Supervised Machine Learning: Regression and Classification
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Supervised Machine Learning: Regression and Classification at Coursera Overview
Duration | 33 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
- Overview
- Highlights
- Course Details
- Curriculum
Supervised Machine Learning: Regression and Classification at Coursera Highlights
- Earn a certificate of completion
Supervised Machine Learning: Regression and Classification at Coursera Course details
- Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn
- Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression
- The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online
- In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications
Supervised Machine Learning: Regression and Classification at Coursera Curriculum
Week 1: Introduction to Machine Learning
Welcome to machine learning!
Applications of machine learning
What is machine learning?
Supervised learning part
Supervised learning part
Unsupervised learning part
Unsupervised learning part
Jupyter Notebooks
Linear regression model part
Linear regression model part
Cost function formula
Cost function intuition
Visualizing the cost function
Week 2: Regression with multiple input variables
Multiple features
Vectorization part
Vectorization part
Gradient descent for multiple linear regression
Feature scaling part
Feature scaling part
Checking gradient descent for convergence
Choosing the learning rate
Feature engineering
Polynomial regression
Week 3: Classification
Motivations
Logistic regression
Decision boundary
Cost function for logistic regression
Simplified Cost Function for Logistic Regression
Gradient Descent Implementation
The problem of overfitting
Addressing overfitting
Cost function with regularization
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