Course Outline
Introduction to Machine Learning
- Types of machine learning – supervised versus unsupervised.
- The transition from statistical learning to machine learning.
- The data mining workflow: business understanding, data preparation, modelling, and deployment.
- Selecting the appropriate algorithm for the task.
- Overfitting and the bias-variance tradeoff.
Overview of Python and ML Libraries
- The rationale for using programming languages in ML.
- Choosing between R and Python.
- Python crash course and Jupyter Notebooks.
- Python libraries: pandas, NumPy, scikit-learn, matplotlib, and seaborn.
Testing and Evaluating ML Algorithms
- Generalisation, overfitting, and model validation.
- Evaluation strategies: holdout, cross-validation, and bootstrapping.
- Metrics for regression: ME, MSE, RMSE, and MAPE.
- Metrics for classification: accuracy, confusion matrix, and handling unbalanced classes.
- Model performance visualisation: profit curve, ROC curve, and lift curve.
- Model selection and grid search for tuning.
Data Preparation
- Importing and storing data in Python.
- Exploratory analysis and summary statistics.
- Handling missing values and outliers.
- Standardisation, normalisation, and transformation.
- Qualitative data recoding and data wrangling with pandas.
Classification Algorithms
- Binary versus multiclass classification.
- Logistic regression and discriminant functions.
- Naïve Bayes and k-nearest neighbours.
- Decision trees: CART, Random Forests, Bagging, Boosting, and XGBoost.
- Support Vector Machines and kernels.
- Ensemble learning techniques.
Regression and Numerical Prediction
- Least squares and variable selection.
- Regularisation methods: L1 and L2.
- Polynomial regression and nonlinear models.
- Regression trees and splines.
Neural Networks
- Introduction to neural networks and deep learning.
- Activation functions, layers, and backpropagation.
- Multilayer perceptrons (MLP).
- Using TensorFlow or PyTorch for basic neural network modelling.
- Neural networks for classification and regression.
Sales Forecasting and Predictive Analytics
- Time series versus regression-based forecasting.
- Handling seasonal and trend-based data.
- Building a sales forecasting model using ML techniques.
- Evaluating forecast accuracy and uncertainty.
- Business interpretation and communication of results.
Unsupervised Learning
- Clustering techniques: k-means, k-medoids, hierarchical clustering, and SOMs.
- Dimensionality reduction: PCA, factor analysis, and SVD.
- Multidimensional scaling.
Text Mining
- Text preprocessing and tokenisation.
- Bag-of-words, stemming, and lemmatisation.
- Sentiment analysis and word frequency.
- Visualising text data with word clouds.
Recommendation Systems
- User-based and item-based collaborative filtering.
- Designing and evaluating recommendation engines.
Association Pattern Mining
- Frequent itemsets and the Apriori algorithm.
- Market basket analysis and lift ratio.
Outlier Detection
- Extreme value analysis.
- Distance-based and density-based methods.
- Outlier detection in high-dimensional data.
Machine Learning Case Study
- Understanding the business problem.
- Data preprocessing and feature engineering.
- Model selection and parameter tuning.
- Evaluation and presentation of findings.
- Deployment.
Summary and Next Steps
Requirements
- Basic understanding of machine learning concepts, including supervised and unsupervised learning.
- Familiarity with Python programming (variables, loops, functions).
- Some experience with data handling using libraries like pandas or NumPy is beneficial but not mandatory.
- No prior experience with advanced modelling or neural networks is expected.
Audience
- Data scientists.
- Business analysts.
- Software engineers and technical professionals working with data.
Testimonials (3)
I really liked the end where we took the time to play around with CHAT GPT. The room was not set up the best for this- instead of one large table a couple of small ones so we could get into small groups and brainstorm would have helped
Nola - Laramie County Community College
Course - Artificial Intelligence (AI) Overview
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
