These are all my work for Machine Learning and Deep Learning Specialization on Coursera.
- Linear Regression
- Cost Function
- Gradient Descent
- NumPy and Vectorization
- Multiple Variable Linear Regression
- Feature scaling and Learning Rate (Multi-variable)
- Feature Engineering and Polynomial Regression
- Linear Regression using Scikit-Learn with Gradient Descent
- Linear Regression using Scikit-Learn (closed form, normal equation)
- Time Series introduction
- Classification
- Logistic Regression, Sigmoid function
- Logistic Regression, Decision Boundary
- Logistic Regression, Logistic Loss
- Cost Function for Logistic Regression
- Gradient Descent for Logistic Regression
- Logistic Regression using Scikit-Learn
- Overfitting
- Regularized Cost and Gradient
- Logistic Regression Implementation
- Model Evaluation and Selection
- Diagnosing Bias and Variance
- Advice for Applying Machine Learning
- Back propagation using a computation graph
- Understanding Derivatives
- Neural Networks for Handwritten Digit Recognition, Binary
- Decision Trees
- Trees Ensemble
- Neurons and Layers
- Simple Neural Network using Tensorflow
- Simple Neural Network using Numpy
- Multi-class Classification
- ReLU activation
- Softmax Function
- Neural Networks for Handwritten Digit Recognition, Multiclass
- Anomaly Detection
- K-means Clustering - Clustering Algorithm
- PCA - An example on Exploratory Data Analysis
- Collaborative Filtering Recommender Systems
- Logistic Regression with a Neural Network mindset
- Python Basics with Numpy
- Planar data classification with one hidden layer
- Building your Deep Neural Network: Step by Step
- Deep Neural Network for Image Classification: Application
- Gradient Checking
- Initialization
- Regularization
- Optimization Methods
- Introduction to TensorFlow