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Duration 28 hours
Course Outline
Introduction to Machine Learning
- Classification of machine learning approaches: supervised versus unsupervised
- Transition from statistical learning frameworks to machine learning methodologies
- The data mining lifecycle: operational context, data preparation, model development, and deployment
- Selecting appropriate algorithms for specific government or enterprise objectives
- Addressing overfitting and the bias-variance tradeoff
Overview of Python and Machine Learning Libraries
- Rationale for utilizing programming languages in machine learning operations
- Comparative analysis of R and Python for technical implementation
- Essential Python programming fundamentals and Jupyter Notebook environments
- Core Python libraries: pandas, NumPy, scikit-learn, matplotlib, and seaborn
Testing and Evaluation of Machine Learning Algorithms
- Model generalization, overfitting mitigation, and validation strategies
- Evaluation methodologies: holdout sets, cross-validation, and bootstrapping
- Regression metrics: Mean Error, Mean Squared Error, Root Mean Squared Error, and Mean Absolute Percentage Error
- Classification metrics: accuracy, confusion matrices, and strategies for imbalanced datasets
- Visualizing model performance: profit curves, ROC curves, and lift curves
- Model selection and grid search optimization for parameter tuning
Data Preparation
- Data ingestion and storage management in Python
- Exploratory data analysis and generation of summary statistics
- Procedures for addressing missing data points and outliers
- Data standardization, normalization, and transformation techniques
- Recoding qualitative variables and data wrangling using pandas
Classification Algorithms
- Distinctions between binary and multiclass classification tasks
- Logistic regression and discriminant function analysis
- Application of Naïve Bayes and k-nearest neighbors algorithms
- Decision tree methodologies: CART, Random Forests, Bagging, Boosting, and XGBoost
- Support Vector Machines and kernel functions
- Ensemble learning techniques for improved predictive accuracy
Regression and Numerical Prediction
- Least squares methods and variable selection processes
- Regularization techniques: L1 and L2 norms
- Polynomial regression and nonlinear modeling approaches
- Regression trees and spline functions
Neural Networks
- Fundamentals of neural networks and deep learning architectures
- Activation functions, network layers, and backpropagation mechanisms
- Implementation of Multilayer Perceptrons (MLP)
- Basic neural network modeling using TensorFlow or PyTorch
- Application of neural networks to classification and regression problems
Sales Forecasting and Predictive Analytics
- Comparative analysis of time series versus regression-based forecasting
- Managing seasonal variations and trend-based data patterns
- Development of sales forecasting models utilizing machine learning techniques
- Assessment of forecast accuracy and uncertainty ranges
- Interpretation of business impact and communication of analytical results
Unsupervised Learning
- Clustering methodologies: k-means, k-medoids, hierarchical clustering, and Self-Organizing Maps
- Dimensionality reduction techniques: PCA, factor analysis, and SVD
- Multidimensional scaling applications
Text Mining
- Text preprocessing and tokenization processes
- Bag-of-words models, stemming, and lemmatization
- Sentiment analysis and word frequency analysis
- Visualization of textual data using word clouds
Recommendation Systems
- User-based and item-based collaborative filtering techniques
- Design and evaluation of recommendation engines for government or organizational use
Association Pattern Mining
- Identification of frequent itemsets and the Apriori algorithm
- Market basket analysis and lift ratio calculations
Outlier Detection
- Analysis of extreme values
- Distance-based and density-based detection methods
- Outlier identification in high-dimensional datasets
Machine Learning Case Study
- Defining and understanding the operational business problem
- Data preprocessing and feature engineering processes
- Model selection and parameter optimization
- Evaluation of results and presentation of findings
- Model deployment and integration
Summary and Next Steps
Requirements
- Basic knowledge of machine learning concepts such as supervised and unsupervised learning
- Familiarity with Python programming constructs (variables, loops, functions)
- Experience with data handling using libraries like pandas or NumPy is beneficial but not mandatory
- No prior experience with advanced modeling or neural networks is expected
Audience
- Data scientists
- Business analysts
- Software engineers and technical professionals working with data for government or public sector applications
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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