Course Outline

Machine Learning Algorithms in Julia

Introductory concepts

  • Supervised & unsupervised learning
  • Cross validation and model selection
  • Bias/variance tradeoff

Linear & logistic regression

(NaiveBayes & GLM)

  • Introductory concepts
  • Fitting linear regression models
  • Model diagnostics
  • Naive Bayes
  • Fitting a logistic regression model
  • Model disgnostics
  • Model selection methods

Distances

  • What is a distance?
  • Euclidean
  • Cityblock
  • Cosine
  • Correlation
  • Mahalanobis
  • Hamming
  • MAD
  • RMS
  • Mean squared deviation

Dimensionality reduction

  • Principal Component Analysis (PCA)
    • Linear PCA
    • Kernel PCA
    • Probabilistic PCA
    • Independent CA
  • Multidimensional scaling

Altered regression methods

  • Basic concepts of regularization
  • Ridge regression
  • Lasso regression
  • Principal component regression (PCR)

Clustering

  • K-means
  • K-medoids
  • DBSCAN
  • Hierarchical clustering
  • Markov Cluster Algorithm
  • Fuzzy C-means clustering

Standard machine learning models

(NearestNeighbors, DecisionTree, LightGBM, XGBoost, EvoTrees, LIBSVM packages)

  • Gradient boosting concepts
  • K nearest neighbours (KNN)
  • Decision tree models
  • Random forest models
  • XGboost
  • EvoTrees
  • Support vector machines (SVM)

Artificial neural networks

(Flux package)

  • Stochastic gradient descent & strategies
  • Multilayer perceptrons forward feed & back propagation
  • Regularization
  • Recurrence neural networks (RNN)
  • Convolutional neural networks (Convnets)
  • Autoencoders
  • Hyperparameters

Requirements

This course is intended for people that already have a background in data science and statistics.

  21 Hours
 

Testimonials

Related Courses

AdaBoost Python for Machine Learning

  14 hours

Artificial Intelligence (AI) with H2O

  14 hours

AutoML with Auto-Keras

  14 hours

AutoML

  14 hours

Google Cloud AutoML

  7 hours

AutoML with Auto-sklearn

  14 hours

Pattern Recognition

  21 hours

DataRobot

  7 hours

Data Mining with Weka

  14 hours

H2O AutoML

  14 hours

Machine Learning for Mobile Apps using Google’s ML Kit

  14 hours

Pattern Matching

  14 hours

Machine Learning with Random Forest

  14 hours

RapidMiner for Machine Learning and Predictive Analytics

  14 hours

Apache SystemML for Machine Learning

  14 hours