The Nanodegree has two parts: (i) Computer Vision, Deep Learning, and Sensor Fusion; (ii) Localization, Path Planning, Control, and System Integration.

During the course, I did the end-to-end self-driving car projects:

• Finding lane lines

• Traffic signs classification;

• Behavior cloning;

• Extended Kalman filters;

• Kidnapped vehicle using particle filters;

• Path planning on the highway;

• PID controller;

• Programming a real self-driving car using ROS.

More information could be found here

The specialization has 3 courses: (i) AI for Medical Diagnosis; (ii) AI for Medical Prognosis; (iii) AI for Medical Treatment.

These courses covered how to diagnose chest x-rays and brain scans, evaluate models, handle missing data, estimate the effect of treatments, and efficiently automate the task of labeling medical datasets using natural language entity extraction and question-answering methods.

The five courses in the specialization are:

1. Neural networks and deep learning;

2. Improving deep neural networks: hyper-parameter tuning, regularization and optimization;

3. Structuring machine learning projects;

4. Convolutional neural networks;

5. Sequence models.

The course covered (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks); (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning); (iii) Best practices in machine learning (bias/variance theory, innovation process in machine learning and AI).