Coursera-Machine-Learning-Specialization

Course 1 : Supervised Machine Learning: Regression and Classification

Certificate Of Completion

• Week 1

  • Optional Labs
    • Model Representation
    • Cost Function
    • Gradient Descent

• Week 2

  • Optional Labs
    • Numpy Vectorization
    • Multiple Variable Linear Regression
    • Feature scaling and Learning Rate (Multi-variable)
    • Feature Engineering and Polynomial Regression
    • Linear Regression using Scikit-Learn
  • Programming Assignment

• Week 3

  • Optional Labs
    • Classification
    • Sigmoid or Logistic Function
    • Decision Boundary
    • Logistic Loss
    • Cost Function
    • Gradient Descent
    • Logistic Regression using Scikit-Learn
    • Overfitting
    • Regularization
  • Programming Assignment

Course 2 : Advanced Learning Algorithms

Certificate Of Completion

• Week 1

  • Optional Labs
    • Neurons and Layers
    • Coffee Roasting in Tensorflow
    • CoffeeRoasting NumPy
  • Programming Assignment

• Week 2

  • Optional Labs
    • ReLU activation
    • Softmax Function
    • Multi-class Classification
    • Derivatives
    • Back propagation using a computation graph
  • Programming Assignment

• Week 3

  • Optional Labs
    • Model Evaluation and Selection
    • Diagnosing Bias and Variance
  • Programming Assignment

• Week 4

  • Optional Labs
    • Decision Trees
    • Trees Ensemble
  • Programming Assignment

Course 3 : Unsupervised Learning, Recommenders, Reinforcement Learning

Certificate Of Completion

• Week 1
  • Programming Assignment
    • k-means
    • Anomaly Detection
• Week 2
  • Optional Labs
    • PCA
  • Programming Assignments
    • Collaborative Filtering
    • Content Based Filtering
• Week 3
  • Optional Labs
    • Q-function
  • Programming Assignment
    • Deep Q-Learning