Learning new subjects can be daunting — especially complex tech subjects. Robots, self-driving cars, flying drones, and naturally speaking personal assistants are complex artificially intelligent technologies — and you can learn how to make them run.
Many people wonder how to break into the field of artificial intelligence (AI). AI is an exciting field full of progressive technologies with the potential to improve people’s lives. AI is also a challenging field full of intimidating terms and concepts like “Python,” “machine learning,” “TensorFlow,” and “neural networks.”
Udacity’s team wants to help you overcome feelings of doubt or anxiety you might have about expanding your real-world skill set to include artificial intelligence. Learn how you can get into the field of AI and do amazing things.
Program Computers to Run AI
To operate artificially intelligent technologies, you should understand how to program computers. Here’s a list of some of the more popular programming languages you should learn today.
Python
Python is one of the computer programming languages most often used by AI researchers and developers. The language is user-friendly: it’s simple, flexible, and scalable.
Artificial intelligence specialists like the fact that Python is open source. Python is also platform-independent: it can run on all operating systems.
Python has many open-access online resources including:
- Programming libraries: language documentation, classes, data types, and more
- Frameworks: computing structures used in application development that include code libraries, code compilers, and support programs
Python’s popular libraries include:
- NumPy: useful for multi-dimensional arrays and matrices
- Pandas: useful for numerical tables and time-series data
- Matplotlib: useful for plotting and displaying data
C++
Many AI developers like C++ for its speed. C++ is an efficient, high-performance, general-purpose computer language that is:
- Generic: a programming style based on algorithms (rules or instructions) that are specified and instantiated as needed
- Functional: a declarative programming style based on using code (computer instructions) as functions (code units designed to complete specific tasks)
- Object-oriented: a programming paradigm based on programmable objects (analogous to real-world objects and can include variables, data structures, and functions)
Technologists use C++ to develop many useful products including robots, self-driving cars, video games, and Internet browsers (e.g. Google Chrome).
Java
Java is a popular programming language known for being user-friendly. It is a high-level language (it more closely resembles natural human language than other programming languages) with a simple syntax (rules and structure) and platform independence. Java is also general-purpose, class-based (programmers use classes of objects), and object-oriented. It is related to C and C++.
Java has its own Java Machine Learning Library (Java-ML) that is particularly useful for machine learning projects such as building artificially intelligent robots and running neural networks.
Many Java-compatible AI libraries exist including:
- Neuroph: useful for neural network development
- Deep Learning for Java (DL4J): useful for running deep neural networks (e.g. convolutional neural networks, recurrent neural networks) via distributed computing systems
- Apache OpenNLP: useful for natural language processing (e.g. speech tagging, sentence segmentation)
- Jenetics: useful for evolutionary AI algorithms like genetic algorithms (stochastic search algorithms that operate on populations of solutions)
R
R is both a programming language and a software environment widely used for:
- Statistical computing: statistical methods that rely heavily on computers and, often, large datasets
- Data mining: a process for analyzing and extracting information from large datasets
Scala
Scala is user-friendly, interoperable (it can use and exchange information with other programming language environments), and ideal for computer program maintenance. Scala is also functional and object-oriented.
AI specialists like Scala because it tends to handle complex algorithms (e.g. deep neural network algorithms) well. It is also useful for streaming data across large scales (e.g. cloud computing).
Smile is a Scala machine learning framework that contains algorithms for machine learning functions like data classification and feature selection.
Comprehend Tech Concepts and Tools to Learn AI
To get into AI, familiarize yourself with AI’s major concepts: machine learning, deep learning, neural networks, and learning models. Others include algorithms, unsupervised learning, supervised learning, reinforcement learning, and natural language processing.
When you feel comfortable with AI’s major concepts and programming languages, you can work on AI projects online. There are many freely accessible resources to explore AI in digital workspaces.
Platforms, Frameworks and Libraries
TensorFlow is a popular machine learning platform with libraries and tools. TensorFlow enables you to train machine learning models in the cloud, in browsers, and on devices across computing languages.
PyTorch is another popular framework full of libraries and tools. It is useful for machine learning tasks like natural language processing (how computers process human language data) and computer vision (how computers process visual imagery).
Microsoft’s Azure cloud computing platform is ideal for AI projects such as machine learning model training (e.g. supervised neural network training). It supports many development tools (e.g. Jupyter notebooks), frameworks (e.g. PyTorch), and computing languages (e.g. Python, R).
Datasets
To work on AI projects, specialists often require large datasets. Online data repositories are numerous and include:
- Google Dataset Search: publicly available datasets on nearly any topic
- Microsoft Research Open Data: free datasets for research in computer vision, natural language processing, and more
- U.S. Government Open Data: a federal, state, and local data repository
- VisualData Discovery: datasets useful for computer vision projects
Learn Real-world AI Skills Online With Tech Experts
Online learning platforms like Udacity offer you opportunities to learn and advance your skills in technology and business. The Udacity team is passionate about artificial intelligence and wants to teach you AI.
Udacity’s School of Artificial Intelligence is a great resource for learners across skill levels. The School offers many comprehensive programs like:
- Become a Machine Learning Engineer
- Become a Natural Language Processing Expert
- Artificial Intelligence for Trading
Udacity’s courses teach you about a range of fundamental AI concepts such as deep reinforcement learning, natural language processing, and computer vision. They help you understand and practice:
- Deep learning architectures (e.g. deep neural networks)
- How to train a simulated robotic arm to reach specific targets
- Machine translation: the processes machines use to translate human languages
- How to classify and caption images with deep learning
Udacity’s learning resources include online support structures such as mentors to answer your questions and career services for your portfolio review. Consider registering for an engrossing AI Nanodegree program today!
