Data-Science

• To know more about dummy variables (here)
• Why it’s necessary to create dummy variables (here)
• Missing Values Imputation
• When to Normalize or Standardize the variables?
  • Feature Scaling for Machine Learning: Understanding the Difference Between Normalization vs. Standardization
  • Linear Regression :: Normalization (Vs) Standardization
• Various scaling techniques (here)
• Recursive Feature Elimination(RFE) - scikit-learn (here)
  • Recursive feature elimination is based on the idea to repeatedly construct a model (for example an SVM or a regression model) and choose either the best or worst performing feature (for example based on coefficients), setting the feature aside and then repeating the process with the rest of the features. This process is applied until all features in the dataset are exhausted. Features are then ranked according to when they were eliminated. As such, it is a greedy optimization for finding the best performing subset of features. Read more at this link
• Parametric v/s non parametric models in short and detailed
• Regression guarantees interpolation of data and not extrapolation
  • Interpolation basically means using the model to predict the value of a dependent variable on independent values that lie within the range of data you already have. Extrapolation, on the other hand, means predicting the dependent variable on the independent values that lie outside the range of the data the model was built on.
• Optimization Methods (here)
• Regularization in Machine Learning (here)
• A brief overview of Feature Scaling (here)
• When to standardise, when to normalise (here)
  • When and Why to stardardize a variable
• All about When and How to do train_test_split and pre_processing
  • Things to know before train and test split
  • Data Preparation without data leakage
• Dimensionality Reduction Algorithmns (here)
• Feature Selection (here)
• Naive Bayes Classification explanation (here)
• Factor Analysis
  • Introduction to factor analysis
  • Factor analysis Notes
  • Theory and practice questions on factor analysis\
• Implementing recommendation systems
  • Recommender systems 101 – A step-by-step practical example in R
  • A framework for developing and testing recommendation algorithms
  • Netflix implementation of recommendation engine
• Understanding ROC curve (here)
• Feature Engineering and it’s importance (here)
• Explanation of linear or linearity in Linear Regression
  • The term ‘linear’ in linear regression refers to the linearity in the coefficients, i.e. the target variable y is linearly related to the model coefficients. It does not require that y should be linearly related to the raw attributes or features. Feature functions could be linear or non-linear.
• Techniques for handling Class Imbalance in Dataset
  • 8 Tactics to Combat Imbalanced Classes in Your Machine Learning Dataset
  • 10 Techniques to deal with Imbalanced Classes in Machine Learning
  • Class Imbalance - Different Perspective
• XGBoost
  • XGBoost Algorithm - Medium
  • A Gentle Introduction to XGBoost for Applied Machine Learning
• LightGBM
  • What is LightGBM, How to implement it? How to fine tune the parameters?
  • How to Develop a Light Gradient Boosted Machine (LightGBM) Ensemble
• Logistic Regression (here)
• Voting Ensembles
  • ML|Voting Classifier using Sklearn
  • How to Develop Voting Ensembles With Python
  • How VOTing classifiers work!
• Time-Series forecasting in Python(AR, MA, ARIMA, SARIMA and SARIMAX model) (here)
• Multivariate time-series forecasting
  • A Multivariate Time Series Guide to Forecasting and Modeling in Python
  • Multivariate time series forecasting
• Missing values Imputation
  • 6 Different Ways to Compensate for Missing Values In a Dataset (Data Imputation with examples)
• LightGBM Vs XGBoost
  • Which algorithm takes the crown: Light GBM vs XGBOOST?
• Gradient Descent
  • Gradient Descent For Machine Learning
• Gradient
  • What Is a Gradient in Machine Learning?
• Stochastic Gradient
  • Stochastic Gradient Descent — Clearly Explained !!
  • Stochastic Gradient Descent Algorithm With Python and NumPy
• Clustering
  • 10 Clustering Algorithms With Python
  • Clustering Algorithm for data with mixed Categorical and Numerical features
  • Understanding K-Means, K-Means++ and, K-Medoids Clustering Algorithms
  • Clustering datasets having both numerical and categorical variables
  • K-ModesClustering
• KNN Overview and finding optimal value of K (here)
• Which Classification metric to choose and when?
  • The 5 Classification Evaluation metrics every Data Scientist must know
  • Classification Metrics & Thresholds Explained
  • 24 Evaluation Metrics for Binary Classification (And When to Use Them)
• Ways of Encoding Categorical variables
  • Smarter Ways to Encode Categorical Data for Machine Learning

Related Mathematics

• Mathplanet - Exponentials
• Mathplanet - Logarithms

Model Evaluation (here)

• Regression
  • R-squared/Adj. R-squared
  • Root Mean Squared Error(RMSE) / Mean Squared Error
  • Mean Absolute Error(MAE)
• Classification (here)
  • Accuracy, Precision, and Recall
  • Log Loss/Binary Crossentropy
  • Categorical Crossentropy
  • Confusion Matrix
  • F1 Score
  • AUC