Statistics and Machine Learning Basics

Unit I: Basics

• Statistical Concepts:

  • Mean: Average of all values.
  • Median: Middle value in a sorted list.
  • Mode: Most frequent value.
  • Range: Difference between highest and lowest values.
  • Outliers: Data points significantly different from others.
  • Average Deviation: Average of absolute deviations from the mean.
  • Absolute Deviation: Absolute difference from a central value.
  • Squared Deviation: Square of difference from the mean.
  • Standard Deviation: Measure of data dispersion around the mean.
  • Total Sum of Squares (TSS): Sum of squared deviations from the mean.

• Introduction to Machine Learning (ML):

  • Machine Learning: Training algorithms on data to predict or decide.
  • Supervised Learning: Learning from labeled data (input-output pairs).
  • Unsupervised Learning: Finding patterns in unlabeled data.
  • Reinforcement Learning: Learning through interaction with an environment.

• Introduction to Python:

  • Basic Operations: Arithmetic, variables, data types.
  • Data Structures: Lists, tuples, dictionaries.
  • Flow Control: Conditional statements (if-else), loops.
  • Strings: Text manipulation.
  • File Handling: Reading and writing files.
  • Libraries: NumPy (numerical), Scikit-learn (ML).

Unit II: Exploratory Data Analysis (EDA)

• EDA Introduction:

  • Steps: Data cleaning, transformation, visualization, summary statistics.
  • Data Types: Numerical (discrete, continuous), categorical.

• Data Transformation:

  • Techniques: Deduplication, value replacement, discretization, binning.
  • Missing Data Handling: Methods for dealing with missing values.

• Data Visualization:

  • Libraries: Matplotlib, Seaborn.

Unit III: Supervised Learning Algorithms

• Linear Regression: Predicting continuous variables.
• Logistic Regression: Predicting binary outcomes.
• Decision Trees: Tree-like model for classification/regression.
• Random Forest: Multiple decision trees for better results.
• Support Vector Machines (SVM): Finding optimal hyperplanes.
• K-Nearest Neighbors (KNN): Instance-based algorithm.
• CN2 Algorithm: Rule induction for classification.
• Naive Bayes: Probabilistic classifier using Bayes' theorem.

Unit IV: Clustering and Dimensionality Reduction

• Clustering:

  • K-means: Partitioning data into clusters.
  • Silhouette Scores: Evaluating cluster quality.
  • Hierarchical Clustering: Creating a hierarchy of clusters.
  • Fuzzy c-means: Data points can belong to multiple clusters.
  • DBScan: Density-based clustering.

• Dimensionality Reduction:

  • Feature Selection: Low Variance Filter, High Correlation Filter, Backward Feature Elimination, Forward Feature Selection.
  • PCA: Reducing dimensions while preserving variance.
  • Projection Methods.

Unit V: Model Evaluation and Hyperparameter Tuning

• Model Evaluation & Selection:

  • Cross-validation: Evaluating model performance.
  • Model Evaluation Metrics: Accuracy, precision, recall, F1-score.
  • Model Selection: Choosing the best model.

• Hyperparameter Optimization:

  • Tuning Techniques: Manual search, random search, grid search.
  • Python Example.

This quiz covers fundamental concepts in statistics, including measures of central tendency like mean and median, as well as important statistical deviations. Additionally, it introduces key principles of machine learning, focusing on supervised and unsupervised learning techniques. Test your knowledge on these essential topics!