Archived lab assignments from Andrew Ng’s Coursera courses, for revision and quick lookup

Deep Learning

• 1st Course: Neural Networks and Deep Learning
  • Lab1: Python Basics with Numpy
  • Lab2: Logistic Regression with a Neural Network Mindset
  • Lab3: Planar Data Classification with One Hidden Layer
  • Lab4: Building your Deep Neural Network Step by Step
  • Lab5: Deep Neural Network - Application
• 2nd Course: Improving Deep Neural Networks - Hyperparameter Tuning, Regularization and Optimization
  • Lab1: Initialization
  • Lab2: Regularization
  • Lab3: Gradient Checking
• 3rd Course: Structuring Machine Learning Projects
• 4th Course: Convolutional Neural Networks
• 5th Course: Sequence Models

Machine Learning

• 1st Course: Python and Jupyter Notebooks
  • Lab1: Python and Jupyter Notebooks
  • Lab2: Model Representation
  • Lab3: Cost Function
  • Lab4: Gradient Descent
  • Lab5: Python, NumPy and vectorization
  • Lab6: Multiple linear regression
  • Lab7: Feature scaling and learning rate
  • Lab8: Feature engineering and Polynomial regression
  • Lab9: Linear regression with scikit-learn
  • Lab10: Linear Regression
  • Lab11: Classification
  • Lab12: Sigmoid function and logistic regression
  • Lab13: Decision boundary
  • Lab14: Logistic loss
  • Lab15: Cost function for logistic regression
  • Lab16: Gradient descent for logistic regression
  • Lab17: Logistic regression with scikit-learn
  • Lab18: Overfitting
  • Lab19: Regularization
  • Lab20: logistic regression
• 2nd Course: Advanced Learning Algorithms
  • Lab1: Neurons and Layers
  • Lab2: Coffee Roasting in Tensorflow
  • Lab3: CoffeeRoastingNumPy
  • Lab4: Neural Networks for Binary Classification
  • Lab5: ReLU activation
  • Lab6: Softmax
  • Lab7: Multiclass
  • Lab8: Derivatives
  • Lab9: Back propagation
  • Lab10: Neural Networks for Multiclass classification
  • Lab11: Model Evaluation and Selection
  • Lab12: Diagnosing Bias and Variance
  • Lab13: Advice for Applying Machine Learning
  • Lab14: Decision Trees
  • Lab15: Tree Ensembles
  • Lab16: Decision Trees (Assignment)
• 3rd Course: Unsupervised Learning, Recommenders, Reinforcement Learning
  • Lab1: k-means
  • Lab2: Anomaly Detection
  • Lab3: Collaborative Filtering Recommender Systems
  • Lab4: Deep Learning for Content-Based Filtering
  • Lab5: PCA and data visualization
  • Lab6: State-action value function
  • Lab7: Reinforcement Learning