À propos de ce cours : This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies in the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation and Machine Learning algorithms. In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed! By just putting in a few hours a week for the next few weeks, this is what you’ll get. 1) New skills to add to your resume, such as regression, classification, clustering, sci-kit learn and SciPy 2) New projects that you can add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more. 3) And a certificate in machine learning to prove your competency, and share it anywhere you like online or offline, such as LinkedIn profiles and social media. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge upon successful completion of the course.

Les destinataires de ce cours : This course is primarily for individuals who are passionate about the field of data science and who are aspiring to apply machine learning in their business, industry or research.

Créé par : IBM

Enseigné par : SAEED AGHABOZORGI, Ph.D., Sr. Data Scientist
IBM Developer Skills Network

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Niveau	Intermédiaire
Engagement	5-6 weeks of study, 3-6 hours per week
Langue	English
Comment réussir	Réussissez tous les devoirs notés pour terminer le cours.

Programme de cours

SEMAINE 1

Introduction to Machine Learning

In this week, you will learn about applications of Machine Learning in different fields such as health care, banking, telecommunication, and so on. You’ll get a general overview of Machine Learning topics such as supervised vs unsupervised learning, and the usage of each algorithm. Also, you understand the advantage of using Python libraries for implementing Machine Learning models.

4 vidéos

Vidéo: Introduction to Machine Learning
Vidéo: Python for Machine Learning
Vidéo: Supervised vs Unsupervised

Noté: Intro to Machine Learning

SEMAINE 2

Regression

In this week, you will get a brief intro to regression. You learn about Linear, Non-linear, Simple and Multiple regression, and their applications. You apply all these methods on two different datasets, in the lab part. Also, you learn how to evaluate your regression model, and calculate its accuracy.

6 vidéos

Vidéo: Simple Linear Regression
Vidéo: Model Evaluation in Regression Models
Vidéo: Evaluation Metrics in Regression Models
LTI Item: Lab: Simple Linear Regression
Vidéo: Multiple Linear Regression
LTI Item: Lab: Multiple Linear Regression
Vidéo: Non-Linear Regression
LTI Item: Lab: Polynomial Regression
LTI Item: Lab: Non-linear Regression

Noté: Regression

SEMAINE 3

Classification

In this week, you will learn about classification technique. You practice with different classification algorithms, such as KNN, Decision Trees, Logistic Regression and SVM. Also, you learn about pros and cons of each method, and different classification accuracy metrics.

9 vidéos

Vidéo: K-Nearest Neighbours
Vidéo: Evaluation Metrics in Classification
LTI Item: Lab: KNN
Vidéo: Introduction to Decision Trees
Vidéo: Building Decision Trees
LTI Item: Lab: Decision Trees
Vidéo: Intro to Logistic Regression
Vidéo: Logistic regression vs Linear regression
Vidéo: Logistic Regression Training
LTI Item: Lab: Logistic Regression
Vidéo: Support Vector Machine
LTI Item: Lab: SVM (Support Vector Machines)

Noté: Classification

SEMAINE 4

Clustering

In this section, you will learn about different clustering approaches. You learn how to use clustering for customer segmentation, grouping same vehicles, and also clustering of weather stations. You understand 3 main types of clustering, including Partitioned-based Clustering, Hierarchical Clustering, and Density-based Clustering.

6 vidéos, 1 lecture

Vidéo: Intro to k-Means
Vidéo: More on k-Means
LTI Item: Lab: k-Means
Vidéo: Intro to Hierarchical Clustering
Vidéo: More on Hierarchical Clustering
LTI Item: Lab: Agglomerative clustering
Vidéo: DBSCAN
LTI Item: Lab: DBSCAN Clustering
Reading: IBM Digital Badge

Noté: Clustering

SEMAINE 5

Recommender Systems

In this module, you will learn about recommender systems. First, you will get introduced with main idea behind recommendation engines, then you understand two main types of recommendation engines, namely, content-based and collaborative filtering.

3 vidéos

Vidéo: Content-based Recommender Systems
LTI Item: Lab: Content-based Recommendation Systems
Vidéo: Collaborative Filtering
LTI Item: Lab: Collaborative Filtering on Movies

Noté: Recommender System

SEMAINE 6

Final Project

In this module, you will do a project based of what you have learned so far. You will submit a report of your project for peer evaluation.

2 vidéos, 2 lectures

Reading: How to do the final project?
Vidéo: Sharing Notebooks on Watson Studio
Reading: IBM Digital Badge

Noté: The best classifier

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Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

Help from Your Peers

Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.

Certificates

Earn official recognition for your work, and share your success with friends, colleagues, and employers.

Créateurs

IBM

IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame.

Tarification

	Acheter le cours
Accéder au contenu du cours	Disponible
Accéder au contenu noté	Disponible
Recevoir une Note Finale	Disponible
Obtenir un Certificat de Cours partageable	Disponible