Course Code

ML_LBG

Duration

21 hours (usually 3 days including breaks)

Requirements

Knowledge and awareness of Machine Learning fundamentals

Overview

Це навчальне заняття на основі класу вивчить інструменти машинного навчання з (запропонованим) Python . Делегати матимуть приклади, що базуються на комп’ютері, та навчальні приклади.

Machine Translated

Course Outline

Machine Learning introduction
- Types of Machine learning – supervised vs unsupervised learning
- From Statistical learning to Machine learning
- The Data Mining workflow:
  - Business understanding
  - Data Understanding
  - Data preparation
  - Modelling
  - Evaluation
  - Deployment
- Machine learning algorithms
- Choosing appropriate algorithm to the problem
- Overfitting and bias-variance tradeoff in ML
ML libraries and programming languages
- Why use a programming language
- Choosing between R and Python
- Python crash course
- Python resources
- Python Libraries for Machine learning
- Jupyter notebooks and interactive coding
Testing ML algorithms
- Generalization and overfitting
- Avoiding overfitting
  - Holdout method
  - Cross-Validation
  - Bootstrapping
- Evaluating numerical predictions
  - Measures of accuracy: ME, MSE, RMSE, MAPE
  - Parameter and prediction stability
- Evaluating classification algorithms
  - Accuracy and its problems
  - The confusion matrix
  - Unbalanced classes problem
- Visualizing model performance
  - Profit curve
  - ROC curve
  - Lift curve
- Model selection
- Model tuning – grid search strategies
- Examples in Python
Data preparation
- Data import and storage
- Understand the data – basic explorations
- Data manipulations with pandas library
- Data transformations – Data wrangling
- Exploratory analysis
- Missing observations – detection and solutions
- Outliers – detection and strategies
- Standarization, normalization, binarization
- Qualitative data recoding
- Examples in Python
Classification
- Binary vs multiclass classification
- Classification via mathematical functions
  - Linear discriminant functions
  - Quadratic discriminant functions
- Logistic regression and probability approach
- k-nearest neighbors
- Naïve Bayes
- Decision trees
  - CART
  - Bagging
  - Random Forests
  - Boosting
  - Xgboost
- Support Vector Machines and kernels
  - Maximal Margin Classifier
  - Support Vector Machine
- Ensemble learning
- Examples in Python
Regression and numerical prediction
- Least squares estimation
- Variables selection techniques
- Regularization and stability- L1, L2
- Nonlinearities and generalized least squares
- Polynomial regression
- Regression splines
- Regression trees
- Examples in Python
Unsupervised learning
- Clustering
  - Centroid-based clustering – k-means, k-medoids, PAM, CLARA
  - Hierarchical clustering – Diana, Agnes
  - Model-based clustering - EM
  - Self organising maps
  - Clusters evaluation and assessment
- Dimensionality reduction
  - Principal component analysis and factor analysis
  - Singular value decomposition
- Multidimensional Scaling
- Examples in Python
Text mining
- Preprocessing data
- The bag-of-words model
- Stemming and lemmization
- Analyzing word frequencies
- Sentiment analysis
- Creating word clouds
- Examples in Python
Recommendations engines and collaborative filtering
- Recommendation data
- User-based collaborative filtering
- Item-based collaborative filtering
- Examples in Python
Association pattern mining
- Frequent itemsets algorithm
- Market basket analysis
- Examples in Python
Outlier Analysis
- Extreme value analysis
- Distance-based outlier detection
- Density-based methods
- High-dimensional outlier detection
- Examples in Python
Machine Learning case study
- Business problem understanding
- Data preprocessing
- Algorithm selection and tuning
- Evaluation of findings
- Deployment

Testimonials

★★★★★

Він демонструє багато методів з попередньо підготовленими сценаріями - дуже добре підготовлені матеріали і легко відстежується

Kamila Begej - GE Medical Systems Polska Sp. Zoo

Machine Translated

Мені подобається, що навчання було зосереджено на прикладах і кодуванні. Я думав, що неможливо скласти стільки вмісту на три дні навчання, але я помилявся. Навчання охоплювало багато тем, і все робилося дуже детально (особливо налаштування параметрів моделі - я не очікував, що буде час для цього, і я був дуже здивований).

Bartosz Rosiek - GE Medical Systems Polska Sp. Zoo

Machine Translated