Truffles (free) of Machine Learning Jungle

Where to find good (and free) stuff to learn about machine learning

Photo by Tamara Menzi on Unsplash

If you’re reading this post, at least machine learning (ML) has got your attention. Sooner or later, you might ask yourself where you could learn about ML in a bit more detailed. So do I. ‘Google it’ would be a natural instinct, but I often find myself wandering in a ML jungle. It’s intimidating for a novice gatherer. In this post, I wish to lay out the terrain and share locations of ML truffles I have found. This is definitely an on-going post lately updated in May 2018. I welcome any comments about other useful truffle locations or if ones in the post become dead or inaccurate.

Starters

• Deep Learning Specialization from Andrew Ng offered on Coursera (deeplearning.ai) — I highly recommend this because I took it and it tremendously gives me a good foundation in ML. Free videos to watch on Coursera, but need subscriptions to work on exercises. Read more about the specialization’s content in my review post here.
• Deep Learning book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (www.deeplearningbook.org) — This can very well be handbook of deep learning. A lot of maths makes it fit for serious researchers and data scientists type. The chapters are all free to download. I have found some github combining them into one file, which is quite nice.
• Python booster from Introduction to Computer Science and Programming using Python offered by MITx (link) — I took this one to learn about Python.
• AI courses from Stanford listed in this survival guide.

Computer Vision

• Convolutional Neural Network course by Andrew Ng.
• Visual recognition using convolutional neural networks offered at Stanford University — very good next-step study from Andrew Ng’s course on convolutional neural network.

Sharpen the saw

• Kaggle — public competitions and practice playground for machine learning and data science. Most ML challenges will be posted here.

Research Level

• arXiv.org — open-source research paper repository.

Resources most people forget

• Local expertise! There must be at least one person in your circle of friends or local communities that are deeply interested in ML and are willing to walk you through the jungle.