Review of Python Courses (Part 31)

In 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)

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

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)

I will review more courses next time.