Statistics & Probability
  • Mean, Median, Mode, Variance, Standard Deviation
  • Skewness, Kurtosis, Correlation, Covariance
  • Probability Distributions: Normal, Binomial, Poisson
  • Sampling, Central Limit Theorem
  • Hypothesis Testing: t-test, z-test, ANOVA, chi-square
  • Confidence Intervals, p-values, Type I & II errors
  • Note – Statistics part will be taken along with ML & AI as and when required

Exploratory Data Analysis (EDA)
  • Data Preprocessing & EDA Overview
  • Handling missing values, outliers, duplicates
  • Encoding (One-hot, Label), Scaling (Standard, MinMax)
  • Feature engineering, selection techniques
  • EDA: Univariate, Bivariate, Multivariate analysis
  • Visuals: Heatmaps, Pairplots, Boxplots, Violin plots

Machine Learning
  • Supervised Learning
    • Linear & Logistic Regression
    • Decision Tree, Random Forest, KNN
    • SVM with kernel functions
    • Naive Bayes, AdaBoost, XGBoost
    • Project 1: House Price Prediction
    • Project 2: Email Spam Detection
  • Unsupervised Learning
    • K-Means, DBSCAN, Hierarchical Clustering
    • Dimensionality Reduction: PCA
    • Project 3: Employee Role Segmentation
  • Model Evaluation & Optimization
    • Metrics: Accuracy, Precision, Recall, F1, AUC, R2, RMSE
    • Cross Validation, Stratified K-Fold
    • Hyperparameter tuning: GridSearchCV, RandomizedSearchCV
    • Imbalanced Data Handling: SMOTE, NearMiss

Time Series Forecasting
  • Time Series Analysis Basics
  • Trend, Seasonality, Noise, Stationarity
  • ADF Test, ARIMA, SARIMA
  • Facebook Prophet, Exponential Smoothing
  • Project 4: Sales Forecasting