This course takes you from understanding the fundamentals of a machine learning project. Learners will understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbours and support vector machines are optimally used. Learners will also gain skills to contrast the practical consequences of different data preparation steps and describe common production issues in applied ML. To be successful, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the second course of the Applied Machine Learning Specialization brought to you by 糖心vlog官网观看 and the Alberta Machine Intelligence Institute.
Regression assessment
Skills You'll Learn
Scikit Learn (Machine Learning Library), Supervised Learning, Data Processing, Regression Analysis, Business Solutions, Classification And Regression Tree (CART), Machine Learning, Performance Metric, Jupyter, Feature Engineering, Applied Machine Learning, Machine Learning Algorithms, Python Programming, Performance Analysis
Reviews
4.7 (413 ratings)
- 5 stars75.78%
- 4 stars18.64%
- 3 stars3.14%
- 2 stars1.21%
- 1 star1.21%
DS
May 7, 2020
Excellent course for an overview of different ML algorithms. The course is made from a perspective of giving insights in process and not too many mathematical details.
RM
Dec 9, 2020
I found the course to be enough detailed to get clarity on the basic concepts of Supervised learning algorithms. I hope to apply the learning from the course in work!
From the lesson
Contrasting Models
Taught By
Anna Koop
Senior Scientific Advisor