Overview
Curriculum
In this module, we will cover the essentials of machine learning using Python. We will explore key concepts like supervised and unsupervised learning, model selection, and training, along with popular libraries like scikit-learn. We’ll work through the process of preparing data, selecting the right algorithms, evaluating model performance, and applying machine learning techniques to real-world problems. By the end of this module, you’ll have a strong foundation in applying machine learning methods and be equipped to take on machine learning projects with confidence.
What You'll Learn
- Explain key Object-Oriented Programming (OOP) concepts and their role in structuring machine learning code
- Describe the machine learning process and fundamental terminologies
- Understand the structure and functionality of the scikit-learn estimator API
- Select appropriate models and tune hyperparameters in scikit-learn for different tasks
- Apply supervised learning techniques to predict customer churn using real-world data
Courses you might be interested in
Build foundational Python skills and theory to succeed in bootcamp and practical applications.
-
11 Lessons
$100.00
Explore, visualize, and transform data to enhance analysis, handle issues, and improve modeling.
-
13 Lessons
$100.00
Transform raw data into impactful visuals using pandas, matplotlib, and seaborn for clear communication.
-
12 Lessons
$100.00
Learn to build predictive models that drive business impact while addressing data and ethical considerations.
-
8 Lessons
$100.00