4.6
estrellas
1,116 calificaciones

94 %

Joseph Santarcangelo +1 instructores más

53.534 ya inscrito

Ofrecido Por

Habilidades en redes de IBM

Acerca de este Curso

169.256 vistas recientes

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.

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Certificado para compartir

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100 % en línea

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Nivel intermedio

Aprox. 14 horas para completar

Inglés (English)

¿Podría tu empresa beneficiarse de la capacitación de los empleados en las habilidades más demandadas?

Prueba Coursera para negocios

Habilidades que obtendrás

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

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Nivel intermedio

Aprox. 14 horas para completar

Inglés (English)

¿Podría tu empresa beneficiarse de la capacitación de los empleados en las habilidades más demandadas?

Prueba Coursera para negocios

Instructores

Calificación del instructor

4,46/5 (431 calificaciones)

Joseph Santarcangelo

Ph.D., Data Scientist at IBM

IBM Developer Skills Network

925.904 alumnos

26 cursos

Svitlana (Lana) Kramar

Data Science Content Developer

Skills Network

58.655 alumnos

3 cursos

Ofrecido por

Habilidades en redes de IBM

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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Programa - Qué aprenderás en este curso

Calificación del contenido94%(4,890 calificaciones)

Semana1

Semana 1

2 horas para completar

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 horas para completar

10 videos (Total 57 minutos), 2 lecturas, 3 cuestionarios

10 videos

Course Introduction1mIntroduction to Artificial Intelligence and Machine Learning5mMachine Learning and Deep Learning10mMachine Learning and Deep Learning - Part 15mMachine Learning and Deep Learning - Part 24mHistory of AI7mHistory of Machine Learning and Deep Learning5mModern AI6mApplications3mMachine Learning Workflow6m

2 lecturas

Course Prerequisites4mReview3m

3 ejercicios de práctica

Practice Quiz: Artificial Intelligence and Machine Learning5mPractice Quiz: Modern AI Applications and Workflows5mGraded Quiz: Module 1 - Modern AI and its Applications30m

Semana2

Semana 2

3 horas para completar

Retrieving and Cleaning Data

3 horas para completar

7 videos (Total 43 minutos), 3 lecturas, 6 cuestionarios

7 videos

Retrieving Data from CSV and JSON Files4mRetrieving Data from Databases, APIs, and the Cloud8m[Optional] Lab Solution: Reading Data Jupyter Notebook - Part A7m[Optional]Lab Solution: Reading in Database Files - Part B5mData Cleaning6mHandling Missing Values and Outliers6mHandling Missing Values and Outliers using Residuals4m

3 lecturas

[Optional] Download Assets for Lab: Reading Data in Database Files - Part A2m[Optional] Download Assets for Lab: Reading Data in Jupyter Notebook - Part B2mSummary/Review3m

3 ejercicios de práctica

Practice Quiz: Retrieving Data9mPractice Quiz: Data Cleaning5mGraded Quiz: Module 2 - Retrieving Data and Cleaning Data30m

Semana3

Semana 3

5 horas para completar

Exploratory Data Analysis and Feature Engineering

5 horas para completar

15 videos (Total 104 minutos), 3 lecturas, 7 cuestionarios

15 videos

Introduction to Exploratory Data Analysis (EDA)5mEDA with Visualization6mGrouping 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 48mFeature Engineering and Variable Transformation - Background3m Variable Transformation5mFeature Encoding3mFeature Scaling4mCommon 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 lecturas

[Optional] Download Assets for Lab: Exploratory Data Analysis Lab2m[Optional] Download Assets for Lab: Feature Engineering Demo 2mSummary/Review10m

3 ejercicios de práctica

Practice Quiz: Exploratory Data Analysis5mPractice Quiz: Feature Engineering and Variable Transformation5mGraded Quiz: Module 3 - Exploratory Data Analysis and Feature Engineering30m

Semana4

Semana 4

3 horas para completar

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 horas para completar

16 videos (Total 88 minutos), 2 lecturas, 5 cuestionarios

16 videos

Estimation and Inference - Introduction5mEstimation and Inference - Example5mEstimation and Inference - Parametric vs. Non-Parametric5mEstimation and Inference - Commonly Used Distributions4mFrequentist vs. Bayesian Statistics5mIntroduction to Hypothesis2mHypothesis Testing Example2mBayesian Interpretation of Hypothesis Testing Example4mType 1 vs Type 2 Error5mType 1 vs Type 2 Error: Examples4mHypothesis Testing Terminology 4mSignificance Level and P-Values7mSignificance Level and P-Values and the F Statistic5m[Optional] Hypothesis Testing Demo - Part 15m[Optional] Hypothesis Testing Demo - Part 210mCorrelation vs Causation9m

2 lecturas

[Optional] Download Assets for Lab: Hypothesis Testing Demo2mSummary/Review3m

3 ejercicios de práctica

Practice Quiz: Estimation and Inference, and Hypothesis Testing5mPractice Quiz: Hypothesis Testing5mGraded Quiz: Module 4 - Inferential Statistics and Hypothesis Testing30m

Reseñas

4.6

250 reseña

5 stars
71,27 %
4 stars
19,98 %
3 stars
5,26 %
2 stars
2,05 %
1 star
1,42 %

Principales reseñas sobre EXPLORATORY DATA ANALYSIS FOR MACHINE LEARNING

por PZ6 de abr. de 2021

Very well curated course. Walks through all the topics in detail. Would be better if the professor had a little bit higher voice.

por DS30 de nov. de 2020

The only reason that I do not give it 5 stars is because the website of coursera is not good enough to handle the peer review assignments at the end of the course.

por C1 de ago. de 2021

This course was really good for me because it went into depth on what I believe is the most important part of ML which is the data analysis and preparation.

por AE26 de sep. de 2021

Very detailed course of Exploratory Data Analysis for Machine learning. Ready to take the next step in data science or Machine learning, this is great course for taking you to the next level.

Ver todas las reseñas

Preguntas Frecuentes

¿Cuándo podré acceder a las lecciones y tareas?
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es posible que el curso no ofrezca la opción de participar como oyente. En cambio, puedes intentar con una Prueba gratis o postularte para recibir ayuda económica.
Es posible que el curso ofrezca la opción 'Curso completo, sin certificado'. Esta opción te permite ver todos los materiales del curso, enviar las evaluaciones requeridas y obtener una calificación final. También significa que no podrás comprar una experiencia de Certificado.
¿Qué recibiré si me suscribo a este Certificado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos del Certificado y te darán un certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.

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Exploratory Data Analysis for Machine Learning

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