Machine Learning Training

Learn ML algorithms, model building, evaluation, and deployment using real datasets. Gain hands-on experience to build intelligent systems.

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ML Training
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ML Curriculum

  • Introduction to Python & Installation (Jupyter, VS Code, Anaconda)
  • Variables, Data Types, Loops, Conditionals, and Functions
  • File Handling & Exception Handling
  • Object-Oriented Programming (OOP) Basics
  • NumPy: Arrays, Broadcasting, and Vectorized Operations
  • Pandas: DataFrames, Groupby, Merging, and Pivot Tables
  • Data Visualization with Matplotlib, Seaborn & Plotly
  • Project: Building a Complete Data Analysis Report on a Real-world Dataset
  • Linear Algebra: Vectors, Matrices, Dot Products, and Eigenvalues
  • Calculus: Derivatives, Partial Derivatives & the Chain Rule
  • Probability Theory: Conditional Probability & Bayes' Theorem
  • Statistical Distributions: Normal, Binomial, Poisson & Uniform
  • Hypothesis Testing: t-tests, Chi-square, and ANOVA
  • Correlation Analysis: Pearson, Spearman & Kendall Coefficients
  • Project: Statistical Analysis of Customer Purchase Behavior Dataset
  • Data Cleaning: Handling Missing Values, Duplicates & Outliers
  • Feature Scaling: Standardization (Z-score) vs Min-Max Normalization
  • Categorical Encoding: One-Hot, Label, Target & Frequency Encoding
  • Feature Creation: Polynomial Features & Domain-specific Engineering
  • Handling Imbalanced Datasets: SMOTE, ADASYN & Undersampling
  • Feature Selection: Recursive Feature Elimination (RFE) & Mutual Information
  • Building Scikit-learn Pipelines: Integrating Transformers and Estimators
  • Project: Building an Automated Data Preprocessing Pipeline for Messy CSV Data
  • Simple Linear Regression: Theory, Cost Function (MSE) & OLS
  • House Price Prediction using Multiple Linear Regression
  • Gradient Descent: Batch, Stochastic (SGD) & Mini-batch Optimization
  • Polynomial Regression for Non-linear Relationships
  • Regularization: Ridge (L2), Lasso (L1) & ElasticNet Regression
  • Model Assessment: R-squared, Adjusted R-squared & Residual Analysis
  • Project: Predicting Real Estate Prices with Feature Engineering & Regularized Models
  • Logistic Regression: Sigmoid Function & Decision Boundaries
  • Customer Churn Prediction using Logistic Regression
  • Decision Trees: Information Gain, Gini Impurity & Pruning Strategies
  • Random Forest: Bagging & Feature Importance Analysis
  • Gradient Boosting: XGBoost, LightGBM & CatBoost Comparison
  • Support Vector Machines (SVM): Hyperplanes & the Kernel Trick
  • K-Nearest Neighbors (k-NN) & Naive Bayes Classification
  • Handwritten Digit Recognition using SVM on MNIST Dataset
  • Project: Credit Card Fraud Detection using Ensemble Methods
  • K-Means Clustering: Elbow Method & Silhouette Analysis
  • Customer Segmentation using K-Means on E-commerce Data
  • DBSCAN: Density-based Clustering for Non-spherical Data
  • Hierarchical Clustering: Dendrograms & Linkage Criteria
  • Dimensionality Reduction: PCA (Principal Component Analysis)
  • Visualization with t-SNE & UMAP for High-dimensional Data
  • Association Rule Mining: Apriori Algorithm for Market Basket Analysis
  • Project: Customer Segmentation Dashboard for a Retail Company
  • Confusion Matrix: Accuracy, Precision, Recall & F1-Score
  • ROC Curves & AUC (Area Under Curve) Analysis
  • K-Fold Cross-Validation & Stratified Sampling Techniques
  • Hyperparameter Tuning: Grid Search, Random Search & Bayesian Optimization
  • Bias-Variance Tradeoff: Diagnosing with Learning Curves
  • Model Comparison: Selecting the Best Model for Production
  • Project: Benchmarking 5 Classifiers on Titanic Survival Prediction
  • Model Serialization: Saving Models with Pickle, Joblib & ONNX
  • Model Explainability: SHAP Values and LIME Interpretations
  • Building REST APIs for ML Models using Flask & FastAPI
  • Creating Interactive ML Dashboards with Streamlit
  • Containerizing ML Applications with Docker
  • Experiment Tracking & Model Registry with MLflow
  • Monitoring Model Drift & Data Drift in Production
  • Cloud Deployment: Deploying to AWS SageMaker & Azure ML Studio
  • Project: Deploying a Salary Prediction Model as a Dockerized REST API with CI/CD