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Course Outline
Machine Learning Introduction
- Types of machine learning – supervised vs unsupervised
- From statistical learning to machine learning
- The data mining workflow: business understanding, data preparation, modeling, deployment
- Choosing the right algorithm for the task
- Overfitting and the bias-variance tradeoff
Python and ML Libraries Overview
- Why use programming languages for 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
- Basic knowledge of machine learning concepts such as supervised and unsupervised learning
- Familiarity with Python programming (variables, loops, functions)
- Some experience with data handling using libraries like pandas or NumPy is helpful but not required
- No prior experience with advanced modeling or neural networks is expected
Audience
- Data scientists
- Business analysts
- Software engineers and technical professionals working with data
28 Hours
Testimonials (1)
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
