Offered By: IBM
Machine Learning with Python
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.Continue reading
Machine Learning53.7k+ Enrolled
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
- Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.
- Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests.
Module 1 - Supervised vs Unsupervised Learning
- Machine Learning vs Statistical Modelling
- Supervised vs Unsupervised Learning
- Supervised Learning Classification
- Unsupervised Learning
- K-Nearest Neighbors
- Decision Trees
- Random Forests
- Reliability of Random Forests
- Advantages & Disadvantages of Decision Trees
- Regression Algorithms
- Model Evaluation
- Model Evaluation: Overfitting & Underfitting
- Understanding Different Evaluation Models
- 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
- Dimensionality Reduction: Feature Extraction & Selection
- Collaborative Filtering & Its Challenges
- Python for data science
- 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.
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.
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.