Machine Learning & Deep Learning with Python and R Training Course

Course CodeCourse Code

deepmclrg

Duration Duration

14 hours (usually 2 days including breaks)

Course OutlineCourse Outline

MACHINE LEARNING

1: Introducing Machine Learning

  • The origins of machine learning
  • Uses and abuses of machine learning
  • Ethical considerations
  • How do machines learn?
  • Abstraction and knowledge representation
  • Generalization
  • Assessing the success of learning
  • Steps to apply machine learning to your data
  • Choosing a machine learning algorithm
  • Thinking about the input data
  • Thinking about types of machine learning algorithms
  • Matching your data to an appropriate algorithm
  • Using R for machine learning
  • Installing and loading R packages
  • Installing an R package
  • Installing a package using the point-and-click interface
  • Loading an R package
  • Summary

2: Managing and Understanding Data

  • R data structures
  • Vectors
  • Factors
  • Lists
  • Data frames
  • Matrixes and arrays
  • Managing data with R
  • Saving and loading R data structures
  • Importing and saving data from CSV files
  • Importing data from SQL databases
  • Exploring and understanding data
  • Exploring the structure of data
  • Exploring numeric variables
  • Measuring the central tendency – mean and median
  • Measuring spread – quartiles and the five-number summary
  • Visualizing numeric variables – boxplots
  • Visualizing numeric variables – histograms
  • Understanding numeric data – uniform and normal distributions
  • Measuring spread – variance and standard deviation
  • Exploring categorical variables
  • Measuring the central tendency – the mode
  • Exploring relationships between variables
  • Visualizing relationships – scatterplots
  • Examining relationships – two-way cross-tabulations
  • Summary

3: Lazy Learning – Classification Using Nearest Neighbors

  • Understanding classification using nearest neighbors
  • The kNN algorithm
  • Calculating distance
  • Choosing an appropriate k
  • Preparing data for use with kNN
  • Why is the kNN algorithm lazy?
  • Diagnosing breast cancer with the kNN algorithm
    • Step 1 – collecting data
    • Step 2 – exploring and preparing the data
  • Transformation – normalizing numeric data
  • Data preparation – creating training and test datasets
    • Step 3 – training a model on the data
    • Step 4 – evaluating model performance
    • Step 5 – improving model performance
  • Transformation – z-score standardization
  • Testing alternative values of k
  • Summary

4: Probabilistic Learning – Classification Using

  • Naive Bayes
  • Understanding naive Bayes
  • Basic concepts of Bayesian methods
  • Probability
  • Joint probability
  • Conditional probability with Bayes' theorem
  • The naive Bayes algorithm
  • The naive Bayes classification
  • The Laplace estimator
  • Using numeric features with naive Bayes
  • Example – filtering mobile phone spam with the naive Bayes algorithm
    • Step 1 – collecting data
    • Step 2 – exploring and preparing the data
  • Data preparation – processing text data for analysis
  • Data preparation – creating training and test datasets
  • Visualizing text data – word clouds
  • Data preparation – creating indicator features for frequent words
    • Step 3 – training a model on the data
    • Step 4 – evaluating model performance
    • Step 5 – improving model performance
  • Summary

5: Divide and Conquer – Classification Using

  • Decision Trees and Rules
  • Understanding decision trees
  • Divide and conquer
  • The C5.0 decision tree algorithm
  • Choosing the best split
  • Pruning the decision tree
  • Example – identifying risky bank loans using C5.0 decision trees
    • Step 1 – collecting data
    • Step 2 – exploring and preparing the data
  • Data preparation – creating random training and test datasets
    • Step 3 – training a model on the data
    • Step 4 – evaluating model performance
    • Step 5 – improving model performance
  • Boosting the accuracy of decision trees
  • Making some mistakes more costly than others
  • Understanding classification rules
  • Separate and conquer
  • The One Rule algorithm
  • The RIPPER algorithm
  • Rules from decision trees
  • Example – identifying poisonous mushrooms with rule learners
    • Step 1 – collecting data
    • Step 2 – exploring and preparing the data
    • Step 3 – training a model on the data
    • Step 4 – evaluating model performance
    • Step 5 – improving model performance
  • Summary

6: Forecasting Numeric Data – Regression Methods

  • Understanding regression
  • Simple linear regression
  • Ordinary least squares estimation
  • Correlations
  • Multiple linear regression
  • Example – predicting medical expenses using linear regression
    • Step 1 – collecting data
    • Step 2 – exploring and preparing the data
  • Exploring relationships among features – the correlation matrix
  • Visualizing relationships among features – the scatterplot matrix
    • Step 3 – training a model on the data
    • Step 4 – evaluating model performance
    • Step 5 – improving model performance
  • Model specification – adding non-linear relationships
  • Transformation – converting a numeric variable to a binary indicator
  • Model specification – adding interaction effects
  • Putting it all together – an improved regression model
  • Understanding regression trees and model trees
  • Adding regression to trees
  • Example – estimating the quality of wines with regression trees
  • and model trees
    • Step 1 – collecting data
    • Step 2 – exploring and preparing the data
    • Step 3 – training a model on the data
  • Visualizing decision trees
    • Step 4 – evaluating model performance
  • Measuring performance with mean absolute error
    • Step 5 – improving model performance
  • Summary

7: Black Box Methods – Neural Networks and

  • Support Vector Machines
  • Understanding neural networks
  • From biological to artificial neurons
  • Activation functions
  • Network topology
  • The number of layers
  • The direction of information travel
  • The number of nodes in each layer
  • Training neural networks with backpropagation
  • Modeling the strength of concrete with ANNs
    • Step 1 – collecting data
    • Step 2 – exploring and preparing the data
    • Step 3 – training a model on the data
    • Step 4 – evaluating model performance
    • Step 5 – improving model performance
  • Understanding Support Vector Machines
  • Classification with hyperplanes
  • Finding the maximum margin
  • The case of linearly separable data
  • The case of non-linearly separable data
  • Using kernels for non-linear spaces
  • Performing OCR with SVMs
    • Step 1 – collecting data
    • Step 2 – exploring and preparing the data
    • Step 3 – training a model on the data
    • Step 4 – evaluating model performance
    • Step 5 – improving model performance
  • Summary

8: Finding Patterns – Market Basket Analysis Using

  • Association Rules
  • Understanding association rules
  • The Apriori algorithm for association rule learning
  • Measuring rule interest – support and confidence
  • Building a set of rules with the Apriori principle
  • Example – identifying frequently purchased groceries with
  • association rules
    • Step 1 – collecting data
    • Step 2 – exploring and preparing the data
  • Data preparation – creating a sparse matrix for transaction data
  • Visualizing item support – item frequency plots
  • Visualizing transaction data – plotting the sparse matrix
    • Step 3 – training a model on the data
    • Step 4 – evaluating model performance
    • Step 5 – improving model performance
  • Sorting the set of association rules
  • Taking subsets of association rules
  • Saving association rules to a file or data frame
  • Summary

9: Finding Groups of Data – Clustering with k-means

  • Understanding clustering
  • Clustering as a machine learning task
  • The k-means algorithm for clustering
  • Using distance to assign and update clusters
  • Choosing the appropriate number of clusters
  • Finding teen market segments using k-means clustering
    • Step 1 – collecting data
    • Step 2 – exploring and preparing the data
  • Data preparation – dummy coding missing values
  • Data preparation – imputing missing values
    • Step 3 – training a model on the data
    • Step 4 – evaluating model performance
    • Step 5 – improving model performance
  • Summary

10: Evaluating Model Performance

  • Measuring performance for classification
  • Working with classification prediction data in R
  • A closer look at confusion matrices
  • Using confusion matrices to measure performance
  • Beyond accuracy – other measures of performance
  • The kappa statistic
  • Sensitivity and specificity
  • Precision and recall
  • The F-measure
  • Visualizing performance tradeoffs
  • ROC curves
  • Estimating future performance
  • The holdout method
  • Cross-validation
  • Bootstrap sampling
  • Summary

11: Improving Model Performance

  • Tuning stock models for better performance
  • Using caret for automated parameter tuning
  • Creating a simple tuned model
  • Customizing the tuning process
  • Improving model performance with meta-learning
  • Understanding ensembles
  • Bagging
  • Boosting
  • Random forests
  • Training random forests
  • Evaluating random forest performance
  • Summary

DEEP LEARNING with R

1: Getting Started with Deep Learning

  • What is deep learning?
  • Conceptual overview of neural networks
  • Deep neural networks
  • R packages for deep learning
  • Setting up reproducible results
  • Neural networks
  • The deepnet package
  • The darch package
  • The H2O package
  • Connecting R and H2O
  • Initializing H2O
  • Linking datasets to an H2O cluster
  • Summary

2: Training a Prediction Model

  • Neural networks in R
  • Building a neural network
  • Generating predictions from a neural network
  • The problem of overfitting data – the consequences explained
  • Use case – build and apply a neural network
  • Summary

3: Preventing Overfitting

  • L1 penalty
  • L1 penalty in action
  • L2 penalty
  • L2 penalty in action
  • Weight decay (L2 penalty in neural networks)
  • Ensembles and model averaging
  • Use case – improving out-of-sample model performance
  • using dropout
  • Summary

4: Identifying Anomalous Data

  • Getting started with unsupervised learning
  • How do auto-encoders work?
  • Regularized auto-encoders
  • Penalized auto-encoders
  • Denoising auto-encoders
  • Training an auto-encoder in R
  • Use case – building and applying an auto-encoder model
  • Fine-tuning auto-encoder models
  • Summary

5: Training Deep Prediction Models

  • Getting started with deep feedforward neural networks
  • Common activation functions – rectifiers, hyperbolic tangent,
  • and maxout
  • Picking hyperparameters
  • Training and predicting new data from a deep neural network
  • Use case – training a deep neural network for automatic
  • classification
  • Working with model results
  • Summary

6: Tuning and Optimizing Models

  • Dealing with missing data
  • Solutions for models with low accuracy
  • Grid search
  • Random search
  • Summary

DEEP LEARNING WITH PYTHON

I Introduction

1 Welcome

  • Deep Learning The Wrong Way
  • Deep Learning With Python
  • Summary

II Background

2 Introduction to Theano

  • What is Theano?
  • How to Install Theano
  • Simple Theano Example
  • Extensions and Wrappers for Theano
  • More Theano Resources
  • Summary

3 Introduction to TensorFlow

  • What is TensorFlow?
  • How to Install TensorFlow
  • Your First Examples in TensorFlow
  • Simple TensorFlow Example
  • More Deep Learning Models
  • Summary

4 Introduction to Keras

  • What is Keras?
  • How to Install Keras
  • Theano and TensorFlow Backends for Keras
  • Build Deep Learning Models with Keras
  • Summary

5 Project: Develop Large Models on GPUs Cheaply In the Cloud

  • Project Overview
  • Setup Your AWS Account
  • Launch Your Server Instance
  • Login, Configure and Run
  • Build and Run Models on AWS
  • Close Your EC2 Instance
  • Tips and Tricks for Using Keras on AWS
  • More Resources For Deep Learning on AWS
  • Summary

III Multilayer Perceptrons

6 Crash Course In Multilayer Perceptrons

  • Crash Course Overview
  • Multilayer Perceptrons
  • Neurons
  • Networks of Neurons
  • Training Networks
  • Summary

7 Develop Your First Neural Network With Keras

  • Tutorial Overview
  • Pima Indians Onset of Diabetes Dataset
  • Load Data
  • Define Model
  • Compile Model
  • Fit Model
  • Evaluate Model
  • Tie It All Together
  • Summary

8 Evaluate The Performance of Deep Learning Models

  • Empirically Evaluate Network Configurations
  • Data Splitting
  • Manual k-Fold Cross Validation
  • Summary

9 Use Keras Models With Scikit-Learn For General Machine Learning

  • Overview
  • Evaluate Models with Cross Validation
  • Grid Search Deep Learning Model Parameters
  • Summary

10 Project: Multiclass Classification Of Flower Species

  • Iris Flowers Classification Dataset
  • Import Classes and Functions
  • Initialize Random Number Generator
  • Load The Dataset
  • Encode The Output Variable
  • Define The Neural Network Model
  • Evaluate The Model with k-Fold Cross Validation
  • Summary

11 Project: Binary Classification Of Sonar Returns

  • Sonar Object Classification Dataset
  • Baseline Neural Network Model Performance
  • Improve Performance With Data Preparation
  • Tuning Layers and Neurons in The Model
  • Summary

12 Project: Regression Of Boston House Prices

  • Boston House Price Dataset
  • Develop a Baseline Neural Network Model
  • Lift Performance By Standardizing The Dataset
  • Tune The Neural Network Topology
  • Summary

IV Advanced Multilayer Perceptrons and Keras

13 Save Your Models For Later With Serialization

  • Tutorial Overview .
  • Save Your Neural Network Model to JSON
  • Save Your Neural Network Model to YAML
  • Summary

14 Keep The Best Models During Training With Checkpointing

  • Checkpointing Neural Network Models
  • Checkpoint Neural Network Model Improvements
  • Checkpoint Best Neural Network Model Only
  • Loading a Saved Neural Network Model
  • Summary

15 Understand Model Behavior During Training By Plotting History

  • Access Model Training History in Keras
  • Visualize Model Training History in Keras
  • Summary

16 Reduce Overfitting With Dropout Regularization

  • Dropout Regularization For Neural Networks
  • Dropout Regularization in Keras
  • Using Dropout on the Visible Layer
  • Using Dropout on Hidden Layers
  • Tips For Using Dropout
  • Summary

17 Lift Performance With Learning Rate Schedules

  • Learning Rate Schedule For Training Models
  • Ionosphere Classification Dataset
  • Time-Based Learning Rate Schedule
  • Drop-Based Learning Rate Schedule
  • Tips for Using Learning Rate Schedules
  • Summary

V Convolutional Neural Networks

18 Crash Course In Convolutional Neural Networks

  • The Case for Convolutional Neural Networks
  • Building Blocks of Convolutional Neural Networks
  • Convolutional Layers
  • Pooling Layers
  • Fully Connected Layers
  • Worked Example
  • Convolutional Neural Networks Best Practices
  • Summary

19 Project: Handwritten Digit Recognition

  • Handwritten Digit Recognition Dataset
  • Loading the MNIST dataset in Keras
  • Baseline Model with Multilayer Perceptrons
  • Simple Convolutional Neural Network for MNIST
  • Larger Convolutional Neural Network for MNIST
  • Summary

20 Improve Model Performance With Image Augmentation

  • Keras Image Augmentation API
  • Point of Comparison for Image Augmentation
  • Feature Standardization
  • ZCA Whitening
  • Random Rotations
  • Random Shifts
  • Random Flips
  • Saving Augmented Images to File
  • Tips For Augmenting Image Data with Keras
  • Summary

21 Project Object Recognition in Photographs

  • Photograph Object Recognition Dataset
  • Loading The CIFAR-10 Dataset in Keras
  • Simple CNN for CIFAR-10
  • Larger CNN for CIFAR-10
  • Extensions To Improve Model Performance
  • Summary

22 Project: Predict Sentiment From Movie Reviews

  • Movie Review Sentiment Classification Dataset
  • Load the IMDB Dataset With Keras
  • Word Embeddings
  • Simple Multilayer Perceptron Model
  • One-Dimensional Convolutional Neural Network
  • Summary

VI Recurrent Neural Networks

23 Crash Course In Recurrent Neural Networks

  • Support For Sequences in Neural Networks
  • Recurrent Neural Networks
  • Long Short-Term Memory Networks
  • Summary

24 Time Series Prediction with Multilayer Perceptrons

  • Problem Description: Time Series Prediction
  • Multilayer Perceptron Regression
  • Multilayer Perceptron Using the Window Method
  • Summary

25 Time Series Prediction with LSTM Recurrent Neural Networks

  • LSTM Network For Regression
  • LSTM For Regression Using the Window Method
  • LSTM For Regression with Time Steps
  • LSTM With Memory Between Batches
  • Stacked LSTMs With Memory Between Batches
  • Summary

26 Project: Sequence Classification of Movie Reviews

  • Simple LSTM for Sequence Classification
  • LSTM For Sequence Classification With Dropout
  • LSTM and CNN For Sequence Classification
  • Summary

27 Understanding Stateful LSTM Recurrent Neural Networks

  • Problem Description: Learn the Alphabet
  • LSTM for Learning One-Char to One-Char Mapping
  • LSTM for a Feature Window to One-Char Mapping
  • LSTM for a Time Step Window to One-Char Mapping
  • LSTM State Maintained Between Samples Within A Batch
  • Stateful LSTM for a One-Char to One-Char Mapping
  • LSTM with Variable Length Input to One-Char Output
  • Summary

28 Project: Text Generation With Alice in Wonderland

  • Problem Description: Text Generation
  • Develop a Small LSTM Recurrent Neural Network
  • Generating Text with an LSTM Network
  • Larger LSTM Recurrent Neural Network
  • Extension Ideas to Improve the Model
  • Summary

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