Python Machine Learning
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.
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.
What Is Included
- Expert-Led Instruction – Learn from certified instructors with real-world IT and data science experience.
- 90-Day Access to Class Recordings – Rewatch virtual instructor-led sessions for up to 90 days after class completion.
- Guaranteed-to-Run Courses – We never cancel a scheduled course, ensuring your training stays on track.
- Flexible Rescheduling – Option to reschedule your course if needed (see conditions).
- Free Course Retake Option – Retake the course at no additional cost (see conditions).
- Hands-On Labs – Gain practical experience applying algorithms and models to real-world datasets.
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.
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.
To be successful in this course, learners should have the following: Intermediate Python skills and knowledge
- Level of knowledge and experience gained from Python for Data Science
- Python
- Jupyter notebooks
- Numpy
- Pandas
- Matplotlib
- Machine Learning concepts
- Supervised vs Unsupervised Learning
- Types of Machine Learning – Classification vs Regression
- Evaluation
- Machine Learning Methods – All in Theory and Practice
- Linear Regression
- Logistic Regression
- K Nearest Neighbors
- Support Vector Machine
- Decision Trees
- Unsupervised Learning Methods
- Feature Engineering and Data Preparation