Machine Learning – Data science Training Course

Last updated

Course Code

ML_LBG

Duration

21 hours (usually 3 days including breaks)

Requirements

Knowledge and awareness of Machine Learning fundamentals

Overview

This classroom based training session will explore machine learning tools with (suggested) Python. Delegates will have computer based examples and case study exercises to undertake.

Course Outline

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. Text mining
    • Preprocessing data
    • The bag-of-words model
    • Stemming and lemmization
    • Analyzing word frequencies
    • Sentiment analysis
    • Creating word clouds
    • Examples in Python
  9. Recommendations engines and collaborative filtering
    • Recommendation data
    • User-based collaborative filtering
    • Item-based collaborative filtering
    • Examples in Python
  10. Association pattern mining
    • Frequent itemsets algorithm
    • Market basket analysis
    • Examples in Python
  11. Outlier Analysis
    • Extreme value analysis
    • Distance-based outlier detection
    • Density-based methods
    • High-dimensional outlier detection
    • Examples in Python
  12. Machine Learning case study
    • Business problem understanding
    • Data preprocessing
    • Algorithm selection and tuning
    • Evaluation of findings
    • Deployment

 

 

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