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Exploratory Data Analysis for Machine Learning
  • Level Professional
  • Duration 14 hours
  • Course by IBM
  • Offered by

About

This first course in the IBM Machine Learning Professional Certificate introduces you to Machine Learning and the content of the professional certificate. In this course you will realize the importance of good, quality data. You will learn common techniques to retrieve your data, clean it, apply feature engineering, and have it ready for preliminary analysis and hypothesis testing. 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.

Modules

Course Introduction

    • 1 Videos
    • 1 Readings
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1 Videos

  • Course Introduction

1 Readings

  • Course Prerequisites

Introduction to Artificial Intelligence and Machine Learning

    • 6 Videos
    • 1 Assignment
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1 Assignment

  • Practice Quiz: Artificial Intelligence and Machine Learning

6 Videos

  • Introduction to Artificial Intelligence and Machine Learning
  • Machine Learning and Deep Learning
  • Machine Learning and Deep Learning - Part 1
  • Machine Learning and Deep Learning - Part 2
  • History of AI
  • History of Machine Learning and Deep Learning

Modern AI: Applications and the Machine Learning Workflow

    • 3 Videos
    • 1 Discussion
    • 1 Assignment
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1 Assignment

  • Practice Quiz: Modern AI Applications and Workflows

1 Discussions

  • Optional: Say hi or reach out for help

3 Videos

  • Modern AI
  • Applications
  • Machine Learning Workflow

End of the module review & evaluation

    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Graded Quiz: Module 1 - Modern AI and its Applications

1 Readings

  • Review

Retrieving Data

    • 4 Videos
    • 2 Readings
    • 2 ExternalTool
    • 1 Assignment
Show info about module content

1 Assignment

  • Practice Quiz: Retrieving Data

2 External Tool

  • Demo Lab: Reading Data in Database Files - Part A
  • Demo Lab: Reading Data in Jupyter Notebook - Part B

4 Videos

  • Retrieving Data from CSV and JSON Files
  • Retrieving Data from Databases, APIs, and the Cloud
  • [Optional] Lab Solution: Reading Data Jupyter Notebook - Part A
  • [Optional]Lab Solution: Reading in Database Files - Part B

2 Readings

  • [Optional] Download Assets for Lab: Reading Data in Database Files - Part A
  • [Optional] Download Assets for Lab: Reading Data in Jupyter Notebook - Part B

Data Cleaning

    • 3 Videos
    • 1 ExternalTool
    • 1 Assignment
Show info about module content

1 Assignment

  • Practice Quiz: Data Cleaning

1 External Tool

  • Practice Lab: Data Cleaning

3 Videos

  • Data Cleaning
  • Handling Missing Values and Outliers
  • Handling Missing Values and Outliers using Residuals

End of the module review & evaluation

    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Graded Quiz: Module 2 - Retrieving Data and Cleaning Data

1 Readings

  • Summary/Review

Exploratory Data Analysis

    • 7 Videos
    • 1 Readings
    • 2 ExternalTool
    • 1 Assignment
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1 Assignment

  • Practice Quiz: Exploratory Data Analysis

2 External Tool

  • Demo Lab: Exploratory Data Analysis
  • Practice Lab: Exploratory Data Analysis

7 Videos

  • Introduction to Exploratory Data Analysis (EDA)
  • EDA with Visualization
  • Grouping Data for EDA
  • [Optional]Solution: EDA Notebook - Part 1
  • [Optional]Solution: EDA Notebook - Part 2
  • [Optional]Solution: EDA Notebook - Part 3
  • [Optional]Solution: EDA Notebook - Part 4

1 Readings

  • [Optional] Download Assets for Lab: Exploratory Data Analysis Lab

Feature Engineering and Variable Transformation

    • 8 Videos
    • 1 Readings
    • 2 ExternalTool
    • 1 Assignment
Show info about module content

1 Assignment

  • Practice Quiz: Feature Engineering and Variable Transformation

2 External Tool

  • Demo Lab: Feature Engineering
  • Practice Lab: Feature Engineering

8 Videos

  • Feature Engineering and Variable Transformation - Background
  • Variable Transformation
  • Feature Encoding
  • Feature Scaling
  • Common Variable Transformations in Python
  • [Optional] Solution: Feature Engineering Lab - Part 1
  • [Optional] Solution: Feature Engineering Lab - Part 2
  • [Optional] Solution: Feature Engineering Lab - Part 3

1 Readings

  • [Optional] Download Assets for Lab: Feature Engineering Demo

End of module review and evaluation

    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Graded Quiz: Module 3 - Exploratory Data Analysis and Feature Engineering

1 Readings

  • Summary/Review

Estimation and Inference, and Hypothesis Testing

    • 5 Videos
    • 1 Assignment
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1 Assignment

  • Practice Quiz: Estimation and Inference, and Hypothesis Testing

5 Videos

  • Estimation and Inference - Introduction
  • Estimation and Inference - Example
  • Estimation and Inference - Parametric vs. Non-Parametric
  • Estimation and Inference - Commonly Used Distributions
  • Frequentist vs. Bayesian Statistics

Hypothesis Testing

    • 11 Videos
    • 1 Readings
    • 2 ExternalTool
    • 1 Assignment
Show info about module content

1 Assignment

  • Practice Quiz: Hypothesis Testing

2 External Tool

  • Demo Lab: Hypothesis Testing
  • Practice Lab: Hypothesis Testing

11 Videos

  • Introduction to Hypothesis
  • Hypothesis Testing Example
  • Bayesian Interpretation of Hypothesis Testing Example
  • Type 1 vs Type 2 Error
  • Type 1 vs Type 2 Error: Examples
  • Hypothesis Testing Terminology
  • Significance Level and P-Values
  • Significance Level and P-Values and the F Statistic
  • [Optional] Hypothesis Testing Demo - Part 1
  • [Optional] Hypothesis Testing Demo - Part 2
  • Correlation vs Causation

1 Readings

  • [Optional] Download Assets for Lab: Hypothesis Testing Demo

End of module review & evaluation

    • 1 Readings
    • 1 Discussion
    • 1 Assignment
Show info about module content

1 Assignment

  • Graded Quiz: Module 4 - Inferential Statistics and Hypothesis Testing

1 Discussions

  • Optional Brainstorming

1 Readings

  • Summary/Review

Final Project

    • 1 Readings
    • 1 PeerReview
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1 Peer Review

  • Course Project - Peer Review

1 Readings

  • Project Overview

Auto Summary

This IBM Machine Learning Professional Certificate course on Coursera focuses on exploratory data analysis in the domain of Data Science & AI. Taught by expert instructors, it covers data retrieval, cleaning, feature engineering, and preliminary analysis techniques. Ideal for aspiring data scientists with Python and fundamental math skills, the course lasts 840 minutes and offers Starter, Professional, and Paid subscription options.

Joseph Santarcangelo
Instructors

Joseph Santarcangelo

Svitlana (Lana) Kramar
Instructors

Svitlana (Lana) Kramar