Statistics, M.L. & Adv. M.L.
  • Machine Learning Examples
    • Introduction to Machine Learning & Data Analysis
    • Machine Learning Examples
  • Statistics & Exploratory Data Analysis (EDA)
    • Descriptive Statistics
    • Descriptive Analytics
    • Measure of Central Tendency
    • Dispersion
    • Probability Distribution
    • Correlation & Covariance
    • Inferential Statistics
    • Diagnostic Analytics
    • Outliers | Missing Values
    • Sparsity
    • Inferential Statistics
    • Random Sampling
    • Hypothesis Testing
       
  • Supervised Learning
    • Installing & Using Scikit-Learn Package
    • Linear Regression
    • Multi-Linear Regression
    • Decision Tree (CART)
    • Ensemble Learning
    • Random Forest
    • xgBoost
    • K Nearest Neighbors (KNN)
    • Support Vector Machine (SVM)
    • Kernel Functions
    • Naive Bayes Classifier (NBC) / MultinomialNB Algorithm
    • GRID SEARCH CV AND RANDOM SEARCG CV
    • Linear Discriminant Analysis (LDA)
       
  • Unsupervised Learning
    • Hierachical Clustering
    • K Means Clustering
       
  • Dimensionality Reduction
    • Principal Component Analysis (PCA)
       
  • Time Series Forecasting
    • Time Series Data
    • Trend Chart
    • Stationarity
    • Seasonality
    • Moving Average
    • Exponential Smoothing
    • ARIMA
    • GARCH
       
  • Model Improvement & Validation
    • Regularization
    • Lasso
    • Ridge
    • ElasticNet
    • Cross Validation
       
  • Methods
    • Sigmoid Function
    • Entropy
    • Information Gain
    • Gini Index
    • Feature Selection
    • Ensemble Modeling (Bagging | Boosting | Stacking)
    • K Selection / Distance Metrics
    • Kernel Functions
    • Elbow Method
       
  • Metrics
    • MAE / MSE/ RMSE / R2 and Adjusted R2
    • AUC ROC CURVE
    • Precision
    • Recall
    • F1 score
    • Confusion Metrics
  • Only Theory
    • SMOTE and near miss
    • EXTRA TREE CLASSIFIER (Random forest)
    • TPOT
    • LOG LOSS
    • CROSS VALIDATION
    • KERNAL PCA
    • LIGHT GBM and cat boosting
    • DBSCAN
    • MINI BATCH K MEANS
    • Passive Aggressive Classifier Algorithm
    • Facebook Prophet
    • time series RNN LSTM
       
  • When to Use Which Algorithms!
     
  • Projects / Case Studies
    • 1) Real Estate Price Prediction
    • 2) Fraud Detection Problem in BFSI domain
    • 3) Disease Detection in Healthcare domain
    • 4) Market Segmentation in Advertising Sector
    • 5) Market Basket Analysis in Departmental Sector
    • 6) Passenger Forecasting in Aviation Sector
    • 7) News Classification in Media Sector
    • 8) Dashboard (Visualization Tool)
    • Please Note - Case studies / projects changes from time to time and will be covered along with their respective modules.