- Regression
- Perceptron Algorithms
- Decision Trees
- Naive Bayes
- Support Vector Machines
- Ensemble of Learners
- Evaluation Metrics
- Training and Tuning Models
- Introduction to Neural Networks
- Implementing Gradient Descent
- Training Neural Networks
- Deep Learning with TensorFlow
- Clustering
- Hierarchical and Density-Based Clustering
- Gaussian Mixture Models
- Dimensionality Reduction
Apply supervised learning techniques on data collected for the US census to help CharityML (a fictitious charity organization) identify groups of people that are most likely to donate to their cause.
Define and train a neural network in TensorFlow that learns to classify images; going from image data exploration to network training and evaluation.
Study a real dataset of customers for a company, and apply several unsupervised learning techniques in order to segment customers into similar groups and extract information that may be used for marketing or product improvement.