Skip to main content
Back to top
Ctrl
+
K
Welcome to Machine Learning for Business
Course Introduction
Course Goals & Business Outcomes
How to Use This Book
Roadmap & Prerequisites
Math & Notation Foundations
Common Math Symbols
Quick Linear Algebra
Calculus Essentials
Probability Essentials
Math Cheat-Sheet (Worked Examples)
Data Loading, Wrangling & Visualisation
Data Loading (CSV, Excel, SQL, APIs)
Data Cleaning & Preprocessing
Handling Missing Data & Outliers
Feature Types & Encoding
Exploratory Data Analysis
Data Visualisation
Business Dashboards
Performance Metrics & Visualisation
Regression Metrics
Classification Metrics
Business Visualisation
Notebook – Metric Dashboard
Supervised Regression – Linear Models
Linear Model Family
Mean Squared Error
Gradients & Partial Derivatives
OLS & Normal Equations
Non-linear & Polynomial Features
Regularization (Ridge, Lasso, Elastic Net)
Bias–Variance Tradeoff
Lab – Sales Forecasting
Optimization & Training Practicalities
Gradient Descent Variants
Advanced Optimizers (Adam, etc.)
Learning Rate Schedules
Numerical Stability & Vectorization
Lab – Comparing GD Variants
Classification Models
Logistic Regression
Naive Bayes
Calibration & Class Imbalance
Lab – Churn Prediction
Support Vector Machines
Max-Margin Intuition
Kernel SVMs (RBF, Polynomial)
Soft Margin & Regularization
Lab – Sentiment Classification with SVM
Distance-Based & Instance Methods
Tree-Based Models & Ensembles
Decision Trees
🌲 Bagging, Random Forests & XGBoost
Feature Importance
Lab – Fraud Detection
Unsupervised Learning & Dimensionality Reduction
PCA
K-Means
Gaussian Mixture Models
Visualisation (t-SNE, UMAP)
Lab – Customer Segmentation
Recommender Systems & Association Rules
Collaborative Filtering
Content-Based Filtering
Hybrid Recommenders
Association Rule Mining
Lab – Market Basket & Recommendations
Model Evaluation & Selection
Cross-Validation Strategies
Nested CV & Model Comparison
Hyperparameter Tuning
Business-Aware Metrics
Lab – Campaign Targeting
Time Series & Forecasting
Stationarity & Differencing
ARIMA / SARIMA
Prophet
Backtesting & KPIs
Case Study – Inventory Planning
Survival Analysis & Customer Lifetime Value
Survival Analysis Basics
Kaplan–Meier Estimator
Cox Proportional Hazards
Customer Lifetime Value Modeling
Lab – CLV Estimation
Neural Networks & Applied Deep Learning
Perceptron & MLP
CNN Basics
ResNet & TCN
Lab – PDF Images OCR and structure understanding to get data
Transformers, LSTMs & LLMs
LSTM Architecture & Use Cases
Transformer Architecture
LSTM vs Transformer
Fine-Tuning Transformers
Lab – HuggingFace LLM project
LLM Agents for Business
What is an AI Agent?
LangChain & Tool-Augmented LLMs
Workflow Orchestration
Business Use Cases
Lab – LangChain Agent for KPI Queries
Generative Models & Multimodal Learning
Variational Autoencoders
GANs & Diffusion Models
Multimodal Learning
Synthetic Data
Lab – Synthetic Dataset Generation
Advanced Topics
Sparsity in Business Data
Uncertainty Quantification
Scaling to Large Datasets
Causal Inference for Business Experiments
Retrieval-Augmented Generation
Practical Production & Business Essentials
Feature Engineering Pipelines
Model Monitoring & Drift Detection
Interpretability
A/B Testing & KPI Alignment
Mini Case – Dashboard Deployment
Assessment, Labs & Capstone
Guided Labs
Capstone Projects
Practical Exam
Appendices
Math Cheat-Sheets
Dataset Index
Tooling Guides
References & Further Reading
Repository
Open issue
Index
