使用 Python 和 ChatGPT 快速实现机器学习
Fast-Track Machine Learning in Python and ChatGPT
- 2. What is Machine Learning
- 1. Machine Learning and Its Characteristics
- 2. Complete Machine Learning Work-flow
- 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