Get ready to dive into the world of Machine Learning (ML) by using Python! This course is for you whether you want to advance your Data Science career or get started in Machine Learning and Deep Learning.

课程信息
A working knowledge of Python and Data Analysis and Visualization techniques. A minimum of high school math.
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体验 Coursera 企业版您将学到的内容有
Describe the various types of Machine Learning algorithms and when to use them
Compare and contrast linear classification methods including multiclass prediction, support vector machines, and logistic regression
Write Python code that implements various classification techniques including K-Nearest neighbors (KNN), decision trees, and regression trees
Evaluate the results from simple linear, non-linear, and multiple regression on a data set using evaluation metrics
您将获得的技能
- SciPy and scikit-learn
- Machine Learning
- regression
- classification
- Hierarchical Clustering
A working knowledge of Python and Data Analysis and Visualization techniques. A minimum of high school math.
对员工进行热门技能培训能否为您的公司带来益处?
体验 Coursera 企业版提供方
开始攻读学士学位
授课大纲 - 您将从这门课程中学到什么
Introduction to Machine Learning
Regression
Classification
Linear Classification
审阅
- 5 stars75.88%
- 4 stars18.68%
- 3 stars3.44%
- 2 stars0.94%
- 1 star1.03%
来自使用 PYTHON 进行机器学习的热门评论
This was a very informative course. The videos provided a good background on the concepts and I found the labs especially helpful for learning to implement Python code for each technique covered.
In peer graded assignments, if someone is grading any peer below passing criteria then it must be compulsory to let the learner know his mistakes or shortcomings because of which he does not graded.
Great course, they teach the very basic steps for data analysis world, which is awesome so we can get a solid basics understanding, the tests are in a great level (neither too easy nor too hard).
This course is a great way to start learning about ML, as it sets out what you need to do step-by-step, explains very clearly why, and gives you a chance to experiment and practise. Thank you IBM!
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