Machine Learning in Python – Theory & Implementation
Learn the theories and implementation of Machine Learning algorithms in Python. Perfect for those interested in Python and Machine Learning.
What you’ll learn
- Learn Data Science
- Learn the theories behind the Machine Learning Algorithms
- Learn applying the Machine Learning Algorithms in Python
- Learn feature engineering
- Learn Python fundamentals
Welcome to the Machine Learning in Python – Theory and Implementation course. This course aims to teach students the machine learning algorithms by simplfying how they work on theory and the application of the machine learning algorithms in Python. Course starts with the basics of Python and after that machine learning concepts like evaluation metrics or feature engineering topics are covered in the course. Lastly machine learning algorithms are covered. By taking this course you are going to have the knowledge of how machine learning algorithms work and you are going to be able to apply the machine learning algorithms in Python. We are going to be covering python fundamentals, pandas, feature engineering, machine learning evaluation metrics, train test split and machine learning algorithms in this course. Course outline is
Python Fundamentals
Pandas Library
Feature Engineering
Evaluation of Model Performances
Supervised vs Unsupervised Learning
Machine Learning Algorithms
The machine learning algorithms that are going to be covered in this course is going to be Linear Regression, Logistic Regression, K-Nearest Neighbors, Support Vector Machines, Decision Tree, Random Forests and K-Means Clustering. If you are interested in Machine Learning and want to learn the algorithms theories and implementations in Python you can enroll into the course. You can always ask questions from course Q&A section. Thanks for reading the course description, have a nice day.
Who this course is for:
- People who wants to learn Machine Learning
- People who wants to learn Python
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