Machine Learning with Python: A Practical Introduction (IBM)

Introduction

This course dives into the basics of Machine Learning using an approachable, and well-known programming language Python. This course will be reviewing two main components:

1. the purpose of Machine Learning and where it applies to the real world.
2. a general overview of Machine Learning topics, such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms.

In this course, we will practice with real-life examples of Machine Learning and see how it affects society in ways we may not have guessed!

By just putting in a few hours a week for the next several weeks, we'll gain:

• new skills to add to your resume, such as Regression, Classification, Clustering, Sci-kit Learn and SciPy
• new projects that you can add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more.
• 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.

Learning Objectives

In this course, we will:

• Explore examples of Machine Learning and the libraries and languages used to create them.
• Apply the appropriate form of regression to a data set for estimation.
• Apply an appropriate classification method for a particular Machine Learning challenge.
• Use the correct clustering algorithms on different data sets.
• Explain how recommendation systems work, and implement one on a data set.
• Demonstrate your understanding of Machine Learning in an assessed project.

Projects Covered

Through-out the project, we will be covering 10 different projects to get a hands-on experience in Machine Learning using Python, Numpy, Pandas, Matplotlib and Scikit-Learn. The projects that we will complete are:

Regression

1. Prediction of CO2 Emission from Cars based on the attributes of Vehicle using Regression.

Classification

1. Classifying the types of customer for a Telecommunication service provider based on the demographic data of users.
2. Prescribing Medicine based on the historical data of the patients using Classification.
3. Probablity of a Customer leaving a Telecommunication provider in the next month based on the demographic information of users.
4. Predicting if a Tumor is Malignent or Benign using Support Vector Machine.

Clustering

1. Customer Segmentation using Clustering.
2. Grouping the models of cars using Hirarchical Clustering.
3. Clustering the Weather Stations in Canadad based on the weather similarties.

Recommendation

1. Movie Recommendation System based on Content Based Filtering.
2. Movie Recommendation System based on Collaborative Filtering.