This course was ranked in the Top 100 Best Courses on Coursera, based on its high ratings and large number of ratings.
This course dives into the basics of machine learning using a familiar and accessible programming language, Python. In this course, we'll review two main components: First, you'll learn about the purpose of machine learning and where it applies to the real world. Second, you'll get an overview of machine learning topics such as supervised versus unsupervised learning, model evaluation, and machine learning algorithms. In this course, you will practice with real life examples.
This course dives into the basics of machine learning using a familiar and accessible programming language, Python.
In this course, we'll review two main components: First, you'll learn about the purpose of machine learning and where it applies to the real world.
Second, you'll get an overview of machine learning topics such as supervised versus unsupervised learning, model evaluation, and machine learning algorithms.
In this course, you'll 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, here's what you'll get.
1) New skills to add to your resume, such as regression, classification, clustering, learning sci-kit, and SciPy 2) New projects you can add to your portfolio, including cancer detection, economic trend prediction, customer churn, recommendation engines and many more.
3) And a certificate in machine learning to prove your proficiency and share it anywhere you want online or offline, like LinkedIn profiles and social networks.
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.
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In this week, you will learn about the applications of Machine Learning in different fields such as health, banking, telecommunications, etc. You'll get an overview of machine learning topics, such as supervised and unsupervised learning, and the use of each algorithm. Additionally, you understand the benefit of using Python libraries to implement machine learning models.
In this week, you will get a brief introduction to regression. You will learn about linear, nonlinear, simple and multiple regression and their applications. Apply all these methods on two different data sets, in the laboratory part. In addition, you will learn how to evaluate your regression model and calculate its accuracy.
In this week, you will learn about the sorting technique. Practice with different classification algorithms, such as KNN, Decision Trees, Logistic Regression, and SVM. Additionally, you will learn about the pros and cons of each method and the different classification accuracy metrics.
In this section, you will learn about the different clustering approaches. You will learn how to use clustering for customer segmentation, grouping the same vehicles and also grouping weather stations. It comprises 3 main types of clustering, including partition-based clustering, hierarchical clustering, and density-based clustering.
In this module, you will learn about recommender systems. First, you will be introduced to the main idea behind recommendation engines, then you will understand two main types of recommendation engines, namely content-based filtering and collaborative filtering.
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Data science, business and personal development. You can enroll in multiple courses at once, earn unlimited certificates, and learn in-demand job skills to start, grow, and even change careers.
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