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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