Machine Learning with Python

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.

This course is part of multiple programs

This course can be applied to multiple Specializations or Professional Certificates programs.

Completing this course will count towards your learning in any of the following programs:

• IBM Data Science Professional Certificate
• IBM AI Engineering Professional Certificate

Syllabus

WEEK 1: Introduction to Machine Learning

• 1 hour to complete

In this week, 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

• 5 hours to complete

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

WEEK 3: Classification

• 5 hours to complete

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.

WEEK 4: Clustering

• 4 hours to complete

In this section, you will learn about different clustering approaches. You learn how to use clustering for customer segmentation, grouping same vehicles, and also clustering of weather stations. You understand 3 main types of clustering, including Partitioned-based Clustering, Hierarchical Clustering, and Density-based Clustering.

WEEK 5: Recommender Systems

• 3 hours to complete

In this module, you will learn about recommender systems. First, you will get introduced with main idea behind recommendation engines, then you understand two main types of recommendation engines, namely, content-based and collaborative filtering.

WEEK 6: Final Project

• 4 hours to complete

In this module, you will do a project based of what you have learned so far. You will submit a report of your project for peer evaluation.

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