List of topics it is best to know for practical Machine Learning approach… Data mining framework: Tools: Visualisation Libraries: Evaluation metric: Model Hubs/Zoo: Hyperparameter Tuning: Outlier Detection: Resampling: EDA: Cross Validation: Feature Engineering: Feature selection: Ensemble Methods: Dimensionality Reduction: Models:

1. CRISP-DM
2. SEMMA

1. Anaconda/miniconda
2. Streamlit
3. Jupyter Notebooks
4. MLflow
5. Weights & Biases
6. comet.ml

1. Matplotlib
2. Seaborn
3. Plotly
4. PivotTable.js

-Accuracy
– Precision (P)
– Recall (R)
– F1 rating (F1)
– Area under the ROC (Receiver Operating Characteristic) curve
– Log loss
– Precision at k (P@k)
– Average precision at k (AP@k)
– Mean average precision at k (MAP@k)

– Mean absolute error (MAE)
– Mean squared error (MSE)
– Root mean squared error (RMSE)
– Root mean squared logarithmic error (RMSLE)
– Mean percentage error (MPE)
– Mean absolute percentage error (MAPE)
– R2

1. Hugging Face models
2. TensorFlow hub
3. PyTorch hub

1. Grid search
2. Random search
3. Coarse-to-fine search
4. Bayesian
   optimisation with Gaussian process.
5. Skopt
6. Hyperopt
7. W&B Hyper-parameter Sweep
8. Ray Tune
9. Optuna

1. Z-Rating
2. IQR method
3. Percentile method
4. Residual vs Leverage plot

1. Random under sampler
2. Random over sampler
3. SMOTE

1. Pandas-Profiling
2. Sweetviz
3. Autoviz
4. D-Tale

1. k-fold cross-validation
2. stratified k-fold cross-validation
3. hold-out based validation
4. leave-one-out cross-validation
5. group k-fold cross-validation

1. Pandas date-time feature
2. Aggregates in pandas
3. tsfresh library (for time series data)
4. Polynomial features(Sk-learn)
5. Binning (using panda’s cut function)
6. Transformations(log,reciprocal,power,box-cox,etc.)

1. Variance threshold & Variance inflation factor(VIF)
2. Feature coefficient & feature importance
3. Pearson correlation
4. chi-square
5. F-test
6. Mutual information
7. Greedy feature selection
8. RFE(recursive feature elimination)
9. SelectFromModel(from Sklearn)

2.

• Vote classification
• Mixing /Stacking

1. PCA(incremental,kernel,randomise)
2. LLE(Locally Linear Embedding)
3. Multidimensional Scaling (MDS)
4. t-Distributed Stochastic Neighbor Embedding (t-SNE)
5. Isomap
6. Singular Value Decomposition (SVD)

• OLS regression
• Polynomial regression
• Generalized Linear Model(GLM)
• curvefit(non-linear regression)
• Lasso & Ridge regression
• ElasticNet regression
• Logistic regression
• Lazy classifier
• Softmax Regression
• SVM
• KNN
• Random forest
• Adaboost
• XGboost

• K-means
• Agglomerative clustering
• Spectral clustering
• Mean shift
• Density-based Spatial Clustering of Applications with Noise
  (DBSCAN)
• K-medoids

• Kernel density estimation (KDE) &Bandwidth selection criteria
• Anomaly detection with Isolation Forests

And that’s a wrap! 📄

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