Learn machine learning through real use cases. Build the skills for a career in one of the most relevant fields of modern AI through hands-on projects and curriculum from IBM’s experts.

4.8

étoiles

121 évaluations

Mark J Grover +1 enseignant de plus

3 045 déjà inscrits

Offert par

Spécialisation IBM Introduction to Machine LearningIBM

Ce que vous allez apprendre

Understand the potential applications of machine learning

Gain technical skills like SQL, machine learning modelling, supervised and unsupervised learning, regression, and classification.

Identify opportunities to leverage machine learning in your organization or career

Communicate findings from your machine learning projects to experts and non-experts

Compétences que vous acquerrez

Machine Learning Regression
Machine Learning Classification
Machine Learning
Unsupervised Learning
Exploratory Data Analysis
Artificial Intelligence (AI)
Feature Engineering
Statistical Hypothesis Testing
Regression Analysis
Supervised Learning
Linear Regression
Ridge Regression

À propos de ce Spécialisation

4 240 consultations récentes

Machine learning skills are becoming more and more essential in the modern job market. In 2019, Machine Learning Engineer was ranked as the #1 job in the United States, based on the incredible 344% growth of job openings in the field between 2015 to 2018, and the role’s average base salary of $146,085 (Indeed). This four-course Specialization will help you gain the introductory skills to succeed in an in-demand career in machine learning and data science. After completing this program, you’ll be able to realize the potential of machine learning algorithms and artificial intelligence in different business scenarios. You’ll be able to identify when to use machine learning to explain certain behaviors and when to use it to predict future outcomes. You’ll also learn how to evaluate your machine learning models and to incorporate best practices. By the end of this program, you will have developed concrete machine learning skills to apply in your workplace or career search, as well as a portfolio of projects demonstrating your proficiency. In addition to receiving a certificate from Coursera, you'll also earn an IBM Badge to help you share your accomplishments with your network and potential employer. You can also leverage the learning from the program to complete the remaining two courses of the six-course IBM Machine Learning Professional Certificate and power a new career in the field of machine learning.

Projet d'apprentissage appliqué

In this program, you’ll complete hands-on projects designed to develop your analytical and machine learning skills. You’ll also produce a summary of your insights from each project using data analysis skills, in a similar way as you would in a professional setting, including producing a final presentation to communicate insights to fellow machine learning practitioners, stakeholders, C-suite executives, and chief data officers.

You are highly encouraged to compile your completed projects into an online portfolio that showcases the skills learned in this Specialization.

Certificat partageable

Obtenez un Certificat lorsque vous terminez

Cours en ligne à 100 %

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Planning flexible

Définissez et respectez des dates limites flexibles.

Niveau intermédiaire

Certaines connaissances prérequises.

Approximativement 3 mois pour terminer

Rythme recommandé de 3 heures/semaine

Anglais

Sous-titres : Anglais, Arabe, Français, Portugais (européen), Italien, Vietnamien, Allemand, Russe, Espagnol

Certificat partageable

Obtenez un Certificat lorsque vous terminez

Cours en ligne à 100 %

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Planning flexible

Définissez et respectez des dates limites flexibles.

Niveau intermédiaire

Certaines connaissances prérequises.

Approximativement 3 mois pour terminer

Rythme recommandé de 3 heures/semaine

Anglais

Sous-titres : Anglais, Arabe, Français, Portugais (européen), Italien, Vietnamien, Allemand, Russe, Espagnol

Comment fonctionne la Spécialisation

Suivez les cours

Une Spécialisation Coursera est une série de cours axés sur la maîtrise d'une compétence. Pour commencer, inscrivez-vous directement à la Spécialisation ou passez en revue ses cours et choisissez celui par lequel vous souhaitez commencer. Lorsque vous vous abonnez à un cours faisant partie d'une Spécialisation, vous êtes automatiquement abonné(e) à la Spécialisation complète. Il est possible de terminer seulement un cours : vous pouvez suspendre votre formation ou résilier votre abonnement à tout moment. Rendez-vous sur votre tableau de bord d'étudiant pour suivre vos inscriptions aux cours et vos progrès.

Projet pratique

Chaque Spécialisation inclut un projet pratique. Vous devez réussir le(s) projet(s) pour terminer la Spécialisation et obtenir votre Certificat. Si la Spécialisation inclut un cours dédié au projet pratique, vous devrez terminer tous les autres cours avant de pouvoir le commencer.

Obtenir un Certificat

Lorsque vous aurez terminé tous les cours et le projet pratique, vous obtiendrez un Certificat que vous pourrez partager avec des employeurs éventuels et votre réseau professionnel.

Cette Spécialisation compte 4 cours

Cours1

Cours 1

Exploratory Data Analysis for Machine Learning

4.6

étoiles

557 évaluations

•

132 avis

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.

Cours2

Cours 2

Supervised Machine Learning: Regression

4.7

étoiles

199 évaluations

•

39 avis

This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques.

By the end of this course you should be able to: Differentiate uses and applications of classification and regression in the context of supervised machine learning Describe and use linear regression models Use a variety of error metrics to compare and select a linear regression model that best suits your data Articulate why regularization may help prevent overfitting Use regularization regressions: Ridge, LASSO, and Elastic net Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Regression techniques 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 Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.

Cours3

Cours 3

Supervised Machine Learning: Classification

4.9

étoiles

127 évaluations

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30 avis

This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification. You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. The hands-on section of this course focuses on using best practices for classification, including train and test splits, and handling data sets with unbalanced classes.

By the end of this course you should be able to: -Differentiate uses and applications of classification and classification ensembles -Describe and use logistic regression models -Describe and use decision tree and tree-ensemble models -Describe and use other ensemble methods for classification -Use a variety of error metrics to compare and select the classification model that best suits your data -Use oversampling and undersampling as techniques to handle unbalanced classes in a data set Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Classification techniques 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 Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.

Cours4

Cours 4

Unsupervised Machine Learning

4.8

étoiles

109 évaluations

•

25 avis

This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. The hands-on section of this course focuses on using best practices for unsupervised learning.

By the end of this course you should be able to: Explain the kinds of problems suitable for Unsupervised Learning approaches Explain the curse of dimensionality, and how it makes clustering difficult with many features Describe and use common clustering and dimensionality-reduction algorithms Try clustering points where appropriate, compare the performance of per-cluster models Understand metrics relevant for characterizing clusters Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Unsupervised Machine Learning techniques 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 Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.

Enseignants

Mark J Grover

Digital Content Delivery Lead

IBM Data & AI Learning

44 948 étudiants

14 cours

Miguel Maldonado

Machine Learning Curriculum Developer

Data and AI Learning

33 577 étudiants

6 cours

Offert par

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.

Foire Aux Questions

Quelle est la politique de remboursement ?
Si vous vous abonnez, vous bénéficiez d'une période d'essai gratuite de 7 jours, durant laquelle vous pouvez annuler votre abonnement sans pénalité. Ensuite, nous n'accordons plus de remboursements, mais vous pouvez annuler votre abonnement à tout instant. Consultez notre politique de remboursement complète.
Puis-je m'inscrire à un seul cours ?
Oui ! Pour commencer, cliquez sur la carte du cours qui vous intéresse et inscrivez-vous. Vous pouvez vous inscrire et terminer le cours pour obtenir un Certificat partageable, ou vous pouvez accéder au cours en auditeur libre afin d'en visualiser gratuitement le contenu. Si vous vous abonnez à un cours faisant partie d'une Spécialisation, vous êtes automatiquement abonné(e) à la Spécialisation complète. Visitez votre tableau de bord d'étudiant(e) pour suivre vos progrès.
Une aide financière est-elle possible ?
Oui, Coursera offre une Aide Financière aux étudiants qui n'ont pas les moyens d'acquitter les frais. Demandez-la en cliquant sur le lien Aide Financière sous le bouton S'inscrire situé à gauche. Vous devrez remplir un formulaire de demande et vous serez averti(e) si elle est acceptée. Vous devrez répéter cette procédure pour chaque cours de la Spécialisation, y compris pour le Projet Final. En savoir plus.
Puis-je suivre le cours gratuitement ?
Si vous vous inscrivez au cours, vous pouvez accéder à tous les cours de la Spécialisation et obtenir un Certificat lorsque vous terminez le travail. Si vous souhaitez seulement lire et examiner le contenu du cours, vous pouvez accéder gratuitement au cours en auditeur libre. Si vous n'avez pas les moyens d'acquitter les frais, vous pouvez faire une demande d'Aide Financière.
Ce cours est-il vraiment accessible en ligne à 100 % ? Dois-je assister à certaines activités en personne ?
Ce cours est entièrement en ligne : vous n'avez donc pas besoin de vous présenter physiquement dans une salle de classe. Vous pouvez accéder à vos vidéos de cours, lectures et devoirs en tout temps et en tout lieu, par l'intermédiaire du Web ou de votre appareil mobile.
How can I earn my IBM Badge?
Upon completion of this program, you will receive an email from Coursera with directions on how to claim your IBM Badge through Acclaim. Learn more about IBM Badges
What is machine learning?
Machine learning is a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time without being programmed to do so.
What careers can I pursue in the field of machine learning?
Machine learning skills are applicable to a variety of fields, but some jobs that require machine learning skills include:
-Data Scientist
-Machine Learning Engineer
-Software Engineer
In 2019, Machine Learning Engineer was ranked as the #1 job in the United States, based on the incredible 344% growth of job openings in the field between 2015 to 2018, and the role’s average base salary of $146,085 (Indeed). Take advantage of this opportunity to develop your machine learning skills for a high-paying, in-demand career in machine learning today!
Quelle est la durée nécessaire pour terminer la Spécialisation ?
The entire specialization requires 40-45 hours of study. Each of the 4 courses requires 7-10 hours of study.
What background knowledge is necessary?
Ideally, you should have some background in Math, Stats, and computer programming, as most demonstrations, labs, and projects use Python programming language and concepts like matrix factorization, convergence, or stochastic gradient descent.This Specialization is designed specifically for scientists, software developers, and business analysts who want to round their analytical skills in Data Science, AI, and Machine Learning, but is also appropriate for anyone with a passion for data and basic Math, Statistics, and programming skills.
Do I need to take the courses in a specific order?
We recommend you to take the courses in the order presented in the specialization page, as each course builds on material presented in previous courses.
Puis-je obtenir des crédits universitaires si je réussis la Spécialisation ?
No.
What will I be able to do upon completing the Specialization?
You will be able to use high-demand Machine Learning techniques in real world data sets. You will be able to derive and communicate insights from data using Exploratory Data Analysis, Supervised Learning, and Unsupervised Learning.

D'autres questions ? Visitez le Centre d'Aide pour les Étudiants.