Machine Learning with Python by IBM
This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. 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!
Created by: Saeed Aghabozorgi
Produced in 2018
Course Depth & Coverage
Overall Score : 94 / 100
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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 to 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.
- 4.7 Rating
- 146 Reviews
Saeed Aghabozorgi, PhD is a Sr. Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients' ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like deep learning, machine learning and statistical modelling on large datasets.
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