Lessons
33 lessons are ready.
- Python as an ML Calculator
- Control Flow and Functions for Data Work
- Vectorization and Broadcasting
- Plotting with Matplotlib
- pandas DataFrames
- Cleaning and Grouping
- Statistics with NumPy
- Linear Algebra with NumPy
- ML Capstone: Your First Classifier
- Randomness, Sampling, and Train/Test Index Helpers
- Series, DataFrame, I/O
- Missing Data, Dtypes, and Categorical Prep
- GroupBy, Aggregation, and Feature Construction
- Joins, Concat, and Tidy Reshape
- From DataFrame to Model Matrices
- matplotlib Fundamentals for EDA
- seaborn for Relationships and Categoricals
- Estimator API and the First Baseline
- Model-Diagnostic Plots
- Preprocessing Transformers
- Linear Models for Regression
- Linear Models for Classification
- Trees and Ensembles
- Neighbors, Linear SVM, and Scaling
- Multiclass and Model-Choice Etiquette
- Metrics that Match the Decision
- Cross-validation and Honest Nesting
- Pipelines and ColumnTransformer
- Calibration, Thresholds, and Error Analysis
- PCA for Visualization and Compression
- k-means and Clustering Hygiene
- Capstone Brief and Baseline
- Capstone Hardening and Reflection