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Introduction
Lesson 1: What is Machine Learning?
Learning objectives
1.1 Install
1.2 Understand the ML Libraries (new lesson, title TBD)
1.3 Describe the techniques used in machine learning
1.4 Understand the difference between "deep learning" and other ML techniques
1.5 Understand classification versus regression versus.clustering and over/underfitting
1.6 Perform dimensionality reduction, explain feature engineering, and utilize feature selection
1.7 Distinguish categorical versus ordinal versus continuous variables
1.8 Perform one-hot encoding
1.9 Utilize hyperparameters and grid search
1.10 Understand choose and metrics
Lesson 2: Exploring a Data Set
Learning objectives
2.1 Uncover anomalies and data integrity problems
2.2 Clean and massage your data
2.3 Choose features and a target
2.4 Implement a train/test split and choose model
Lesson 3: Classification
Learning objectives
3.1 Understand feature importances
3.2 Establish cut points in a decision tree
3.3 Utilize a common API
3.4 Use a more encouraging dataset
3.5 Compare multiple classifiers
3.6 Understand more about feature importances
3.7 Use multiclass classification
3.8 Understand prediction probabilities and decision boundaries
Lesson 4: Regression
Learning objectives
4.1 Sample data sets in scikit-learn
4.2 Compare a gaggle of regressors
4.3 Use linear models
4.4 Understand the pitfalls of linear models
4.5 Use non-linear regressors
Lesson 5: Clustering
Learning objectives
5.1 Compare clustering algorithms
5.2 Cluster to test a hypothesis
5.3 Cluster into N classes
5.4 Cluster into an unknown number of categories
5.5 Use density based clustering: DBScan and HDBScan
5.6 Evaluate clustering
Lesson 6: Hyperparameters
Learning objectives
6.1 Explore one hyperparameter
6.2 Explore many hyperparameters
6.3 Use GridsearchCV
Lesson 7: Feature Engineering and Feature Selection
Learning objectives
7.1 Understand a synthetic example
7.2 Understand dimensionality reduction
7.3 Use principal component analysis (PCA)
7.4 Use other decompositions: NMF, LDA, ICA, t-dist
7.5 Implement feature selection: Univariate
7.6 Implement feature selection: Model-based
7.7 Understand dimensionality expansion (polynomial features)
7.8 Use one-hot encoding
7.9 Scale with StandardScaler, RobustScaler, MinMaxScaler, Normalizer, and others
7.10 Bin values with quantiles or binarize
Lesson 8: Pipelines
Learning objectives
8.1 Understand imperative sequential processing
8.2 Use pipelines
8.3 Do pipelines with grid search
Lesson 9: Robust Train/Test Splits
Learning objectives
9.1 Understand splitting
9.2 Understand multiple splitting: KFold, LeaveOneOut, StratifiedKFold, etc
9.3 Use cross validation
Summary