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861 ratings

Joseph Santarcangelo +1 more instructor

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Offered By

IBM Skills Network

About this Course

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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.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Intermediate Level

Approx. 14 hours to complete

English

Subtitles: English

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Skills you will gain

Artificial Intelligence (AI)
Machine Learning
Feature Engineering
Statistical Hypothesis Testing
Exploratory Data Analysis

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Intermediate Level

Approx. 14 hours to complete

English

Subtitles: English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructors

Instructor rating

4.45/5 (366 Ratings)

Joseph Santarcangelo

Ph.D., Data Scientist at IBM

IBM Developer Skills Network

795,081 Learners

24 Courses

Svitlana (Lana) Kramar

Data Science Content Developer

Skills Network

44,167 Learners

2 Courses

Offered by

IBM Skills Network

IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.

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Syllabus - What you will learn from this course

Content Rating93%(3,494 ratings)

Week

Week 1

2 hours to complete

A Brief History of Modern AI and its Applications

Artificial Intelligence is not new, but it is new in a sense that it is easier than ever to get started using Machine Learning in business settings. In this module, we will go over a quick introduction to AI and Machine Learning and we will visit a brief history of the modern AI. We will also explore some of the current applications of AI and Machine Learning for you, to think about how you want to leverage them in your day to day business practice or personal projects.

2 hours to complete

10 videos (Total 57 min), 2 readings, 3 quizzes

10 videos

Course Introduction1m

Introduction to Artificial Intelligence and Machine Learning5m

Machine Learning and Deep Learning10m

Machine Learning and Deep Learning - Part 15m

Machine Learning and Deep Learning - Part 24m

History of AI7m

History of Machine Learning and Deep Learning5m

Modern AI6m

Applications3m

Machine Learning Workflow6m

2 readings

Course Prerequisites4m

Review3m

3 practice exercises

Practice Quiz: Artificial Intelligence and Machine Learning5m

Practice Quiz: Modern AI Applications and Workflows5m

Graded Quiz: Module 1 - Modern AI and its Applications30m

Week

Week 2

3 hours to complete

Retrieving and Cleaning Data

Good data is the fuel that powers Machine Learning and Artificial Intelligence. In this module, you will learn how to retrieve data from different sources, how to clean it to ensure its quality.

3 hours to complete

7 videos (Total 43 min), 3 readings, 6 quizzes

7 videos

Retrieving Data from CSV and JSON Files4m

Retrieving Data from Databases, APIs, and the Cloud8m

[Optional] Lab Solution: Reading Data Jupyter Notebook - Part A7m

[Optional]Lab Solution: Reading in Database Files - Part B5m

Data Cleaning6m

Handling Missing Values and Outliers6m

Handling Missing Values and Outliers using Residuals4m

3 readings

[Optional] Download Assets for Lab: Reading Data in Database Files - Part A2m

[Optional] Download Assets for Lab: Reading Data in Jupyter Notebook - Part B2m

Summary/Review3m

3 practice exercises

Practice Quiz: Retrieving Data9m

Practice Quiz: Data Cleaning5m

Graded Quiz: Module 2 - Retrieving Data and Cleaning Data30m

Week

Week 3

5 hours to complete

Exploratory Data Analysis and Feature Engineering

In this module you will learn how to conduct exploratory analysis to visually confirm it is ready for machine learning modeling by feature engineering and transformations.

5 hours to complete

15 videos (Total 104 min), 3 readings, 7 quizzes

15 videos

Introduction to Exploratory Data Analysis (EDA)5m

EDA with Visualization6m

Grouping Data for EDA6m

[Optional]Solution: EDA Notebook - Part 15m

[Optional]Solution: EDA Notebook - Part 210m

[Optional]Solution: EDA Notebook - Part 36m

[Optional]Solution: EDA Notebook - Part 48m

Feature Engineering and Variable Transformation - Background3m

Variable Transformation5m

Feature Encoding3m

Feature Scaling4m

Common Variable Transformations in Python2m

[Optional] Solution: Feature Engineering Lab - Part 111m

[Optional] Solution: Feature Engineering Lab - Part 212m

[Optional] Solution: Feature Engineering Lab - Part 39m

3 readings

[Optional] Download Assets for Lab: Exploratory Data Analysis Lab2m

[Optional] Download Assets for Lab: Feature Engineering Demo 2m

Summary/Review10m

3 practice exercises

Practice Quiz: Exploratory Data Analysis5m

Practice Quiz: Feature Engineering and Variable Transformation5m

Graded Quiz: Module 3 - Exploratory Data Analysis and Feature Engineering30m

Week

Week 4

3 hours to complete

Inferential Statistics and Hypothesis Testing

Inferential statistics and hypothesis testing are two types of data analysis often overlooked at early stages of analyzing your data. They can give you quick insights about the quality of your data. They also help you confirm business intuition and help you prescribe what to analyze next using Machine Learning. This module looks at useful definitions and simple examples that will help you get started creating hypothesis around your business problem and how to test them.

3 hours to complete

16 videos (Total 88 min), 2 readings, 5 quizzes

16 videos

Estimation and Inference - Introduction5m

Estimation and Inference - Example5m

Estimation and Inference - Parametric vs. Non-Parametric5m

Estimation and Inference - Commonly Used Distributions4m

Frequentist vs. Bayesian Statistics5m

Introduction to Hypothesis2m

Hypothesis Testing Example2m

Bayesian Interpretation of Hypothesis Testing Example4m

Type 1 vs Type 2 Error5m

Type 1 vs Type 2 Error: Examples4m

Hypothesis Testing Terminology 4m

Significance Level and P-Values7m

Significance Level and P-Values and the F Statistic5m

[Optional] Hypothesis Testing Demo - Part 15m

[Optional] Hypothesis Testing Demo - Part 210m

Correlation vs Causation9m

2 readings

[Optional] Download Assets for Lab: Hypothesis Testing Demo2m

Summary/Review3m

3 practice exercises

Practice Quiz: Estimation and Inference, and Hypothesis Testing5m

Practice Quiz: Hypothesis Testing5m

Graded Quiz: Module 4 - Inferential Statistics and Hypothesis Testing30m

Reviews

4.6

197 reviews

5 stars
71.24%
4 stars
20.21%
3 stars
5.18%
2 stars
2.16%
1 star
1.18%

TOP REVIEWS FROM EXPLORATORY DATA ANALYSIS FOR MACHINE LEARNING

by ACSep 30, 2020

It is a really insightful and interactive learning experience. Furthermore, the trainers and coaches were very knowledgable.

by NWDec 7, 2020

Recommended course for those wanting to advance their understanding about Exploratory Data Analysis for Machine Learning.

by MLSep 21, 2021

Excellent, very detailed. However, if the lessons can be expand for hypothesis testing and some of their common test like T test, Anova 1 and 2 way, chi square,..it would be better further.

by AFNov 6, 2020

Great course and very well structured. I'm really impressed with the instructor who give thorough walkthrough to the code.

View all reviews

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Exploratory Data Analysis for Machine Learning

About this Course

Skills you will gain