Exploratory Data Analysis for Machine Learning

This course is part of multiple programs. Learn more

Instructors: Joseph Santarcangelo +1 more

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Skills you'll gain

  •   Scalability
  •   Anomaly Detection
  •   Probability & Statistics
  •   Data Manipulation
  •   Statistical Analysis
  •   Data Transformation
  •   Data Analysis
  •   Data Access
  •   Exploratory Data Analysis
  •   Statistical Inference
  •   Workflow Management
  •   Data Processing
  •   Machine Learning
  •   Data Cleansing
  •   Feature Engineering
  • There are 5 modules in this course

    By the end of this course you should be able to: Retrieve data from multiple data sources: SQL, NoSQL databases, APIs, Cloud  Describe and use common feature selection and feature engineering techniques Handle categorical and ordinal features, as well as missing values Use a variety of techniques for detecting and dealing with outliers Articulate why feature scaling is important and use a variety of scaling techniques   Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience  with Machine Learning and Artificial Intelligence in a business setting.   What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Calculus, Linear Algebra, Probability, and Statistics.

    Retrieving and Cleaning Data

    Exploratory Data Analysis and Feature Engineering

    Inferential Statistics and Hypothesis Testing

    Final Project

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