Intro to Logistic Regression - Machine Learning with Python

Link to this course:
https://click.linksynergy.com/deeplink?id=Gw/ETjJoU9M&mid=40328&murl=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fmachine-learning-with-python
Intro to Logistic Regression - Machine Learning with Python
IBM AI Engineering Specialization
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.

Labs were incredibly useful as a practical learning tool which therefore helped in the final assignment! I wouldn't have done well in the final assignment without it together with the lecture videos!,In peer graded assignments, if someone is grading any peer below passing criteria then it must be compulsory to let the learner know his mistakes or shortcomings because of which he does not graded.
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.
Intro to Logistic Regression - Machine Learning with Python
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