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Duration 28 hours
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 (2)
Getting people that never used AI some repetition in prompting and people that do use AI to consider different methods to using it.
Matthew Gay - Tarsus Pharmaceuticals
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