Last updated date: November 18, 2019
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Artificial intelligence online courses | adjusted.me

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The field of artificial intelligence and online AI courses is vast and includes a variety of different disciplines. As such, it also covers a wide breadth of skills and positions in the job marketplace. With roles in data science, machine learning, artificial neural network, deep learning, programming languages and many more, there is something for everyone within the AI field. The problem is, how do you get the skills you need to succeed in AI if you’re already a developer or software engineer?

One way is to take one of the many AI online courses available. But which one do you choose? Well, to help you out we’ve compiled a list of the best artificial intelligence online courses for developers currently available. We have excluded machine learning courses due to it being a popular subset of AI. We will discuss this more in a future article.

Online AI Courses Overview

Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learningicon by deeplearning.ai

This course is part of the “TensorFlow in Practice Specialization” and is designed to be accessible by Data Scientists, Machine Learning Engineers, CTOs, and Biostatisticians. If you wish to take the course, then be aware that experience with Python and a decent level of mathematics is an advantage. During the course you will learn:

  • Foundation and principles of Machine Learning and Deep Learning.
  • The best practices for using the popular open-source machine learning framework, TensorFlow.
  • How to build a basic neural network with TensorFlow.
  • Understand how to use convolutions to improve neural networks.

Deep Learning Nanodegree by Udacity

This short, Nanodegree style course takes about 4 months to complete and will teach you to build deep learning models. ​You will need at least an intermediate knowledge of Python to get the best front the course. The course covers:

  • An introduction to deep learning.
  • The ability to gain experience using development tools, such as Anaconda and Jupyter notebooks.
  • The basics of neural networks.
  • You will build your first network with Python and NumPy.
  • You will learn how to build convolutional networks and use them to classify images.
  • You will learn how to build recurrent networks and long short-term memory networks with PyTorch.

Convolutional Neural Networks in TensorFlowicon by deeplearning.ai

This is another course in the “TensorFlow in Practice” specialization and is designed for people with at least an intermediate level of Python coding. During the course you will learn:

  • How to handle real-world image data.
  • Plot loss and accuracy with a neural network.
  • Explore strategies that will prevent overfitting, including augmentation and dropout techniques.
  • Learn what transfer learning is and how learned features can be extracted from models.

AI & Deep Learning with TensorFlowicon by Edureka

This course teaches you how to master the concepts of AI such as SoftMax function, Autoencoder Neural Networks, Restricted Boltzmann Machine (RBM). It will also give you experience with AI libraries like Keras and TFLearn and hands-on experience of linear regression modeling to predict house prices. During the course you will learn:

  • The basics of Deep Learning.
  • What the limitations of Machine Learning are.
  • What exactly Deep Learning is.
  • The advantage of Deep Learning over Machine learning.
  • The real-life use of Deep Learning to solve problems.

AI Programming with Python by Udacity

This is another short, Nanodegree course taking about 3 months to complete. During the course, you will learn everything you need to start building your own AI applications. It is beneficial to have some basic programming knowledge and a good understanding of algebra. ​The course covers:

  • The basics to start coding with Python, including being able to draw on libraries and automation scripts.
  • Understand how to use the key Python tools of Jupyter Notebooks, NumPy, Anaconda, Pandas, and Matplotlib.
  • Understand the foundations of linear algebra, including vectors, linear transformations, and matrices.
  • Understand the foundations of calculus so you can train a neural network.
  • Gain a solid foundation in neural networks, deep learning, and PyTorch.

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Artificial intelligence online courses | adjusted.me

More in this series:

The field of artificial intelligence and online AI courses is vast and includes a variety of different disciplines. As such, it also covers a wide breadth of skills and positions in the job marketplace. With roles in data science, machine learning, artificial neural network, deep learning, programming languages and many more, there is something for everyone within the AI field. The problem is, how do you get the skills you need to succeed in AI if you’re already a developer or software engineer?

One way is to take one of the many AI online courses available. But which one do you choose? Well, to help you out we’ve compiled a list of the best artificial intelligence online courses for developers currently available. We have excluded machine learning courses due to it being a popular subset of AI. We will discuss this more in a future article.

Online AI Courses Overview

Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learningicon by deeplearning.ai

This course is part of the “TensorFlow in Practice Specialization” and is designed to be accessible by Data Scientists, Machine Learning Engineers, CTOs, and Biostatisticians. If you wish to take the course, then be aware that experience with Python and a decent level of mathematics is an advantage. During the course you will learn:

  • Foundation and principles of Machine Learning and Deep Learning.
  • The best practices for using the popular open-source machine learning framework, TensorFlow.
  • How to build a basic neural network with TensorFlow.
  • Understand how to use convolutions to improve neural networks.

Deep Learning Nanodegree by Udacity

This short, Nanodegree style course takes about 4 months to complete and will teach you to build deep learning models. ​You will need at least an intermediate knowledge of Python to get the best front the course. The course covers:

  • An introduction to deep learning.
  • The ability to gain experience using development tools, such as Anaconda and Jupyter notebooks.
  • The basics of neural networks.
  • You will build your first network with Python and NumPy.
  • You will learn how to build convolutional networks and use them to classify images.
  • You will learn how to build recurrent networks and long short-term memory networks with PyTorch.

Convolutional Neural Networks in TensorFlowicon by deeplearning.ai

This is another course in the “TensorFlow in Practice” specialization and is designed for people with at least an intermediate level of Python coding. During the course you will learn:

  • How to handle real-world image data.
  • Plot loss and accuracy with a neural network.
  • Explore strategies that will prevent overfitting, including augmentation and dropout techniques.
  • Learn what transfer learning is and how learned features can be extracted from models.

AI & Deep Learning with TensorFlowicon by Edureka

This course teaches you how to master the concepts of AI such as SoftMax function, Autoencoder Neural Networks, Restricted Boltzmann Machine (RBM). It will also give you experience with AI libraries like Keras and TFLearn and hands-on experience of linear regression modeling to predict house prices. During the course you will learn:

  • The basics of Deep Learning.
  • What the limitations of Machine Learning are.
  • What exactly Deep Learning is.
  • The advantage of Deep Learning over Machine learning.
  • The real-life use of Deep Learning to solve problems.

AI Programming with Python by Udacity

This is another short, Nanodegree course taking about 3 months to complete. During the course, you will learn everything you need to start building your own AI applications. It is beneficial to have some basic programming knowledge and a good understanding of algebra. ​The course covers:

  • The basics to start coding with Python, including being able to draw on libraries and automation scripts.
  • Understand how to use the key Python tools of Jupyter Notebooks, NumPy, Anaconda, Pandas, and Matplotlib.
  • Understand the foundations of linear algebra, including vectors, linear transformations, and matrices.
  • Understand the foundations of calculus so you can train a neural network.
  • Gain a solid foundation in neural networks, deep learning, and PyTorch.
 
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