Microsoft’s Artificial Intelligence for Beginners
There’s a new free, self-paced, online course about Artificial Intelligence from Microsoft’s Azure Cloud Advocates. Its 24 lesson curriculum, expected to take 12 weeks to complete, is targeted at those brand new to Artificial Intelligence.
This is a continuation of last year’s Microsoft’s Machine Learning for Beginners.That course made a clear distinction between Machine Learning and AI – it was about “classic machine learning” and did not concern itself with artificial intelligence. That is the job of its sibling course, AI for Beginners.This separation of topics meant that ML for Beginners was not as complicated as AI for Beginners is, well at the novice level anyway.
Both courses require Python. ML uses Sci-kit and with good reason :
Python certainly is the most popular language of doing ML, mainly due to the number of relevant libraries available. scikit-learn is one of the top Machine Learning libraries alongside PyTorch, NumPy, SciPy, TensorFlow and Theano.
Additionally, scikit-learn is one of the easiest to learn as such perfect for beginning one’s ML journey. That doesn’t mean that it lacks functionality though; it is perfectly capable of pulling off many ML tasks such as classification, clustering, pre-processing, regression, etc.
AI on the other hand, uses what ML doesn’t – that is it demonstrates Neural Networks and Deep Learning with TensorFlow and PyTorch. So at the higher level, the curriculum is comprised of:
- Different approaches to Artificial Intelligence, including the “good old” symbolic approach with Knowledge Representation and reasoning (GOFAI)
- Neural Networks and Deep Learning, as said with TensorFlow and PyTorch
- Neural Architectures for working with images and text
- Less popular AI approaches, such as Genetic Algorithms and Multi-Agent Systems
That overview in detail translates to :
- Introduction to AI
Introduction and History of AI
- Symbolic AI
Knowledge Representation and Expert Systems
- Introduction to Neural Networks
Multi-Layered Perceptron and Creating our own
Intro to Frameworks (PyTorch/TensorFlow)
Microsoft Learn Module on Computer Vision
Intro to Computer Vision. OpenCV
Convolutional Neural Networks
Pre-trained Networks and Transfer Learning
Autoencoders and VAEs
Generative Adversarial Networks
Artistic Style Transfer
Semantic Segmentation. U-Net
- Natural Language Processing
Microsoft Learn Module on Natural Language
Text Representation. Bow/TF-IDF
Semantic word embeddings. Word2Vec and GloVe
Language Modeling. Training your own embeddings
Recurrent Neural Networks
Generative Recurrent Networks
Named Entity Recognition
Large Language Models, Prompt Programming and Few-
- Other AI Techniques
Deep Reinforcement Learning
- AI Ethics
AI Ethics and Responsible AI
Multi-Modal Networks, CLIP and VQGAN
Like its ML predecessor, it is carefully planned and well structured. It includes quizzes, doodles, assignments, projects, group discussions and some executable Jupyter Notebooks, which are often specific to the framework (PyTorch or TensorFlow).
And, in my opinion, it’s quite complete and perfectly addressed to CS students as a side-dish to their classes or for those having touched the subject at college and are looking to expand more under the scope of a Masters degree or of finding a job.
Microsoft with its three part series, Data Science, ML and Al, all for beginners, has managed to cover those closely interrelated fields, giving a holistic education to those interested. In the current job landscape these fields could be used in isolation or in combination. The three-part series has every case covered.
And,of course here at I Programmer we have a keen interest in anything to do with Data/ML/AI as well as the topic of Ethics and as such we highlight many relevant educational resources. You’ll find some of those at the end of this article.
If until now you were finding it confusing as to how to get started in the science of AI, then confusion begone. AI for beginners is the perfect place to start from.
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