Machine Learning for Business

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Machine Learning for Business

Course Introduction

• Course goals & business outcomes
• How to use the book (notebooks, datasets, evaluation)
• Short roadmap and prerequisites (linear algebra / basic probability refresher links)

Math & Notation Foundations (compact, business-oriented)

• Common math symbols and notations used in ML
• Quick linear algebra: vectors, matrices, design matrix, target vector
• Calculus essentials: partial derivatives, gradient notation
• Probability essentials for classifiers (conditional probability, Bayes rule)
• Notebook: Math cheat-sheet with worked examples

Data Loading, Wrangling & Visualisation

• Data loading from CSV, Excel, SQL, APIs
• Data cleaning & preprocessing for business datasets
• Handling missing data & outliers
• Feature types & encoding
• Exploratory Data Analysis (EDA)
• Visualization: histograms, scatter plots, box plots, heatmaps
• Business dashboards for quick data insights

Performance Metrics & Visualisation

• Regression: MSE, RMSE, MAE
• Classification: precision/recall, ROC, PR, calibration plots
• Business visualization: lift charts, profit curves
• Notebook: Metric dashboard utility functions

Supervised Regression: Linear Models

• Model class: linear model family (notation & assumptions)
• Mean Squared Error (MSE), squared error in matrix form
• Gradients & partial derivatives
• OLS & normal equations
• Non-linear least squares & polynomial features
• Regularization: Ridge, Lasso, Elastic Net
• Biasvariance tradeoff & overfitting
• Lab: Sales forecasting with regularized linear models

Optimization & Training Practicalities

• Gradient Descent: batch / mini-batch / stochastic
• Advanced optimizers (SGD variants, Adam)
• Learning rate schedules, early stopping, initialization
• Numerical stability & vectorization
• Notebook: Comparing GD variants on a business dataset

Classification: Probabilistic & Discriminative Models

• Logistic Regression (math, log-loss, decision boundaries)
• Naive Bayes (multinomial & Bernoulli)
• Performance metrics: accuracy, precision/recall, AUC
• Calibration & class imbalance handling
• Lab: Churn prediction with logistic regression

Distance-Based & Instance Methods

• k-Nearest Neighbors (classification & regression)
• Efficient search structures: KD-Trees, Ball Trees
• Scaling considerations
• Lab: KNN for customer segmentation

Tree-Based Models & Ensembles

• Decision Trees: splitting criteria, pruning, Gini/entropy
• Bagging, Random Forests, Gradient Boosted Trees (XGBoost, LightGBM)
• Feature importance & interpretability
• Lab: Fraud detection with LightGBM

Unsupervised Learning & Dimensionality Reduction

• PCA: standardization, explained variance, SVD link
• K-Means clustering
• Gaussian Mixture Models & EM
• Visualization: MDS, t-SNE, UMAP
• Lab: Customer segmentation using PCA K-Means GMM

Model Evaluation, Selection & Tuning

• Cross-validation strategies (time-series vs i.i.d.)
• Nested CV, model comparison, statistical tests
• Hyperparameter tuning: grid, random, Bayesian
• Business-aware metrics: cost-sensitive evaluation, lift, ROI mapping
• Lab: Model selection for campaign targeting

Time Series & Forecasting

• Stationarity & differencing
• ARIMA / SARIMA & seasonal models
• Prophet for quick forecasting
• Backtesting & business KPIs
• Case Study: Inventory planning via demand forecasting

Neural Networks & Applied Deep Learning

• Perceptron & MLP: activations, backprop basics
• CNN basics for image-related business tasks
• 1D-ResNet & TCN for sequence tasks
• Lab: Small NN for product image classification

Transformers, LSTMs & LLMs (Applied Overview)

• LSTM architecture & business use cases (e.g., call center logs, time series)
• Transformer architecture: attention mechanism, positional encoding, feed-forward
• LSTM vs Transformers: when to use each in business
• Fine-tuning transformers for NLP business tasks (classification, summarization, semantic search)
• Notebook: Mini transformer block on toy tokens
• Lab: Fine-tuning BERT for business text classification

LLM Agents for Business

• What is an AI agent? (planner, memory, tools)
• LangChain & similar frameworks
• Tool-augmented LLMs: connecting to CRMs, ERPs, APIs
• Workflow orchestration with agents

• Business cases:

  • Automated market research
  • Report generation from multiple data sources
  • Customer support triage & routing

• Lab: Build a LangChain-based agent to answer business KPI queries

Generative Models & Multimodal Learning

• Variational Autoencoders (VAEs)
• GANs & diffusion models
• Multimodal learning (text + tabular / image fusion)
• Synthetic data for business scenarios
• Lab: Synthetic marketing dataset generation

Advanced Topics

• Sparsity in business datasets
• Uncertainty quantification
• Scaling methods for large datasets
• Retrieval-Augmented Generation (RAG) for proprietary data search

Practical Production & Business Essentials

• Feature engineering pipelines
• Model monitoring & drift detection
• Interpretability: SHAP, partial dependence
• A/B testing & KPI alignment
• Mini-case: Model dashboard deployment

Assessment, Labs & Capstone

• Guided labs for each major chapter
• Capstone projects: churn prediction, demand forecasting, pricing optimization, recommendation system
• Practical exam (35 marks) design

Appendices

• Compact math cheat-sheets
• Dataset index & usage notes
• Tooling guides (Colab, MLflow, DVC)
• References & further reading