Course Outline
Introduction to Machine Learning
- Machine learning categories – supervised vs. unsupervised
- Transitioning from statistical learning to machine learning
- The data mining lifecycle: business insight, data prep, modeling, and deployment
- Selecting the appropriate algorithm for specific tasks
- Overfitting and the bias-variance balance
Overview of Python and ML Libraries
- The role of programming languages in ML
- Comparison between R and Python
- Python fundamentals and Jupyter Notebooks
- Key Python libraries: pandas, NumPy, scikit-learn, matplotlib, seaborn
Testing and Assessing ML Algorithms
- Generalization, overfitting, and model verification
- Assessment methods: holdout, cross-validation, bootstrapping
- Regression metrics: ME, MSE, RMSE, MAPE
- Classification metrics: accuracy, confusion matrices, handling class imbalance
- Visualizing model performance: profit curves, ROC curves, lift curves
- Model selection and grid search for optimization
Data Preparation
- Importing and storing data in Python
- Exploratory analysis and statistical summaries
- Managing missing values and anomalies
- Standardization, normalization, and feature transformation
- Recoding qualitative data and data cleansing using pandas
Classification Algorithms
- Binary vs. multi-class classification
- Logistic regression and discriminant analysis
- Naïve Bayes and k-nearest neighbors
- Decision trees: CART, Random Forests, Bagging, Boosting, XGBoost
- Support Vector Machines and kernel functions
- Ensemble learning methods
Regression and Numerical Forecasting
- Least squares methods and variable selection
- Regularization techniques: L1, L2
- Polynomial regression and nonlinear modeling
- Regression trees and splines
Unsupervised Learning
- Clustering methods: k-means, k-medoids, hierarchical clustering, SOMs
- Dimensionality reduction: PCA, factor analysis, SVD
- Multidimensional scaling
Text Mining
- Text preprocessing and tokenization
- Bag-of-words model, stemming, and lemmatization
- Sentiment analysis and term frequency
- Visualizing textual data via word clouds
Recommendation Systems
- Collaborative filtering based on users and items
- Architecture and evaluation of recommendation engines
Association Pattern Mining
- Frequent itemsets and the Apriori algorithm
- Market basket analysis and lift ratios
Outlier Detection
- Extreme value analysis
- Distance-based and density-based approaches
- Detecting outliers in high-dimensional datasets
Machine Learning Case Study
- Analyzing the business objective
- Data preprocessing and feature engineering
- Model selection and parameter optimization
- Evaluating and communicating results
- Deployment strategies
Wrap-up and Future Pathways
Requirements
- Fundamental grasp of statistics and linear algebra
- Acquaintance with data analysis or business intelligence principles
- Some programming experience (ideally in Python or R) is advised
- A desire to explore applied machine learning for data-centric initiatives
Target Audience
- Data analysts and data scientists
- Statisticians and research experts
- Developers and IT professionals investigating machine learning frameworks
- Professionals engaged in data science or predictive analytics endeavors
Testimonials (3)
Even with having to miss a day due to customer meetings, I feel I have a much clearer understanding of the processes and techniques used in Machine Learning and when I would use one approach over another. Our challenge now is to practice what we have learned and start to apply it to our problem domain
Richard Blewett - Rock Solid Knowledge Ltd
Course - Machine Learning – Data science
I like that training was focused on examples and coding. I thought that it is impossible to pack so much content into three days of training, but I was wrong. Training covered many topics and everything was done in a very detailed manner (especially tuning of model's parameters - I didn't expected that there will be a time for this and I was gratly surprised).
Bartosz Rosiek - GE Medical Systems Polska Sp. Zoo
Course - Machine Learning – Data science
It is showing many methods with pre prepared scripts- very nicely prepared materials & easy to traceback
