Course description
Machine Learning with Python: A Practical Introduction
This Machine Learning with Python course dives into the basics of machine learning using Python, 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.
We'll explore many popular algorithms including Classification, Regression, Clustering, and Dimensional Reduction and popular models such as Train/Test Split, Root Mean Squared Error (RMSE), and Random Forests. Along the way, you’ll look at real-life examples of machine learning and see how it affects society in ways you may not have guessed!
Most importantly, you will transform your theoretical knowledge into practical skill using hands-on labs. Get ready to do more learning than your machine!
We'll explore many popular algorithms including Classification, Regression, Clustering, and Dimensional Reduction and popular models such asTrain/Test Split, Root Mean Squared Error and Random Forests.
Mostimportantly, you will transform your theoretical knowledge into practical skill using hands-on labs. Get ready to do more learning than your machine!
Upcoming start dates
Suitability - Who should attend?
Prerequisites
Python Basics for Data Science
Outcome / Qualification etc.
What you'll learn
- The difference between the two main types of machine learning methods: supervised and unsupervised
- Supervised learning algorithms, including classification and regression
- Unsupervised learning algorithms, including Clustering and Dimensionality Reduction
- How statistical modeling relates to machine learning and how to compare them
- Real-life examples of the different ways machine learning affects society
Training Course Content
Introduction to Machine Learning
- Applications of Machine Learning
- Supervised vs Unsupervised Learning
- Python libraries suitable for Machine Learning
Regression
- Linear Regression
- Non-linear Regression
- Model evaluation methods
Classification
- K-Nearest Neighbour
- Decision Trees
- Logistic Regression
- Support Vector Machines
- Model Evaluation
Unsupervised Learning
- K-Means Clustering
- Hierarchical Clustering
- Density-Based Clustering
Recommender Systems
- Content-based recommender systems
- Collaborative Filtering
Course delivery details
This course is offered through IBM, a partner institute of EdX.
4-6 hours per week
Expenses
- Verified Track -$99
- Audit Track - Free