Course Outline

Introduction to Applied Machine Learning

  • Statistical learning vs. Machine learning
  • Iteration and evaluation
  • Bias-Variance trade-off
  • Supervised vs Unsupervised Learning
  • Problems solved with Machine Learning
  • Train Validation Test – ML workflow to avoid overfitting
  • Workflow of Machine Learning
  • Machine learning algorithms
  • Choosing appropriate algorithm to the problem

Algorithm Evaluation

  • 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

Data preparation for Modelling

  • 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

Machine learning algorithms for Outlier detection

  • Supervised algorithms
    • KNN
    • Ensemble Gradient Boosting
    • SVM
  • Unsupervised algorithms
    • Distance-based
    • Density based methods
    • Probabilistic methods
    • Model based methods

Understanding Deep Learning

  • Overview of the Basic Concepts of Deep Learning
  • Differentiating Between Machine Learning and Deep Learning
  • Overview of Applications for Deep Learning

Overview of Neural Networks

  • What are Neural Networks
  • Neural Networks vs Regression Models
  • Understanding Mathematical Foundations and Learning Mechanisms
  • Constructing an Artificial Neural Network
  • Understanding Neural Nodes and Connections
  • Working with Neurons, Layers, and Input and Output Data
  • Understanding Single Layer Perceptrons
  • Differences Between Supervised and Unsupervised Learning
  • Learning Feedforward and Feedback Neural Networks
  • Understanding Forward Propagation and Back Propagation

Building Simple Deep Learning Models with Keras

  • Creating a Keras Model
  • Understanding Your Data
  • Specifying Your Deep Learning Model
  • Compiling Your Model
  • Fitting Your Model
  • Working with Your Classification Data
  • Working with Classification Models
  • Using Your Models 

Working with TensorFlow for Deep Learning

  • Preparing the Data
    • Downloading the Data
    • Preparing Training Data
    • Preparing Test Data
    • Scaling Inputs
    • Using Placeholders and Variables
  • Specifying the Network Architecture
  • Using the Cost Function
  • Using the Optimizer
  • Using Initializers
  • Fitting the Neural Network
  • Building the Graph
    • Inference
    • Loss
    • Training
  • Training the Model
    • The Graph
    • The Session
    • Train Loop
  • Evaluating the Model
    • Building the Eval Graph
    • Evaluating with Eval Output
  • Training Models at Scale
  • Visualizing and Evaluating Models with TensorBoard 

Application of Deep Learning in Anomaly Detection

  • Autoencoder
    • Encoder - Decoder Architecture
    • Reconstruction loss
  • Variational Autencoder
    • Variational inference
  • Generative Adversarial Network
    • Generator – Discriminator architecture
    • Approaches to AN using GAN

Ensemble Frameworks

  • Combining results from different methods
  • Bootstrap Aggregating
  • Averaging outlier score

 

Requirements

  • Experience with Python programming
  • Basic familiarity with statistics and mathematical concepts

Audience

  • Developers
  • Data scientists
  28 Hours
 

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Dates are subject to availability and take place between 09:30 and 16:30.
Open Training Courses require 5+ participants.

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