Python for Machine Learning
5 Based on 213 reviews- 228,696 Learners
- 287 Courses
Unlock the power of machine learning and transform your Python skills into real-world impact. With over 91% of businesses investing in AI initiatives, the ability to apply machine learning is one of the most in-demand tech skills today. This hands-on course will guide you through building powerful algorithms using Python’s Scikit-learn library—equipping you to predict classifications, continuous values, and more.
Request More InfoUpcoming Class Schedule
$1,195.00
Nov 05 - Nov 06, 2026
8:00 AM - 4:00 PM Eastern- VILT
Whether you're refining your models with Lasso and Ridge regression or deploying interactive APIs, this course gives you the tools and techniques to apply machine learning confidently in your day-to-day work.
In this course, you’ll gain practical experience applying machine learning algorithms using Python. You’ll learn how to process and analyze data using NumPy and Pandas, create both classification and regression models with Scikit-learn, and apply feature engineering techniques to real-world datasets. You’ll also explore key concepts such as supervised vs unsupervised learning, model evaluation, and end-to-end model deployment as APIs.Who should attend Python for Machine Learning Course
This course is ideal for experienced Python developers who are ready to expand their skillset into machine learning. If you want to build a modern portfolio of machine learning projects, understand both supervised and unsupervised learning algorithms, and learn practical deployment methods, this course is for you.
Prerequisites for Python for Machine Learning Course
Python for Machine Learning Course Outline
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FAQs on Python for Machine Learning
This course is designed for experienced Python developers looking to expand into machine learning, particularly those wanting to build a portfolio of ML projects and understand both supervised and unsupervised learning algorithms.
Learners should have intermediate Python skills and knowledge equivalent to what’s gained from a Python for Data Science course.
The course covers Linear Regression, Logistic Regression, K Nearest Neighbors, Support Vector Machine, Decision Trees, and various unsupervised learning methods, taught through both theory and hands-on practice.
You’ll gain hands-on experience with Jupyter notebooks, NumPy, Pandas, Matplotlib, and Scikit-learn for data processing, visualization, and building machine learning models.
You’ll learn to process and analyze data, build classification and regression models, apply feature engineering techniques, evaluate model performance, and deploy machine learning models as APIs.