MachineLearningWithPython

This repository tracks my progress with respect to the IBM course Machine Learning With Python and other learnings in ML with Python.
This is the link of the course(ML with Python)
This is the link to my badge(Badge)

This is the weekly split up of the course.
Week 1:Introduction to Machine Learning

In this module, 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.

Week 2: Regression

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

Week 3: Classification

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

Week 4: Linear Classification
Week 5: Clustering

In this module, you will learn about clustering specifically k-means clustering. You learn how the k-means clustering algorithm works and how to use k-means clustering for customer segmentation.

Week 6: Final Exam and Project

Other resources that I referred to alongside the course

ML notes