Machine Learning – Data science Schulung

Alle Preise zzgl. MwSt

Kurs Code

ML_LBG

Dauer

21 hours (üblicherweise 3 Tage inklusive Pausen)

Voraussetzungen

Knowledge and awareness of Machine Learning fundamentals

Überblick

In dieser auf Klassenräumen basierenden Schulungssitzung werden maschinelle Lernwerkzeuge mit (empfohlenem) Python . Die Teilnehmer erhalten computergestützte Beispiele und Fallstudien.

Machine Translated

Schulungsübersicht

  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

 

 

Erfahrungsberichte

★★★★★
★★★★★

Verwandte Kategorien

Kombinierte Kurse

Sonderangebote

Sonderangebote Newsletter

Wir behandeln Ihre Daten vertraulich und werden sie nicht an Dritte weitergeben.
Sie können Ihre Einstellungen jederzeit ändern oder sich ganz abmelden.

EINIGE UNSERER KUNDEN

is growing fast!

We are looking for a good mixture of IT and soft skills in Switzerland!

As a NobleProg Trainer you will be responsible for:

  • delivering training and consultancy Worldwide
  • preparing training materials
  • creating new courses outlines
  • delivering consultancy
  • quality management

At the moment we are focusing on the following areas:

  • Statistic, Forecasting, Big Data Analysis, Data Mining, Evolution Alogrithm, Natural Language Processing, Machine Learning (recommender system, neural networks .etc...)
  • SOA, BPM, BPMN
  • Hibernate/Spring, Scala, Spark, jBPM, Drools
  • R, Python
  • Mobile Development (iOS, Android)
  • LAMP, Drupal, Mediawiki, Symfony, MEAN, jQuery
  • You need to have patience and ability to explain to non-technical people

To apply, please create your trainer-profile by going to the link below:

Apply now!

This site in other countries/regions