What Will You Learn?
Build machine learning models in Python using popular machine learning libraries NumPy & scikit-learn.
Build & train supervised machine learning models for prediction & binary classification tasks, including linear regression & logistic regression.
About This Course
Provider: Coursera
Format: Online
Duration: 33 hours to complete [Approx]
Target Audience: Beginners
Learning Objectives: By the end of this free course, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems.
Course Prerequisites: Basic coding (for loops, functions, if/else statements) & high school-level math (arithmetic, algebra)
Assessment and Certification: Earn a Certificate upon completion from the relevant Provider
Instructor: Stanford University, DeepLearning.AI
Key Topics: Linear Regression, Regularization to Avoid Overfitting, Logistic Regression for Classification, Gradient Descent, Supervised Learning
Topic Covered:
- - Welcome to machine learning!
- - Applications of machine learning
- - What is machine learning?
- - Supervised learning
- - Unsupervised learning
- - Jupyter Notebooks
- - Linear regression model
- - Cost function formula
- - Cost function intuition
- - Visualizing the cost function
- - Visualization examples
- - Gradient descent
- - Implementing gradient descent
- - Gradient descent intuition
- - Learning rate
- - Gradient descent for linear regression
- - Running gradient descent
- - Multiple features
- - Vectorization
- - Gradient descent for multiple linear regression
- - Feature scaling
- - Checking gradient descent for convergence
- - Choosing the learning rate
- - Feature engineering
- - Polynomial regression
- - 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
- - Regularized linear regression
- - Regularized logistic regression
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