Machine Learning with Python
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Dive into machine learning with Python. Learn to build models, preprocess data, and evaluate results using Python libraries like Scikit-learn, pandas, and numpy, to unlock powerful insights for a variety of business and data science applications.
At a Glance
Machine Learning is the foundation of Data Science and Artificial Intelligence (AI) and Python is the language of choice. Get started with ML and Python by enrolling in this hands-on course.
About This Course
This Machine Learning with Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You’ll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each.
Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed!
Explore many algorithms and models:
- Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.
- Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests.
Get ready to do more learning than your machine!
Course Syllabus
Module 1 – Supervised vs Unsupervised Learning
- Machine Learning vs Statistical Modelling
- Supervised vs Unsupervised Learning
- Supervised Learning Classification
- Unsupervised Learning
Module 2 – Supervised Learning I
- K-Nearest Neighbors
- Decision Trees
- Random Forests
- Reliability of Random Forests
- Advantages & Disadvantages of Decision Trees
Module 3 – Supervised Learning II
- Regression Algorithms
- Model Evaluation
- Model Evaluation: Overfitting & Underfitting
- Understanding Different Evaluation Models
Module 4 – Unsupervised Learning
- K-Means Clustering plus Advantages & Disadvantages
- Hierarchical Clustering plus Advantages & Disadvantages
- Measuring the Distances Between Clusters – Single Linkage Clustering
- Measuring the Distances Between Clusters – Algorithms for Hierarchy Clustering
- Density-Based Clustering
Module 5 – Dimensionality Reduction & Collaborative Filtering
- Dimensionality Reduction: Feature Extraction & Selection
- Collaborative Filtering & Its Challenges
Prerequisites
- Python for data science
Recommended skills prior to taking this course
- You have to do hands-on lab for this course. The tool that you use for hands-on is called Jupyter and it is one of the most popular tools used by data scientists. If you are not familiar with Jupyter, I would recommend that you take our free Data Science Hands-on with Open Source Tools.
- This hands-on lab requires that you have working knowledge of Python programming language as it applies to data analytics. If you don’t feel you have sufficient skill in Data Analysis with Python, I recommend you take Data Analysis with Python courses.
Course Staff
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 machine learning and statistical modelling on large datasets.
Kevin Wong
Kevin Wong is a Technical Curriculum Developer. He enjoys developing courses that focuses on the education in the Big Data field. Kevin updates courses to be compatible with the newest software releases, recreates courses on the new cloud environment, and develops new courses such as Introduction to Machine Learning.Kevin is from the University of Alberta, where he has completed his third year of Computer Engineering Co-op.
Daniel Tran Daniel Tran is an IBM Technical Curriculum Developer in Toronto, Ontario. He develops courses to improve the education of customers who seek knowledge in the Big Data field. He has also reworked previously developed courses, updating them to be compatible with the newest software releases, as well as work at the forefront of recreating courses on a newly developed cloud environment. Daniel is from the University of Alberta, where he has completed his third year of traditional Computer Engineering Co-op.
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