Course Outline
Introduction to Machine Learning
- Categorizations of machine learning – supervised vs. unsupervised
- The evolution from statistical learning to machine learning
- The data mining lifecycle: business comprehension, data preparation, modeling, and deployment
- Selecting the appropriate algorithm for specific tasks
- Overfitting and the bias-variance tradeoff
Overview of Python and ML Libraries
- The rationale for using programming languages in ML
- Evaluating the choice between R and Python
- An introductory Python session and an introduction to Jupyter Notebooks
- Key Python libraries: pandas, NumPy, scikit-learn, matplotlib, and seaborn
Testing and Assessing ML Algorithms
- Generalization, overfitting, and model validation
- Evaluation approaches: holdout, cross-validation, and bootstrapping
- Regression metrics: ME, MSE, RMSE, and MAPE
- Classification metrics: accuracy, confusion matrices, and handling imbalanced classes
- Visualizing model performance: profit curves, ROC curves, and lift curves
- Model selection and grid search for parameter tuning
Data Preparation
- Data importation and storage within Python
- Exploratory analysis and summary statistics
- Managing missing values and outliers
- Standardization, normalization, and data transformation
- Recoding qualitative data and data wrangling using pandas
Classification Algorithms
- Binary versus multiclass classification
- Logistic regression and discriminant functions
- Naïve Bayes and k-nearest neighbors
- Decision trees: CART, Random Forests, Bagging, Boosting, and XGBoost
- Support Vector Machines and kernel methods
- Ensemble learning techniques
Regression and Numerical Prediction
- Least squares and variable selection
- Regularization methods: L1 and L2
- Polynomial regression and nonlinear models
- Regression trees and splines
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 tokenization
- Bag-of-words models, stemming, and lemmatization
- Sentiment analysis and word frequency analysis
- Visualizing textual data using 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 ratios
Outlier Detection
- Extreme value analysis
- Distance-based and density-based methods
- Detecting outliers in high-dimensional data
Machine Learning Case Study
- Understanding the business problem
- Data preprocessing and feature engineering
- Model selection and parameter tuning
- Evaluating and presenting findings
- Deployment
Summary and Future Steps
Requirements
- Fundamental knowledge of statistics and linear algebra
- Acquaintance with data analysis or business intelligence principles
- Some exposure to programming (ideally in Python or R) is advised
- A genuine interest in applying machine learning to data-centric projects
Target Audience
- Data analysts and scientists
- Statisticians and research professionals
- Developers and IT specialists exploring machine learning tools
- Professionals engaged in data science or predictive analytics initiatives
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
