Course Outline
Introduction to Machine Learning
- Categories of machine learning – supervised vs. unsupervised
- Transitioning from statistical learning to machine learning
- The data mining lifecycle: business comprehension, data readiness, modeling, and deployment
- Selecting appropriate algorithms for specific tasks
- Addressing overfitting and the bias-variance trade-off
Overview of Python and ML Libraries
- The role of programming languages in machine learning
- Deciding between R and Python
- Essentials of Python and Jupyter Notebooks
- Key Python libraries: pandas, NumPy, scikit-learn, matplotlib, seaborn
Testing and Assessing ML Algorithms
- Concepts of generalization, overfitting, and model validation
- Validation approaches: holdout, cross-validation, and bootstrapping
- Regression metrics: ME, MSE, RMSE, 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
- Importing and storing data in Python
- Conducting exploratory analysis and computing summary statistics
- Managing missing values and outliers
- Applying standardization, normalization, and transformations
- Recoding qualitative data and manipulating datasets with pandas
Classification Algorithms
- Distinguishing between binary and 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
- Techniques for ensemble learning
Regression and Numerical Forecasting
- Least squares method and variable selection
- Regularization techniques: L1 and L2
- Polynomial regression and nonlinear modeling
- Regression trees and splines
Unsupervised Learning
- Clustering methods: 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 model, stemming, and lemmatization
- Sentiment analysis and word frequency studies
- Visualizing textual data using word clouds
Recommendation Systems
- User-based and item-based collaborative filtering
- Architecting and assessing recommendation engines
Association Pattern Mining
- Identifying frequent itemsets using the Apriori algorithm
- Market basket analysis and calculating lift ratios
Outlier Detection
- Analysis of extreme values
- Distance-based and density-based detection methods
- Identifying outliers in high-dimensional datasets
Machine Learning Case Study
- Defining the business challenge
- Preparing data and engineering features
- Selecting models and tuning parameters
- Assessing results and presenting insights
- Deployment strategies
Conclusions and Future Directions
Requirements
- Fundamental grasp of statistics and linear algebra
- Working knowledge of data analysis or business intelligence principles
- Basic programming experience (ideally in Python or R) is advised
- Curiosity to apply machine learning in data-centric projects
Target Audience
- Data analysts and scientists
- Statisticians and research specialists
- Developers and IT experts 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
