ChatGPT for Data Science and Machine Learning
This course covers an exciting journey in the application of ChatGPT in the field of Data Science and Machine Learning. Throughout this program, you will explore ChatGPT’s ability as a valuable tool in data analysis, preprocessing, and machine learning model building without needing to write a single line of code!
In the first part, we will dive into fundamental data analysis techniques. You will learn how to extract crucial statistical information from your datasets, handle missing values, and identify and treat outliers. We will explore the relationships between variables and the visual representation of categorical and numerical data. In the second part, we will delve deeper into the field of machine learning, and you will learn how to handle categorical attributes using techniques like LabelEncoder and OneHotEncoding. We will address the challenge of imbalanced datasets and discuss the importance of feature scaling. Cross-validation, parameter tuning, and feature selection are essential parts of the modeling process, and you will have the opportunity to enhance your skills in these areas.
Upon completing this course, you will be equipped with advanced skills in data science and machine learning, empowered to effectively apply ChatGPT in real-world projects. This program offers a unique opportunity to enhance your analytical skills and stand out in the field of data science and machine learning. Get ready to reach a new level in your professional career!
Data analysis and exploration
Machine learning
Final remarks
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15
Categorical features - LabelEncoder -
16
Unbalanced data
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17
Categorical features - OneHotEncoding
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18
Feature scaling
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19
Train and test data
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20
Algorithms and evaluation
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21
Cross validation 1
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22
Cross validation 2
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23
Hyperparameter tuning
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24
Final model
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25
Feature selection
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26
HOMEWORK - regression -
27
Solution 1
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28
Solution 2
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29
Solution 3
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30
Solution 4
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31
Solution 5
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32
Twitter dataset 1
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33
Twitter dataset 2
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34
Pre-processing texts
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35
Sentiment analysis
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