By the end of this course, learners will be able to build, evaluate, and optimize machine learning models using Python. They will develop the ability to preprocess data with NumPy and Pandas, visualize insights using Matplotlib, and implement workflows with scikit-learn pipelines. Learners will apply regression, classification, clustering, and dimensionality reduction techniques to real-world datasets, while mastering hyperparameter tuning for improved model performance.
Machine Learning with Python: Build & Optimize
您将学到什么
Build and optimize ML models using scikit-learn.
Preprocess and visualize data with NumPy, Pandas, and Matplotlib.
Apply regression, classification, and clustering techniques.
您将获得的技能
- Regression Analysis
- Pandas (Python Package)
- Predictive Modeling
- Data Science
- Python Programming
- Statistical Machine Learning
- Performance Tuning
- Feature Engineering
- Matplotlib
- Statistical Modeling
- Applied Machine Learning
- NumPy
- Machine Learning Algorithms
- Unsupervised Learning
- Data Manipulation
- Data Processing
- Data Visualization
- Dimensionality Reduction
- Scikit Learn (Machine Learning Library)
- Machine Learning
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最近已更新！
October 2025
作业
11 项作业
授课语言：英语（English）
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本课程是 AI Driven Machine Learning with Python 专项课程 专项课程的一部分
在注册此课程时，您还会同时注册此专项课程。
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该课程共有3个模块
This module introduces learners to the fundamentals of machine learning, including its lifecycle, prerequisites, and essential data handling techniques. Learners will gain practical skills in numerical computing with NumPy and data analysis using Pandas, setting a solid foundation for advanced machine learning tasks.
涵盖的内容
15个视频4个作业
This module focuses on preparing and transforming data for machine learning models. Learners will master visualization using Matplotlib and Pandas, understand the importance of scaling and encoding, and implement preprocessing pipelines for streamlined workflows.
涵盖的内容
7个视频3个作业
This module provides hands-on experience with building, evaluating, and optimizing machine learning models. Learners will explore regression, classification, clustering, dimensionality reduction, and hyperparameter tuning to achieve robust and scalable solutions.
涵盖的内容
15个视频4个作业
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常见问题
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
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