Review of Python Courses (Part 31)
Posted by Mark on March 1, 2021 at 07:37 | Last modified: February 18, 2021 13:43In Part 30, I summarized my Datacamp courses 89-91. Today I will continue with the next three.
As a reminder, I introduced you to my recent work learning Python here.
My course #92 was Market Basket Analysis in Python. This course covers:
- What is market basket analysis (using lambda function for string processing)?
- Identifying association rules
- The simplest metric (from mlxtend.preprocessing import TransactionEncoder)
- Confidence and lift
- Leverage and conviction
- Association and dissociation
- Advanced rules
- Aggregation
- The Apriori algorithm (from mlxtend.frequent_patterns import apriori)
- Basic Apriori results: pruning (from mlxtend.frequent_patterns import association_rules)
- Advanced Apriori results: pruning
- Heatmaps
- Scatterplots
- Parallel coordinates plot (from pandas.plotting import parallel_coordinates)
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My course #93 was Winning a Kaggle Competition in Python. Topics covered in this course include:
- Competitions overview
- Prepare your first submission (from sklearn.linear_model import LinearRegression)
- Public vs. private leaderboard
- Understand the problem (from sklearn.metrics import roc_auc_score, f1_score, mean_squared_error, log_loss)
- Initial EDA (create/extract hour feature)
- Local validation (from sklearn.model_selection import KFold, StratifiedKFold)
- Validation usage (from sklearn.model_selection import TimeSeriesSplit)
- Feature engineering
- Categorical features (from sklearn.preprocessing import LabelEncoder)
- Target encoding
- Missing data (from sklearn.impute import SimpleImputer)
- Baseline model (import train_test_split; from sklearn.ensemble import GradientBoostingRegressor)
- Hyperparameter tuning (from sklearn.linear_model import Ridge)
- Model ensembling
- Final tips
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My course #94 was Machine Learning for Time Series in Python. This course covers:
- Kinds of time series and applications
- Machine learning basics [from sklearn.svc import LinearSVC, .reshape()]
- Combining time series data with machine learning (from glob import glob; import librosa as lr)
- Classification and feature engineering (axis = -1)
- Improving the features we use for classification (from sklearn.model_selection import cross_val_score)
- The spectrogram—spectral changes to sound over time (from librosa.core import stft, amplitude_to_db)
- Predicting data over time (from sklearn.metrics import r2_score)
- Cleaning and improving your data (percent_change function, visualizing outlier thresholds)
- Creating features over time (from functools import partial)
- Time-delayed features and auto-regressive models
- Cross-validating time series data (from sklearn.model_selection import ShuffleSplit, TimeSeriesSplit)
- Stationarity and stability (from sklearn.utils import resample)
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I will review more courses next time.
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