Page List: Introduction to Machine Learning

This the page list for Introduction to Machine Learning where I document what I've learned from the Machine Learning Specialization offered by Deeplearning.ai

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Day 0: Introduction to Machine Learning

Day 1: Cost Function (Examples)

Day 2: Train the model with Gradient Descent

Day 3: Gradient Descent (part 2)

Day 4: Learning Rate

Day 5: Summary of Introduction to ML

Day 6: Multiple Linear Regression

Day 7: Gradient Descent for Multiple Linear Regression

Day 8: Feature Scaling and Learning Rate

Day 9: Feature Engineering and Polynomial Regression

Day 10: Summary of Regression with Multiple Variables

Day 11: Introduction to Classification

Day 12: Decision Boundary

Day 13: Cost Function for Logistic Regression

Day 14: Gradient Descent for Logistic Regression

Day 15: Linear and Logistic Regression with Scikit-Learn

Day 16: Overfitting

Day 17: Cost Function with Regularization

Day 18: Introduction to Neural Network

Day 19: Neural Network Model

Day 20: Inference-making predictions(Forward Propagation)

Day 21: TensorFlow Implementation

Day 22: Data in TensorFlow

Day 23: Building a Neural Network

Day 24: Neural Network Implementation in Python

Day 25: Neural Network Training

Day 26: Activation Functions

Day 27: Multi-Class Classification

Day 28: Neural Network with Softmax Output

Day 29: Additional Neural Network Concepts

Day 30: Advice for applying Machine Learning

Day 31: Model selection and training/cv/test sets

Day 32: Bias and Variance

Day 33: Establishing a Baseline Level of Performance

Day 34: Bias/Variance and Neural Network

Day 35: Iterative Loop of ML Development

Day 36: Decision Trees

Day 37: Information Gain