UDACITY NANODEGREE
Intro to Machine Learning with PyTorch
The ultimate goal of the Intro to Machine Learning with PyTorch Nanodegree program is to help students learn machine learning techniques such as data transformation and algorithms that can find patterns in data and apply machine learning algorithms to tasks of their own design.
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Introduction to Machine Learning
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Supervised Learning
- Regression
- Perceptron Algorithms
- Decision Trees
- Naive Bayes
- Support Vector Machines
- Ensemble of Learners
- Evaluation Metrics
- Training and Tuning Models
- Deep Learning with PyTorch
- Introduction to Neural Networks
- Implementing Gradient Descent
- Training Neural Networks
- Deep Learning with PyTorch
- Unsupervised Learning
- Clustering
- Hierarchical and Density-Based Clustering
- Gaussian Mixture Models
- Dimensionality Reduction
Projects:
- Finding Donors for CharityML: 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.
- Create Your Own Image Classifier: Define and train a neural network in PyTorch that learns to classify images; going from image data exploration to network training and evaluation.
- Identify Customer Segments with Arvato: 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.