README.md

Machine Learning Specialization Coursera

This repository contains all of the courses in the Machine Learning Specialization offered by DeepLearning.AI on Coursera. The specialization is divided into three courses:

1. Supervised Machine Learning: Regression and Classification
2. Advanced Learning Algorithms
3. Unsupervised Learning, Recommenders, Reinforcement Learning

Each course has its own dedicated folder with a number of Jupyter notebooks that demonstrate the concepts covered in the course.

Course 1 : Supervised Machine Learning: Regression and Classification

• Week 1

  • Python and Jupyter Notebooks
  • Optional Labs
    • Model representation
    • Cost function
    • Gradient descent

• Week 2

  • Optional Labs
    • Python, NumPy and vectorization
    • Multiple linear regression
    • Feature scaling and learning rate
    • Feature engineering and Polynomial regression
    • Linear regression with scikit-learn
  • Practice lab
    • Linear regression

• Week 3

  • Optional Labs
    • Classification
    • Sigmoid function and logistic regression
    • Decision boundary
    • Logistic loss
    • Cost function for logistic regression
    • Gradient descent for logistic regression
    • Logistic regression with scikit-learn
    • Overfitting
    • Regularization
  • Programming Assignment
    • Logistic Regression

Course 2 : Advanced Learning Algorithms

• Week 1

  • Neurons and Layers
  • Coffee Roasting in Tensorflow
  • Coffee Roasting NumPy

• Programming Assignment

  • Neural Networks for Binary Classification

• Week 2

  • ReLU activation
  • Softmax
  • Multiclass

• Optional Labs

  • Derivatives
  • Back propagation

• Programming Assignment

  • Neural Networks for Multiclass classification

• Week 3

  • Model Evaluation and Selection
  • Diagnosing Bias and Variance

• Programming Assignment

  • Advice for Applied Machine Learning

• Week 4

• Optional labs

  • Decision Trees
  • Tree Ensembles

• Programming Assignment

  • Decision Trees

Course 3 : Unsupervised Learning, Recommenders, Reinforcement Learning

• Week 1

  • k-means

• Programming Assignment

  • Anomaly Detection

• Week 2

  • Programming Assignment
    • Collaborative Filtering Recommender Systems
    • Deep Learning for Content-Based Filtering

• Optional Lab

  • PCA and data visualization

• Week 3

• Optional Labs

  • State-action value function

• Programming Assignment

  • Reinforcement Learning