This repository contains all the graded projects I completed for the Udacity Nanodegree Programs in 2021. If you have any questions, please feel free to contact me.
I chose Udacity because it offers many interesting courses and endless possibilities. My goal was to learn Data Science and Machine Learning with a solid theoretical foundation. I followed “The Data Science Hierarchy of Needs” from bottom to top and selected courses related to each topic. The total duration of the courses is 26 months (assuming 10 hours/week).
Note: Udacity reviewers provide valuable feedback even after passing a project. I am still working on implementing most of it.
Recommended time: 5 months, Level: Intermediate, Perequisite: Intermediate Python & SQL
Data Engineering enables you to harness the power of Big Data. You will learn how to create reliable and scalable data infrastructure, a key skill for advancing in the data field.
- Data Modeling with Postgres
- Data Modeling with Cassandra
- Data Warehouse
- Data Lake
- Data Pipelines with Airflow
Recommended time: 4 months, Level: Intermediate, Perequisite: Python & SQL
Learn to use Python, SQL, and statistics to find insights, communicate critical results, and create data-driven solutions.
After completing this course, you can advance your skills with the Data Scientist Nanodegree Program.
- Explore Weather Trends
- Investigate a Dataset
- Analyze A/B Test Results (Practical Statistics)
- Wrangle and Analyze Data (Data Wrangling)
- Communicate Data Findings (Data Visualization)
Recommended time: 4 months, Level: Advanced, Perequisite: Python, SQL & Statistics
The Data Scientist Nanodegree program teaches you the skills that employers look for in Data Scientist candidates, such as:
- Building supervised and unsupervised machine learning models
- Understanding neural networks, deep learning, and PyTorch
- Applying software engineering and data engineering principles
- Designing experiments and analyzing A/B test results
- Data Science Blog Post
- Disaster Response Pipeline (Data Engineering)
- Recommendations with IBM (Experimental Design & Recommendations)
- Capstone: Dog Project
Recommended time: 3 months, Level: Advanced, Perequisite: Algebra, Calculus, Statistics, & Python
The course follows the book by Peter Norvig, who also delivers many of the lectures in the program:
- English: Artificial Intelligence: A Modern Approach
- German: Künstliche Intelligenz: Ein moderner Ansatz
After completing this course, you can further your learning with the Nanodegree Programs in Deep Learning, Deep Reinforcement Learning, and Machine Learning Engineering.
- Build a Sudoku Solver
- Build a Forward-Planning Agent (Automated Planning)
- Build an Adversarial Game Playing Agent (Adversarial Search)
- Part of Speech Tagging (Probabilistic Models)
This Nanodegree Program used to have two terms: a basic one and a specialization in either computer vision or natural language processing. Now, these are two separate Nanodegree Programs.
Recommended time: 3 months, Level: Advanced, Perequisite: Python, Statistics, Machine Learning, & Deep Learning
- Facial Keypoint Detection
- Automatic Image Captioning
- Landmark Detection and Tracking
Recommended time: 3 months, Level: Advanced, Perequisite: Python, Statistics, Machine Learning, & Deep Learning
- Part of Speech Tagging
- Machine Translation
- Speech Recognizer
Recommended time: 4 months, Level: Intermediate, Perequisite: Basic Python
The course recommends the book by Andrew Trask who also gives a lecture on Sentiment Analysis:
- English: Grokking Deep Learning
- German: Neuronale Netze und Deep Learning kapieren: Der einfache Praxiseinstieg mit Beispielen in Python
The Deep Learning Nanodegree program teaches you how to use key techniques that power many of the most innovative AI solutions. You’ll explore different network architectures and applications, such as computer vision, natural language processing, and generative models, with a focus on practical examples.
- Predicting Bike-Sharing Patterns (Neural Networks)
- Landmark Classification & Tagging for Social Media (Convolutional Neural Networks)
- Generate TV Scripts (Recurrent Neural Networks)
- Generate Faces (Generative Adversarial Networks)
- Deploying a Sentiment Analysis Model (Deploying a Model)
Recommended time: 3 months, Level: Advanced, Perequisite: Experience with Python, Probability, Machine Learning, & Deep Learning.
The course recommends the book by Miguel Morales who also gives a lecture on Actor-Critic Methods:
- English: Grokking Deep Reinforcement Learning
The Deep Reinforcement Learning Nanodegree program shows you how to teach AI agents to master challenging tasks, such as playing games, controlling robots, and optimizing systems. You’ll learn the latest algorithms and techniques in this rapidly evolving field.
- Navigation (Value-Based Methods)
- Continuous Control (Policy-Based Methods)
- Collaboration and Competition (Multi-Agent Reinforcement Learning)
Recommended time: 3 months, Level: Intermediate, Perequisite: Intermediate Python & Machine Learning Algorithms
Master advanced machine learning techniques and algorithms, and learn how to deploy your models to production.
- Deploy a Sentiment Analysis Model (Machine Learning in Production)
- Plagiarism Detector (Machine Learning, Case Studies)
- Capstone: Dog Project
At the end of the course Udacity recommends:
- Write a blog post about the course experience or project.
- Apply to at least two jobs with "machine learning" in the description.