Comprehensive Machine Learning


Course Number: PYTH-126

Duration: 4 days (26 hours)

Format: Live, hands-on

Machine Learning Training Overview

Accelebrate's Comprehensive Machine Learning class builds on our Comprehensive Data Science with Python class and teaches attendees how to write machine learning applications in Python.

Location and Pricing

Accelebrate offers instructor-led enterprise training for groups of 3 or more online or at your site. Most Accelebrate classes can be flexibly scheduled for your group, including delivery in half-day segments across a week or set of weeks. To receive a customized proposal and price quote for private corporate training on-site or online, please contact us.

Objectives

  • Understanding machine learning as a useful tool for predictive models
  • Know when to reach for machine learning as a tool
  • Implementing data preprocessing for an ML workflow
  • Understand the difference between supervised and unsupervised tasks
  • Implement several classification algorithms
  • Evaluate model performance using a variety of metrics
  • Compare models across a workflow
  • Implement regression algorithm variations
  • Understand clustering approaches to data
  • Interpret labels generated from clustering
  • Learn the fundamental theory behind neural networks
  • Use a neural network to model an arbitrary function
  • Practice reading the loss metrics output from a neural net
  • Apply a neural net to a regression problem
  • Understand regularization within the context of ANNs
  • Implement dropout as a regularization strategy
  • Apply an ANN to a classification problem
  • Extend networks architectures with convolution layers
  • Perform a multi class classification
  • Transform unstructured text data into structured data
  • Understand text-specific data preparation
  • Visualize frequency data from text sources
  • Perform topic modeling on a collection of documents
  • Use labeled text to perform document classification

Prerequisites

All attendees should have completed the Comprehensive Data Science with Python class or have equivalent experience.

Outline

Expand All | Collapse All

An Overview of Machine Learning
  • Machine Learning Theory
  • Data pre-processing
    • Missing Data
    • Dummy Coding
    • Standardization
    • Data Validation Strategies
  • Supervised Versus Unsupervised Learning
Supervised Learning: Regression
  • Linear Regression
  • Penalized Linear Regression
  • Stochastic Gradient Descent
  • Decision Tree Regressor
  • Random Forest Regression
  • Gradient Boosting Regressor
  • Scoring New Data Sets
  • Cross Validation
  • Variance-Bias Tradeoff
  • Feature Importance
Supervised Learning: Classification
  • Logistic Regression
  • LASSO
  • Support Vector Machine
  • Random Forest
  • Ensemble Methods
  • Feature Importance
  • Scoring New Data Sets
  • Cross Validation
Unsupervised Learning: Clustering
  • Preparing Data for Ingestion
  • K-Means Clustering
  • Visualizing Clusters
  • Comparison of Clustering Methods
  • Agglomerative Clustering and DBSCAN
  • Evaluating Cluster Performance with Silhouette Scores
  • Scaling
  • Mean Shift, Affinity Propagation and Birch
Introduction to Artificial Neural Networks (ANNs) and Deep Learning
  • Components of Neural Network Architecture
  • Evaluate Neural Network Fit on a Known Function
  • Define and Monitor Convergence of a Neural Network
  • Evaluating Models
  • Scoring New Datasets with a Model
Constructing Deep Learning Models
  • Preprocessing Tabular Datasets for Deep Learning Workflows
  • Data Validation Strategies
  • Architecture Modifications for Managing Over-fitting
  • Regularization Strategies
  • Deep Learning Classification Models
  • Deep Learning Regression Models
Beyond Feed-forward Neural Network Architectures
  • Identify Limitations of Feed-forward ANN architecture
  • Modify Model Architecture to Include Recurrent (RNN) Components
  • Preprocessing Time Series Data for Ingestion into RNN Models
  • Applying Recurrent Models to Time Series Data
  • Applying 1D Convolutions to Time Series Data
  • Preprocessing Image Data for ANN Models
  • Applying 2D Convolutional Architectures to Image Data
Deep Learning for Image Classification
  • Image data is multidimensional
  • Convolution layers act as filters
  • Pooling layers reduce computation
  • Data augmentation through image transformation for smaller datasets
  • Image transformation using the pillow library
  • Applying a model to a multi class labeled dataset
  • Evaluating a confusion matrix for multiple classes
Introduction to Natural Language Processing (NLP)
  • Transforming Raw Text Data into a Corpus of Documents
  • Identifying Methods for Representing Text Data
  • Transformations of Text Data
  • Summarizing a Corpus into a TF—IDF Matrix
  • Visualizing Word Frequencies
NLP Normalization, Parts-of-speech and Topic Modeling
  • Installing And Accessing Sample Text Corpora
  • Tokenizing Text
  • Cleaning/Processing Tokens
  • Segmentation
  • Tagging And Categorizing Tokens
  • Stopwords
  • Vectorization Schemes for Representing Text
  • Parts-of-speech (POS) Tagging
  • Sentiment Analysis 
  • Topic Modeling with Latent Semantic Analysis
NLP and Machine Learning
  • Unsupervised Machine Learning and Text Data
  • Topic Modeling via Clustering
  • Supervised Machine Learning Applications in NLP
Conclusion

Training Materials:

All attendees receive courseware covering the topics in the class.

Software Requirements:

  • Windows, Mac, or Linux with at least 8 GB RAM
  • A current version of Anaconda for Python 3.x
  • Related lab files that Accelebrate will provide


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