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.
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.
More important, you will transform your theoretical knowledge in to practical skill using many hands-on labs.
Get ready to do more learning than your machine!
COURSE SYLLABUS
Module 1 - Introduction to Machine Learning
- Applications of Machine Learning
- Supervised vs Unsupervised Learning
- Python libraries suitable for Machine Learning
Module 2 - Regression
- Linear Regression
- Non-linear Regression
- Model evaluation methods
Module 3 - Classification
- K-Nearest Neighbour
- Decision Trees
- Logistic Regression
- Support Vector Machines
- Model Evaluation
Module 4 - Unsupervised Learning
- K-Means Clustering
- Hierarchical Clustering
- Density-Based Clustering
Module 5 - Recommender Systems
- Content-based recommender systems
- Collaborative Filtering
PREREQUISITES FOR THIS COURSE
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 JupyterLab and it is one of the most popular tools used by data scientists. If you are not familiar with JupyterLab, 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.
