Course Outline
Introduction to Machine Learning
- Types of machine learning – supervised vs unsupervised.
- Transition from statistical learning to machine learning.
- The data mining workflow: business understanding, data preparation, modeling, deployment.
- Selecting the appropriate algorithm for the task.
- Overfitting and the bias-variance tradeoff.
Python and ML Libraries Overview
- Reasons for using programming languages in ML.
- Choosing between R and Python.
- Python crash course and Jupyter Notebooks.
- Python libraries: pandas, NumPy, scikit-learn, matplotlib, seaborn.
Testing and Evaluating ML Algorithms
- Generalization, overfitting, and model validation.
- Evaluation strategies: holdout, cross-validation, bootstrapping.
- Metrics for regression: ME, MSE, RMSE, MAPE.
- Metrics for classification: accuracy, confusion matrix, unbalanced classes.
- Model performance visualization: profit curve, ROC curve, lift curve.
- Model selection and grid search for tuning.
Data Preparation
- Data import and storage in Python.
- Exploratory analysis and summary statistics.
- Handling missing values and outliers.
- Standardization, normalization, and transformation.
- Qualitative data recoding and data wrangling with pandas.
Classification Algorithms
- Binary vs multiclass classification.
- Logistic regression and discriminant functions.
- Naïve Bayes, k-nearest neighbors.
- Decision trees: CART, Random Forests, Bagging, Boosting, XGBoost.
- Support Vector Machines and kernels.
- Ensemble learning techniques.
Regression and Numerical Prediction
- Least squares and variable selection.
- Regularization methods: L1, 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 modeling.
- Neural networks for classification and regression.
Sales Forecasting and Predictive Analytics
- Time series vs 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, SOMs.
- Dimensionality reduction: PCA, factor analysis, SVD.
- Multidimensional scaling.
Text Mining
- Text preprocessing and tokenization.
- Bag-of-words, stemming, and lemmatization.
- Sentiment analysis and word frequency.
- Visualizing 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 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
- Foundational knowledge of machine learning principles, 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 modeling or neural networks is expected.
Audience
- Data scientists.
- Business analysts.
- Software engineers and technical professionals working with data.
Testimonials (2)
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
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