This course explores techniques and applications of machine learning using Python and has two main components:
- The purpose of Machine Learning and it’s applications in the real world.
- A general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms.
The course covers real-life examples of Machine learning and shows you how it affects society in ways you may not have guessed!
With only a few hours study a week you get:
- New skills for CV including regression, classification, clustering, sci-kit learn and SciPy
- New application to demonstrate, including image classification detection, trend analysis and prediction, customer analysis, recommendation engines, and more.
- A certificate in machine learning to prove your competency plus an IBM digital badge upon successful completion.
Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.
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.
Week 1 – Introduction to Machine Learning
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
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
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
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
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
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.
Cost: $38.00
Duration: 17
