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Feature Engineering Made Easy

  • Course Code: Data Analysis / BI - Feature Engineering Made Easy
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 2 Days Audience: This course is geared for experienced developers, analysts or others who wants a perfect guide to speed up the predicting power of Machine Learning Algorithms

Course Snapshot 

  • Duration: 2 days 
  • Skill-level: Foundation-level Feature Engineering skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for experienced developers, analysts or others who wants a perfect guide to speed up the predicting power of Machine Learning Algorithms 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Feature engineering is the most important step in creating powerful machine learning systems. This course will take you through the entire feature-engineering journey to make your machine learning much more systematic and effective. You will start with understanding your data—often the success of your ML models depends on how you leverage different feature types, such as continuous, categorical, and more, You will learn when to include a feature, when to omit it, and why, all by understanding error analysis and the acceptability of your models. You will learn to convert a problem statement into useful new features. You will learn to deliver features driven by business needs as well as mathematical insights. You’ll also learn how to use machine learning on your machines, automatically learning amazing features for your data. By the end of the course, you will become proficient in Feature Selection, Feature Learning, and Feature Optimization. 

Working in a hands-on learning environment, led by our Feature Engineering expert instructor, students will learn about and explore: 

  • Design, discover, and create dynamic, efficient features for your machine learning application 
  • Understand your data in-depth and derive astonishing data insights with the help of this Guide 
  • Grasp powerful feature-engineering techniques and build machine learning systems 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Identify and leverage different feature types 
  • Clean features in data to improve predictive power 
  • Understand why and how to perform feature selection, and model error analysis 
  • Leverage domain knowledge to construct new features 
  • Deliver features based on mathematical insights 
  • Use machine-learning algorithms to construct features 
  • Master feature engineering and optimization 
  • Harness feature engineering for real world applications through a structured case study  

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who wants a perfect guide to speed up the predicting power of Machine Learning Algorithms. 

Pre-Requisites:  Students should have  

  • developers with some knowledge of Python.  

Course Agenda / Topics 

  1. Introduction to Feature Engineering 
  • Introduction to Feature Engineering 
  • Motivating example – AI-powered communications 
  • Why feature engineering matters 
  • What is feature engineering? 
  • Evaluation of machine learning algorithms and feature engineering procedures 
  • Feature understanding – what’s in my dataset? 
  • Feature improvement – cleaning datasets 
  • Feature selection – say no to bad attributes 
  • Feature construction – can we build it? 
  • Feature transformation – enter math-man 
  • Feature learning – using AI to better our AI 
  1. Feature Understanding – What’s in My Dataset? 
  • Feature Understanding – What’s in My Dataset? 
  • The structure, or lack thereof, of data 
  • An example of unstructured data – server logs 
  • Quantitative versus qualitative data 
  • The four levels of data 
  • Recap of the levels of data 
  1. Feature Improvement – Cleaning Datasets 
  • Feature Improvement – Cleaning Datasets 
  • Identifying missing values in data 
  • Dealing with missing values in a dataset 
  • Standardization and normalization 
  1. Feature Construction 
  • Feature Construction 
  • Examining our dataset 
  • Imputing categorical features 
  • Encoding categorical variables 
  • Extending numerical features 
  • Text-specific feature construction 
  1. Feature Selection 
  • Feature Selection 
  • Achieving better performance in feature engineering 
  • Creating a baseline machine learning pipeline 
  • The types of feature selection 
  • Choosing the right feature selection method 
  1. Feature Transformations 
  • Feature Transformations 
  • Dimension reduction – feature transformations versus feature selection versus feature construction 
  • Principal Component Analysis 
  • Scikit-learn’s PCA 
  • How centering and scaling data affects PCA 
  • A deeper look into the principal components 
  • Linear Discriminant Analysis 
  • LDA versus PCA – iris dataset 
  1. Feature Learning 
  • Feature Learning 
  • Parametric assumptions of data 
  • Restricted Boltzmann Machines 
  • The BernoulliRBM 
  • Extracting RBM components from MNIST 
  • Using RBMs in a machine learning pipeline 
  • Learning text features – word vectorizations 
  1. Case Studies 
  • Case Studies 
  • Case study 1 – facial recognition 
  • Case study 2 – predicting topics of hotel reviews data 
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