使用 Python 和 ChatGPT 快速实现机器学习

上次更新时间：2024-11-11

课程售价: 2.9 元

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课程内容

2. What is Machine Learning

3. Master Data Cleaning for Error-free ML Model

1. Load your dataset into Python environment
2. Handling missing values with Scikit-learn
3. Identify and deal with inconsistent data
4. Dealing with miss-identified data types
5. Address and remove duplicated data

4. Master Data Manipulation for Strong ML Model

1. Sorting and arranging dataset
2. Filter data based on conditions
3. Merging or adding of supplementary variables
4. Concatenating or adding of supplementary data

5. Master Data Preprocessing for Perfect ML Model

1. Feature engineering Generating new data
2. Extracting day, months, year from date variable
3. Feature encoding Assigning numeric values
4. Creating dummy variables for nominal data
5. Data standardizing and normalizing with StandardScaler
6. Splitting data into training and testing set

6. Hands-on Machine Learning Application Part 1 Regression

2. Linear regression ML model
3. Decision Tree regression ML model
4. Random Forest regression ML model
5. Support Vector regression ML model
6. XGBoost regression ML model

7. Hands-on Machine Learning Application Part 2 Classification

2. Logistic Regression ML model
3. Decision Tree classification ML model
4. Random Forest classification ML model
5. K Nearest Neighbours classification ML model
6. LightGBM classification ML model

8. Hands-on Machine Learning Application Part 3 Clustering

1. KMeans Clustering ML model

9. Tips, Tricks and Resources

1. ChatGPT Your best code companion

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使用 Python 和 ChatGPT 快速实现机器学习

Fast-Track Machine Learning in Python and ChatGPT

上次更新时间：2024-11-11 21:49

课程售价：2.9元

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课程内容

8个章节 , 29个讲座

2. What is Machine Learning

3. Master Data Cleaning for Error-free ML Model

1. Load your dataset into Python environment
2. Handling missing values with Scikit-learn
3. Identify and deal with inconsistent data
4. Dealing with miss-identified data types
5. Address and remove duplicated data

4. Master Data Manipulation for Strong ML Model

1. Sorting and arranging dataset
2. Filter data based on conditions
3. Merging or adding of supplementary variables
4. Concatenating or adding of supplementary data

5. Master Data Preprocessing for Perfect ML Model

1. Feature engineering Generating new data
2. Extracting day, months, year from date variable
3. Feature encoding Assigning numeric values
4. Creating dummy variables for nominal data
5. Data standardizing and normalizing with StandardScaler
6. Splitting data into training and testing set

6. Hands-on Machine Learning Application Part 1 Regression

2. Linear regression ML model
3. Decision Tree regression ML model
4. Random Forest regression ML model
5. Support Vector regression ML model
6. XGBoost regression ML model

7. Hands-on Machine Learning Application Part 2 Classification

2. Logistic Regression ML model
3. Decision Tree classification ML model
4. Random Forest classification ML model
5. K Nearest Neighbours classification ML model
6. LightGBM classification ML model

8. Hands-on Machine Learning Application Part 3 Clustering

1. KMeans Clustering ML model

9. Tips, Tricks and Resources

1. ChatGPT Your best code companion