learning. The explanations are also clear. When I read the book, I could really feel how much effort was put into writing it. Above all, I liked that the author answered questions in a very detailed and friendly manner. However, it is not suitable for those who do not know anything about machine learning. Those who know basic machine learning should take the course. I look forward to other advanced lectures by the author, such as the Kaggle Advanced course! Thank you.
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Reviews 2
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Average Rating 5.0
5
I started watching it over the weekend, and the explanations are really detailed. I think this is a necessary lecture for people who want to use it in the field even if they don't know much about statistics... From the perspective of a dropout...
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Reviews 4
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Average Rating 5.0
5
I am a student who is currently pursuing a master's degree in machine learning abroad. I have theoretical knowledge and have worked on several projects, but since I learned so quickly, I was not organized, so my code was a mess, and my category work in my head was a mess. However, while taking this course, I am organizing a lot and accumulating code. Of course, if you really want to learn the theoretical part, you should look for another course, but if you want to learn through actual code, I strongly recommend it. It is really fun every day. I am having fun taking the course while implementing the parts that were organized in my head as formulas. In summary, 1. This is not a course for beginners, but I recommend it to those who have some sense of machine learning but are not organized. 2. I highly recommend it if you do not need complicated formulas and want to learn coding. 3. I recommend it to those who know formulas and theories through school classes or studying, but are struggling because they do not know how to apply them in practice. Those who do not recommend it: 1. Want to see hardcore mathematical proofs. I do not recommend it. I recommend Murphy's Machine Learning or Bishop's book. 2. I really don't know anything, but I want to start with this. It seems a little difficult.
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Reviews 2
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Average Rating 5.0
5
The Complete Guide to Python Machine Learning came out as a video lecture, so I bought it right away. I think it is the best machine learning book written by a domestic author. The book was very helpful because it explained things in detail, but the video lectures are even more detailed, not only in explanations but also in code explanations. Thank you for the great lecture~ If you film another lecture, I would like to listen to that too! (Personally, I would like to hear more about SQL.)
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Reviews 6
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Average Rating 5.0
5
It's the best in the universe. It's very helpful with detailed explanations and lots of practice code. If I had known it earlier, I wouldn't have wasted time and money on offline lectures.
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