Duration 3 Days – 21 hours

 

Overview

This training provides a practical introduction to Machine Learning (ML) using both Python and R—the two most widely used tools for data science and analytics. Participants will learn how to prepare data, train and evaluate ML models, and interpret results using structured workflows and real datasets. The course covers supervised and unsupervised learning methods, including regression, classification, clustering, and model validation techniques, with hands-on labs for both Python and R implementations.

By the end of the course, learners will be able to build end-to-end ML pipelines, compare model performance, and apply best practices in feature engineering, evaluation, and deployment readiness.

 

Objectives 

• Understand core Machine Learning concepts and workflows
• Prepare and clean datasets for ML in Python and R
• Perform feature engineering and select appropriate features
• Build ML models for regression and classification
• Evaluate models using proper metrics and validation techniques
• Apply hyperparameter tuning and improve model performance
• Implement clustering and dimensionality reduction techniques
• Compare Python vs R ML workflows and choose the right tool for use cases
• Deliver a mini end-to-end ML solution using real-world datasets

 

Target Audience 

• Data Analysts and BI Professionals
• Aspiring Data Scientists / ML Engineers
• Software Developers expanding into AI/ML
• Business Analysts working with predictive analytics
• Researchers / Academics working with data modeling
• IT / Digital Transformation teams supporting data initiatives

 

Prerequisites

• Basic programming understanding (any language is fine)
• Basic knowledge of statistics (mean, variance, correlation—helpful)
• Familiarity with spreadsheets/data tables
• Laptop with admin rights preferred (for setup)
• Recommended (but not required): Basic Python or R fundamentals

 

Course Outline (Standard, Hands-on)

Module 1: Introduction to Machine Learning (Python + R)

• What ML is (and what it is not)
• Supervised vs unsupervised learning
• ML workflow: Data → Features → Model → Evaluation → Deployment readiness
• Common ML use cases in business and industry

       Lab: Setup + run first ML notebook/script (Python + R)

Module 2: Environment Setup and Tools

• Python stack
• Jupyter Notebook / VS Code
• NumPy, Pandas, Matplotlib/Seaborn
• Scikit-learn
• R stack
• RStudio
• Tidyverse, ggplot2
• Caret or tidymodels

Lab: Load dataset + explore it in both tools

Module 3: Data Understanding and Exploratory Data Analysis (EDA)

• Data types: numeric, categorical, datetime, text basics
• Missing values and outliers
• Visualization techniques for insights
• Detecting patterns and relationships

     Lab: EDA checklist in Python + R

Module 4: Data Cleaning and Preprocessing

 Handling missing values (drop, impute strategies)

• Encoding categorical variables (One-hot, Label encoding)
• Scaling and normalization (StandardScaler, Min-Max)
• Train/test split best practices

      Lab: Clean dataset + preprocess pipeline (Python + R)

Module 5: Regression Models (Predicting Continuous Values)

• Linear Regression fundamentals
• Regularization: Ridge, Lasso (overview + when to use)
• Metrics: MAE, MSE, RMSE, R²
• Residual analysis and interpretation

     Lab: Build and evaluate regression model in Python + R

Module 6: Classification Models (Predicting Categories)

• Logistic Regression and Decision Trees
• k-NN and Random Forest (intro and comparison)
• Confusion matrix, accuracy, precision, recall, F1-score
• ROC and AUC

      Lab: Build a classifier (Python + R) + compare metrics

Module 7: Feature Engineering and Feature Selection

• Feature transformation and interaction features
• Handling imbalance (oversampling/undersampling overview)
• Feature importance and selection concepts
• Preventing data leakage

      Lab: Improve classification performance with engineered features

Module 8: Model Validation and Selection

• Cross-validation (K-fold)
• Bias vs variance intuition
• Underfitting vs overfitting
• Baseline model strategy

      Lab: Cross-validation experiment in Python + R

Module 9: Hyperparameter Tuning

• Grid Search vs Random Search
• Tuning for Decision Trees, Random Forest, k-NN
• Selecting best model using validation scores

      Lab: Hyperparameter tuning + reporting best parameters

Module 10: Unsupervised Learning

• Clustering: K-means basics
• Choosing the number of clusters (Elbow method, Silhouette score)
• Dimensionality reduction overview (PCA)

      Lab: Customer segmentation clustering mini-exercise

Module 11: Interpreting and Explaining Models (Practical)

• Model interpretability basics
• Feature importance and coefficients
• When to prioritize interpretability vs accuracy
• Common pitfalls (data leakage, wrong metrics, imbalance)

      Lab: Create a model performance report summary

Module 12: Mini Project (End-to-End ML Build)

• Participants will complete a guided mini-project such as:
• Customer churn prediction
  • Loan default risk classification
  • Sales forecasting regression
  • Customer segmentation clustering
• Deliverables
• Clean dataset + features
• Trained model + evaluation results
• Short presentation: “What problem, what model, what results, what next”