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Chapter 1: Data Science Concepts--Machine Learning-Supervised learning
Chapter 2: Data Science Concepts--Machine Learning-Unsupervised learning
Chapter 3: Supervised Learning-Structured Data-Linear regression
Chapter 5: Supervised Learning-Structured Data-Multi-class classification
Chapter 7: Supervised Learning-Unstructured Data-Image classification
Chapter 8: Supervised Learning-Unstructured Data-Segmentation
Chapter 10: Unsupervised Learning-Association models
Chapter 12: Data visualization and exploration
Chapter 15: Model deployment
Chapter 16: Model monitoring and evaluation (e.g., model explainability, precision, recall, accuracy, confusion matrix)
Chapter 18: Normal versus skewed distributions (e.g., mean, outliers)
Chapter 24: Use Snowpark for Python and SQL-Joins
Chapter 27: Use Snowpark for Python and SQL-Remove irrelevant fields
Chapter 28: Use Snowpark for Python and SQL-Handle missing values
Chapter 31: Snowpark and SQL-Identify initial patterns (i.e., data profiling)
Chapter 32: Snowpark and SQL-Connect external machine learning platforms and/or notebooks (e.g., Jupyter)
Chapter 33: Use Snowflake native statistical functions to analyze and calculate descriptive data statistics.-Window Functions
Chapter 34: Use Snowflake native statistical functions to analyze and calculate descriptive data statistics.-MIN/MAX/AVG/STDEV
Chapter 35: Use Snowflake native statistical functions to analyze and calculate descriptive data statistics.-VARIANCE
Chapter 36: Use Snowflake native statistical functions to analyze and calculate descriptive data statistics.-TOPn
Chapter 37: Use Snowflake native statistical functions to analyze and calculate descriptive data statistics.-Approximation/High Performing function
Chapter 38: Linear Regression-Find the slope and intercept
Chapter 39: Linear Regression-Verify the dependencies on dependent and independent variables
Chapter 40: Preprocessing-Scaling data
Chapter 43: Data Transformations-Data Frames (i.e, pandas, Snowpark, Snowpark pandas)
Chapter 44: Data Transformations-Derived features (e.g., average spend)
Chapter 45: Binarizing data-Binning continuous data into intervals
Chapter 46: Binarizing data-Label encoding
Chapter 47: Binarizing data-One hot encoding
Chapter 54: Connecting Python to Snowflake-Snowpark ML
Chapter 55: Connecting Python to Snowflake-Python connector with Pandas support
Chapter 56: Connecting Python to Snowflake-Spark connector
Chapter 57: Connecting from external IDE (e.g., Visual Studio Code)
Chapter 62: Snowflake Cortex-Task-specific models (e.g., categorization, summarization, sentiment analysis, information extraction)
Chapter 63: Build a data science pipeline-Automation of data transformation (e.g., dynamic tables)
Chapter 64: Build a data science pipeline-Python User-Defined Functions (UDFs)
Chapter 65: Build a data science pipeline-Python User-Defined Table Functions (UDTFs)
Chapter 67: Optimization metric selection (e.g., log loss, AUC, RMSE)
Chapter 69: Partitioning-Train validation hold-out
Chapter 71: Training with Python stored procedures
Chapter 72: Training outside Snowflake through external functions
Chapter 73: Training with Python User-Defined Table Functions (UDTFs)
Chapter 74: ROC curve/confusion matrix-Calculate the expected payout of the model
Chapter 76: Residuals plot-Interpret graphics with context
Chapter 79: Partial dependence plots
Chapter 81: Use an external hosted model-External functions
Chapter 83: Deploy a model in Snowflake-Vectorized/Scalar Python User-Defined Functions (UDFs)
Chapter 86: Deploy a model in Snowflake-Stage commands
Chapter 87: Deploy a model in Snowflake-Snowflake Model Registry-Model logging and retrieving
Chapter 88: Deploy a model in Snowflake-Snowflake Model Registry-Snowpark Container Services
Chapter 89: Do the same data points give the same predictions once a model is deployed?)
Chapter 90: Area under the curve
Chapter 91: Accuracy, precision, recall
Chapter 92: RMSE (regression)
Chapter 93: User-Defined Functions (UDFs)
Chapter 95: Model versioning with (Snowflake Model Registry)
Chapter 96: Automation of model retraining

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