Data Science: Supervised Machine Learning in Python- Udemy

In this course you will start with K-Nearest Neighbor algorithms then you will move to learn Naive Bayes Classifier and General Bayes Classifier. Then you will transitioned to learn decision tree algorithms, perceptron algorithms, neural networks and deep learning which are the key techniques of machine learning. Later on, you will learn use of Sci-Kit Learn library and how to implement machine learning web service. After doing this course you will be able to apply machine learning techniques to various data sets of real world problems.

• Introduction and Review
• K-Nearest Neighbor
• Naive Bayes and Bayes Classifiers
• Decision Trees
• Perceptrons
• Practical Machine Learning
• Building a Machine Learning Web Service
• Conclusion
• Appendix

Duration: 3 Hours

Mode: Online Instructor-Led

Fees: $ 120 (Please check website for discount)

• Experience in Python, Numpy, and Pandas.
• Basics of Probability and statistics