Machine learning Specialization

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Basic

1.Overview of Machine Learning
- 1.Welcome to machine learning!.mp4
  
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- 2.Applications of machine learning.mp4
  
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2.Supervised vs. Unsupervised Machine Learning
- 1.What is machine learning_.mp4
  
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- 2.Supervised learning part 1.mp4
  
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- 3.Supervised learning part 2.mp4
  
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- 4.Unsupervised learning part 1.mp4
  
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- 5.Unsupervised learning part 2.mp4
  
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- 6.Jupyter Notebooks.mp4
  
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Lab
- C1_W1_Lab01_Python_Jupyter_Soln.ipynb
  
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Data Lab Python and Jupyter Notebooks
- Files.zip
  
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3.Regression Model
- 1.Linear regression model part 1.mp4
  
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- 2.Linear regression model part 2.mp4
  
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- 3.Cost function formula.mp4
  
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- 4.Cost function intuition.mp4
  
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- 5.Visualizing the cost function.mp4
  
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- 6.Visualization examples.mp4
  
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Lab
- C1_W1_Lab02_Model_Representation_Soln.ipynb
  
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- C1_W1_Lab03_Cost_function_Soln.ipynb
  
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Optional lab: Cost function
- Files.zip
  
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Optional lab: Model representation
- Files.zip
  
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4.Train the model with gradient descent
- 1.Gradient descent.mp4
  
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- 2.Implementing gradient descent.mp4
  
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- 3.Gradient descent intuition.mp4
  
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- 4.Learning rate.mp4
  
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- 5.Gradient descent for linear regression.mp4
  
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- 6.Running gradient descent.mp4
  
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Lab_ Gradient descent
- C1_W1_Lab04_Gradient_Descent_Soln.ipynb
  
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Optional lab: Gradient descent
- Files.zip
  
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Practice quiz_ Regression
- Screenshot from 2023-03-02 09-45-32.png
  
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Practice quiz_ Supervised vs unsupervised learning
- Screenshot from 2023-03-02 09-42-57.png
  
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Practice quiz_ Train the model with gradient descent
- Screenshot from 2023-03-02 09-47-44.png
  
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1.Multiple linear regression
- 1.Multiple features.mp4
  
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- 2.Vectorization part 1.mp4
  
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- 3.Vectorization part 2.mp4
  
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- 4.Gradient descent for multiple linear regression.mp4
  
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Lab
- C1_W2_Lab01_Python_Numpy_Vectorization_Soln.ipynb
  
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- C1_W2_Lab02_Multiple_Variable_Soln.ipynb
  
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Optional Lab: Multiple linear regression
- Files.zip
  
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Optional lab: Python, NumPy and vectorization
- Files.zip
  
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2. Gradient descent in practice
- 1.Feature scaling part 1.mp4
  
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- 2.Feature scaling part 2.mp4
  
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- 3.Checking gradient descent for convergence.mp4
  
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- 4.Choosing the learning rate.mp4
  
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- 5.Feature engineering.mp4
  
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- 6.Polynomial regression.mp4
  
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Lab
- C1_W2_Lab03_Feature_Scaling_and_Learning_Rate_Soln.ipynb
  
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- C1_W2_Lab04_FeatEng_PolyReg_Soln.ipynb
  
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- C1_W2_Lab05_Sklearn_GD_Soln.ipynb
  
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Optional lab: Feature engineering and Polynomial regression
- Files.zip
  
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Optional Lab: Feature scaling and learning rate
- Files.zip
  
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Optional lab: Linear regression with scikit-learn
- Files.zip
  
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Assignment
- C1_W2_Linear_Regression.ipynb
  
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- Files.zip
  
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Practice quiz_ Gradient descent in practice
- Screenshot from 2023-03-02 09-53-18.png
  
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- Screenshot from 2023-03-02 09-53-24.png
  
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Practice quiz_ Multiple linear regression
- Screenshot from 2023-03-02 09-50-37.png
  
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1.Classification with logistic regression
- 1.Motivations.mp4
  
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- 2.Logistic regression.mp4
  
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- 3.Decision boundary.mp4
  
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Lab
- C1_W3_Lab01_Classification_Soln.ipynb
  
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- C1_W3_Lab02_Sigmoid_function_Soln.ipynb
  
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- C1_W3_Lab03_Decision_Boundary_Soln.ipynb
  
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Optional lab: Classification
- Files.zip
  
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Optional lab: Decision boundary
- Files.zip
  
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Optional lab: Sigmoid function and logistic regression
- Files.zip
  
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2. Practice quiz_ Classification with logistic regression
- Screenshot from 2023-03-02 09-57-11.png
  
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- Screenshot from 2023-03-02 09-57-16.png
  
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3.Cost function for logistic regression
- 1.Cost function for logistic regression.mp4
  
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- 2.Simplified Cost Function for Logistic Regression.mp4
  
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Lab
- C1_W3_Lab04_LogisticLoss_Soln.ipynb
  
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- C1_W3_Lab05_Cost_Function_Soln.ipynb
  
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Optional lab: Cost function for logistic regression
- Files.zip
  
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Optional lab: Logistic loss
- Files.zip
  
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4.Practice quiz_ Cost function for logistic regression
- Screenshot from 2023-03-02 10-07-17.png
  
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5.Gradient descent for logistic regression
- 1.Gradient Descent Implementation.mp4
  
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Lab
- C1_W3_Lab06_Gradient_Descent_Soln.ipynb
  
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- C1_W3_Lab07_Scikit_Learn_Soln.ipynb
  
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Optional lab: Gradient descent for logistic regression
- Files.zip
  
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Optional lab: Logistic regression with scikit-learn
- Files.zip
  
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6.Practice quiz_ Gradient descent for logistic regression
- Screenshot from 2023-03-02 10-09-07.png
  
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7.The problem of overfitting
- 1.The problem of overfitting.mp4
  
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- 2.Addressing overfitting.mp4
  
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- 3.Cost function with regularization.mp4
  
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- 4.Regularized linear regression.mp4
  
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- 5.Regularized logistic regression.mp4
  
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Lab
- C1_W3_Lab08_Overfitting_Soln.ipynb
  
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- C1_W3_Lab09_Regularization_Soln.ipynb
  
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Optional lab: Overfitting
- Fil.zip
  
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Optional lab: Regularization
- Files.zip
  
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8.Practice quiz_ The problem of overfitting
- Screenshot from 2023-03-02 10-10-40.png
  
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- Screenshot from 2023-03-02 10-10-44.png
  
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Assignment
- C1_W3_Logistic_Regression.ipynb
  
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- Files.zip
  
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1.Neural networks intuition
- 1.Welcome!.mp4
  
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- 2.Neurons and the brain.mp4
  
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- 3.Demand Prediction.mp4
  
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- 4.Example_ Recognizing Images.mp4
  
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2.Practice quiz_ Neural networks intuition
- Screenshot from 2023-03-02 10-24-55.png
  
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3.Neural network model
- 1.Neural network layer.mp4
  
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- 2.More complex neural networks.mp4
  
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- 3.Inference_ making predictions (forward propagation).mp4
  
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Lab
- C2_W1_Lab01_Neurons_and_Layers.ipynb
  
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- Files.zip
  
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4.Practice quiz_ Neural network model
- Screenshot from 2023-03-02 10-26-09.png
  
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- Screenshot from 2023-03-02 10-26-14.png
  
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5.TensorFlow implementation
- 1.Inference in Code.mp4
  
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- 2.Data in TensorFlow.mp4
  
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- 3.Building a neural network.mp4
  
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- C2_W1_Lab02_CoffeeRoasting_TF.ipynb
  
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Lab
- C2_W1_Lab02_CoffeeRoasting_TF.ipynb
  
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- Files.zip
  
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6.Practice quiz_ TensorFlow implementation
- Screenshot from 2023-03-02 10-28-54.png
  
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- Screenshot from 2023-03-02 10-28-59.png
  
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- Screenshot from 2023-03-02 10-29-04.png
  
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7.Neural network implementation in Python
- 1.Forward prop in a single layer.mp4
  
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- 2.General implementation of forward propagation.mp4
  
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Lab
- C2_W1_Lab03_CoffeeRoasting_Numpy.ipynb
  
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- Files.zip
  
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8.Practice quiz_ Neural network implementation in Python
- Screenshot from 2023-03-02 10-31-53.png
  
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- Screenshot from 2023-03-02 10-31-59.png
  
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- Screenshot from 2023-03-02 10-32-03.png
  
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9.Speculations on artificial general intelligence (AGI)
- 1.Is there a path to AGI_.mp4
  
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10.Vectorization optional
- 1.How neural networks are implemented efficiently.mp4
  
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- 2.Matrix multiplication.mp4
  
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- 3.Matrix multiplication rules.mp4
  
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- 4.Matrix multiplication code.mp4
  
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Assignment
- C2_W1_Assignment.ipynb
  
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- Files.zip
  
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1.Neural Network Training
- 1.TensorFlow implementation.mp4
  
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- 2.Training Details.mp4
  
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2.Practice quiz_ Neural Network Training
- Screenshot from 2023-03-02 10-36-55.png
  
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- Screenshot from 2023-03-02 10-37-02.png
  
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3.Activation Functions
- 1.Alternatives to the sigmoid activation.mp4
  
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- 2.Choosing activation functions.mp4
  
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- 3.Why do we need activation functions_.mp4
  
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Lab
- C2_W2_Relu.ipynb
  
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- Files.zip
  
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4.Practice quiz_ Activation Functions
- Screenshot from 2023-03-02 10-38-38.png
  
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- Screenshot from 2023-03-02 10-38-43.png
  
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5. Multiclass Classification
- 1.Multiclass.mp4
  
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- 2.Softmax.mp4
  
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- 3.Neural Network with Softmax output.mp4
  
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- 4.Improved implementation of softmax.mp4
  
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- 5.Classification with multiple outputs (Optional).mp4
  
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Lab
- C2_W2_Multiclass_TF.ipynb
  
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- C2_W2_SoftMax.ipynb
  
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Multiclass
- Files.zip
  
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Softmax
- Files.zip
  
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6.Practice quiz_ Multiclass Classification
- Screenshot from 2023-03-02 10-43-21.png
  
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- Screenshot from 2023-03-02 10-43-26.png
  
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- Screenshot from 2023-03-02 10-43-30.png
  
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7.Additional Neural Network Concepts
- 1.Advanced Optimization.mp4
  
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- 2.Additional Layer Types.mp4
  
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8.Practice quiz_ Additional Neural Network Concepts
- Screenshot from 2023-03-02 10-44-53.png
  
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- Screenshot from 2023-03-02 10-44-57.png
  
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9.Back Propagation (Optional)
- 1.What is a derivative_ (Optional).mp4
  
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- 2.Computation graph (Optional).mp4
  
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- 3.Larger neural network example (Optional).mp4
  
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Lab
- C2_W2_Backprop.ipynb
  
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- C2_W2_Derivatives.ipynb
  
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Optional Lab: Back propagation
- Files.zip
  
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Optional Lab: Derivatives
- Files.zip
  
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Assignment
- C2_W2_Assignment.ipynb
  
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- Files.zip
  
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1.Advice for applying machine Learning
- 1.Deciding what to try next.mp4
  
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- 2.Evaluating a model.mp4
  
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- 3.Model selection and training cross validation test sets.mp4
  
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Lab
- C2W3_Lab_01_Model_Evaluation_and_Selection.ipynb
  
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- Files.zip
  
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2.Practice quiz_ Advice for applying machine learning
- Screenshot from 2023-03-02 10-49-01.png
  
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- Screenshot from 2023-03-02 10-49-05.png
  
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3.Bias and variance
- 1.Diagnosing bias and variance.mp4
  
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- 2.Regularization and bias variance.mp4
  
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- 3.Establishing a baseline level of performance.mp4
  
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- 4.Learning curves.mp4
  
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- 5.Deciding what to try next revisited.mp4
  
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- 6.Bias variance and neural networks.mp4
  
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Lab
- C2W3_Lab_02_Diagnosing_Bias_and_Variance.ipynb
  
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- Files.zip
  
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4.Practice quiz_ Bias and variance
- Screenshot from 2023-03-02 10-52-17.png
  
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- Screenshot from 2023-03-02 10-52-24.png
  
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- Screenshot from 2023-03-02 10-52-29.png
  
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5.Machine learning development process
- 1.Iterative loop of ML development.mp4
  
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- 2.Error analysis.mp4
  
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- 3.Adding data.mp4
  
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- 4.Transfer learning_ using data from a different task.mp4
  
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- 5.Full cycle of a machine learning project.mp4
  
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- 6.Fairness, bias, and ethics.mp4
  
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6.Practice quiz_ Machine learning development process
- Screenshot from 2023-03-02 10-53-56.png
  
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- Screenshot from 2023-03-02 10-54-01.png
  
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- Screenshot from 2023-03-02 10-54-05.png
  
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7.Skewed datasets (optional)
- 1.Error metrics for skewed datasets.mp4
  
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- 2.Trading off precision and recall.mp4
  
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Assignment
- C2_W3_Assignment.ipynb
  
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- Files.zip
  
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1.Decision trees
- 1.Decision tree model.mp4
  
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- 2.Learning Process.mp4
  
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2.Practice quiz_ Decision trees
- Screenshot from 2023-03-02 10-17-04.png
  
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- Screenshot from 2023-03-02 10-17-16.png
  
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3.Decision tree learning
- 1.Measuring purity.mp4
  
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- 2.Choosing a split_ Information Gain.mp4
  
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- 3.Putting it together.mp4
  
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- 4.Using one-hot encoding of categorical features.mp4
  
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- 5.Continuous valued features.mp4
  
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- 6.Regression Trees (optional).mp4
  
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lab
- C2_W4_Lab_01_Decision_Trees.ipynb
  
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- Files.zip
  
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4.Practice quiz_ Decision tree learning
- Screenshot from 2023-03-02 10-20-22.png
  
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- Screenshot from 2023-03-02 10-20-26.png
  
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- Screenshot from 2023-03-02 10-20-31.png
  
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- Screenshot from 2023-03-02 10-20-37.png
  
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5.Tree ensembles
- 1.Using multiple decision trees.mp4
  
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- 2.Sampling with replacement.mp4
  
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- 3.Random forest algorithm.mp4
  
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- 4.XGBoost.mp4
  
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- 5.When to use decision trees.mp4
  
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Lab
- C2_W4_Lab_02_Tree_Ensemble.ipynb
  
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- Files.zip
  
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6.Practice quiz_ Tree ensembles
- Screenshot from 2023-03-02 10-22-20.png
  
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- Screenshot from 2023-03-02 10-22-25.png
  
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Assignment
- C2_W4_Decision_Tree_with_Markdown.ipynb
  
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- Files.zip
  
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1. Welcome to the course
- 1.Welcome!.mp4
  
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2.Clustering
- 1.What is clustering_.mp4
  
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- 2.K-means intuition.mp4
  
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- 3.K-means algorithm.mp4
  
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- 4.Optimization objective.mp4
  
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- 5.Initializing K-means.mp4
  
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- 6.Choosing the number of clusters.mp4
  
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3. Clustering Quiz
- Screenshot from 2023-03-02 10-58-50.png
  
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- Screenshot from 2023-03-02 10-58-55.png
  
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4.Anomaly detection
- 1.Finding unusual events.mp4
  
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- 2.Gaussian (normal) distribution.mp4
  
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- 3.Anomaly detection algorithm.mp4
  
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- 4.Developing and evaluating an anomaly detection system.mp4
  
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- 5.Anomaly detection vs. supervised learning.mp4
  
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- 6.Choosing what features to use.mp4
  
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5. Anomaly detection Quiz
- Screenshot from 2023-03-02 11-01-01.png
  
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- Screenshot from 2023-03-02 11-01-06.png
  
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Lab1
- C3_W1_KMeans_Assignment.ipynb
  
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- Files.zip
  
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Lab2
- C3_W1_Anomaly_Detection.ipynb
  
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- Files.zip
  
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1.Collaborative filtering
- 1.Making recommendations.mp4
  
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- 2.Using per-item features.mp4
  
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- 3.Collaborative filtering algorithm.mp4
  
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- 4.Binary labels_ favs, likes and clicks.mp4
  
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2.Collaborative Filtering Quiz
- Screenshot from 2023-03-02 11-06-32.png
  
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- Screenshot from 2023-03-02 11-06-36.png
  
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3.Recommender systems implementation
- 1.Mean normalization.mp4
  
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- 2.TensorFlow implementation of collaborative filtering.mp4
  
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- 3.Finding related items.mp4
  
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4. Recommender systems implementation Quiz
- Screenshot from 2023-03-02 11-09-14.png
  
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- Screenshot from 2023-03-02 11-09-19.png
  
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5.Content-based filtering
- 1.Collaborative filtering vs Content-based filtering.mp4
  
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- 2.Deep learning for content-based filtering.mp4
  
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- 3.Recommending from a large catalogue.mp4
  
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- 4.Ethical use of recommender systems.mp4
  
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- 5.TensorFlow implementation of content-based filtering.mp4
  
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6.Principal Component Analysis
- 1.Reducing the number of features (optional).mp4
  
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- 2.PCA algorithm (optional).mp4
  
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- 3.PCA in code (optional).mp4
  
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Lab
- C3_W2_Lab01_PCA_Visualization_Examples.ipynb
  
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- Files.zip
  
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Content-based filtering Quiz
- Screenshot from 2023-03-02 11-11-31.png
  
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- Screenshot from 2023-03-02 11-11-38.png
  
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- Screenshot from 2023-03-02 11-11-44.png
  
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- Screenshot from 2023-03-02 11-11-49.png
  
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Lab 1
- C3_W2_Collaborative_RecSys_Assignment.ipynb
  
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- Files.zip
  
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Lab 2
- Files.zip
  
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1.Reinforcement learning introduction
- 1.What is Reinforcement Learning_.mp4
  
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- 2.Mars rover example.mp4
  
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- 3.The Return in reinforcement learning.mp4
  
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- 4.Making decisions_ Policies in reinforcement learning.mp4
  
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- 5.Review of key concepts.mp4
  
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2.Reinforcement learning introduction Quiz
- Screenshot from 2023-03-02 11-16-58.png
  
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- Screenshot from 2023-03-02 11-17-03.png
  
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3.State-action value function
- 1.State-action value function definition.mp4
  
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- 2.State-action value function example.mp4
  
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- 3.Bellman Equations.mp4
  
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- 4.Random (stochastic) environment (Optional).mp4
  
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Lab
- Files.zip
  
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- State-action value function example.ipynb
  
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4.State-action value function Quiz
- Screenshot from 2023-03-02 11-19-07.png
  
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5.Continuous state spaces
- 1.Example of continuous state space applications.mp4
  
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- 2.Lunar lander.mp4
  
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- 3.Learning the state-value function.mp4
  
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- 4.Algorithm refinement_ Improved neural network architecture.mp4
  
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- 5.Algorithm refinement_ ϵ-greedy policy.mp4
  
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- 6.Algorithm refinement_ Mini-batch and soft updates (optional).mp4
  
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- 7.The state of reinforcement learning.mp4
  
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6.Continuous state spaces Quiz
- Screenshot from 2023-03-02 11-20-46.png
  
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7.Summary and thank you
- 1.Summary and thank you.mp4
  
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Assignment
- C3_W3_A1_Assignment.ipynb
  
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- Files.zip
  
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