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IBM

Machine Learning: Exploratory Data Analysis

IBM via edX

Overview

Kickstart your Machine Learning journey with IBM’s introductory course in the IBM Machine Learning Professional Certificate. This course provides a foundational understanding of Machine Learning concepts and highlights the importance of working with clean, high-quality data. You’ll get hands-on experience retrieving data from multiple sources, including SQL and NoSQL databases, APIs, and cloud platforms.

Throughout the course, you’ll learn essential data preparation techniques such as handling missing values, encoding categorical and ordinal features, identifying and managing outliers, and applying feature engineering and selection methods. You’ll also explore various feature scaling techniques and understand why scaling is critical for Machine Learning models.

By the end of this course, you’ll be able to prepare datasets for analysis and hypothesis testing, setting the stage for successful Machine Learning projects. This course is ideal for aspiring data scientists and machine learning professionals interested in gaining practical experience in AI, especially for real world applications.

To succeed in this course, learners should have experience programming in Python and a basic understanding of calculus, linear algebra, probability, and statistics.

Syllabus

Module 1: A Brief History of Modern AI and its Applications

  • Reading: Learning Objectives

  • Video: Course Introduction

  • Reading: Course Prerequisites

  • Video: Introduction to Artificial Intelligence and Machine Learning

  • Video: Machine Learning and Deep Learning

  • Video: Machine Learning And Deep Learning Part 1

  • Video: Machine Learning and Deep Learning - Part 2

  • Video: History of AI

  • Video: History of Machine Learning and Deep Learning

  • Practice Quiz: Artificial Intelligence and Machine Learning

  • Video: Modern AI

  • Video: Applications

  • Optional: Say hi or reach out for help

  • Video: Machine Learning Workflow

  • Practice Quiz: Modern AI Applications and Workflows

  • End of the module review & evaluation

  • Reading: Review

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

Module 2: Retrieving and Cleaning Data

  • Module Summary and Learning Objectives

  • Video: Retrieving Data from CSV and JSON Files

  • Video: Retrieving Data from Databases, APIs, and the Cloud

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

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

  • Video: [Optional] Lab Solution: Reading Data Jupyter Notebook - Part A

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

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

  • Video: [Optional]Lab Solution: Reading in Database Files - Part B

  • Practice Quiz: Retrieving Data

  • Video: Data Cleaning

  • Video: Handling Missing Values and Outliers

  • Video: Handling Missing Values and Outliers using Residuals

  • Practice Lab: Data Cleaning

  • Practice Quiz: Data Cleaning

  • Reading: Summary/Review

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

Module 3:

  • Module Summary and Learning Objectives

  • Video: Introduction to Exploratory Data Analysis (EDA)

  • Video: EDA with Visualization

  • Video: Grouping Data for EDA

  • App Item: Demo Lab: Exploratory Data Analysis

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

  • Video: [Optional]Solution: EDA Notebook - Part 1

  • Video: [Optional]Solution: EDA Notebook - Part 2

  • Video: [Optional]Solution: EDA Notebook - Part 3

  • Video: [Optional]Solution: EDA Notebook - Part 4

  • Practice Lab: Exploratory Data Analysis

  • Practice Quiz: Exploratory Data Analysis

  • Video: Feature Engineering and Variable Transformation – Background

  • Video: Variable Transformation

  • Video: Feature Encoding

  • Video: Feature Scaling

  • Video: Common Variable Transformations in Python

  • Demo Lab: Feature Engineering

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

  • Video: [Optional] Solution: Feature Engineering Lab - Part 1

  • Video: [Optional] Solution: Feature Engineering Lab - Part 2

  • Video: [Optional] Solution: Feature Engineering Lab - Part 3

  • Practice Lab: Feature Engineering

  • Practice Quiz: Feature Engineering and Variable Transformation

  • Reading: Summary/Review

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

Module 4:

  • Video: Estimation and Inference - Introduction

  • Video: Estimation and Inference – Example

  • Video: Estimation and Inference - Parametric vs. Non-Parametric

  • Video: Estimation and Inference - Commonly Used Distributions

  • Video: Frequentist vs. Bayesian Statistics

  • Practice Quiz: Estimation and Inference, and Hypothesis Testing

  • Video: Introduction to Hypothesis

  • Video: Hypothesis Testing Example

  • Video: Bayesian Interpretation of Hypothesis Testing Example

  • Video: Type 1 vs Type 2 Error

  • Video: Type 1 vs Type 2 Error: Examples

  • Video: Hypothesis Testing Terminology

  • Video: Significance Level and P-Values

  • Video: Significance Level and P-Values and the F Statistic

  • Demo Lab: Hypothesis Testing

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

  • Video: [Optional] Hypothesis Testing Demo - Part 1

  • Video: [Optional] Hypothesis Testing Demo - Part 2

  • Video: Correlation vs Causation

  • Practice Lab: Hypothesis Testing

  • Practice Quiz: Hypothesis Testing

  • Discussion Prompt: Optional Brainstorming

  • Reading: Summary/Review

  • Reading: Summary/Review

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

Module 5:

  • Module Summary and Learning Objectives

  • Reading: Project Overview

  • Reading: Submission Guidelines

  • Final Project Submission and Evaluation

  • Reading: Congratulations & Next Steps

  • Reading: Thanks from the Course Team

Taught by

Joseph Santarcangelo

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