From my other comment elsewhere. These resources helped me understand the topics better.

If anyone wants to understand fundamentals of machine learning, one of the superb resources I have found is, Stanford's "Probability for computer scientists"[1].

It goes into theoretical underpinnings of probability theory and ML, IMO better than any other course I have seen. But, this is a primarily a probability course that discusses the fundamentals of machine learning. (Yeah, Andrew Ng is legendary, but his course demands some mathematical familiarity with linear algebra topics)

There is a course reader for CS109 [2]. You can download pdf version of this. Caltech's learning from data was really good too, if someone is looking for theoretical understanding of ML topics [3].

There is also book for excellent caltech course[4].

Also, neural networks zero to hero is for understanding how neural networks are built from ground up [5].

[1] https://www.youtube.com/watch?v=2MuDZIAzBMY&list=PLoROMvodv4...

[2] https://chrispiech.github.io/probabilityForComputerScientist...

[3] https://work.caltech.edu/telecourse

[4] https://www.amazon.com/Learning-Data-Yaser-S-Abu-Mostafa/dp/...

[5] https://www.youtube.com/watch?v=VMj-3S1tku0&list=PLAqhIrjkxb...

That's because they posted them somewhere else (easy mistake to make.. HN doesn't show you the full link in a comment, so copy/paste just copies the ellipsis)

https://chrispiech.github.io/probabilityForComputerScientist...

https://www.amazon.com/Learning-Data-Yaser-S-Abu-Mostafa/dp/...

There is a course reader for CS109 [1]. You can download pdf version of this.

There is also book[2] for excellent caltech course[3].

[1] https://chrispiech.github.io/probabilityForComputerScientist...

[2] https://www.amazon.com/Learning-Data-Yaser-S-Abu-Mostafa/dp/...

[3] https://work.caltech.edu/telecourse

Possibly "Learning from Data"[1][2] would be of interest?

[1]: https://www.amazon.com/Learning-Data-Yaser-S-Abu-Mostafa/dp/...

[2]: https://amlbook.com/

This is off the beaten path, but consider Abu-Mostafa et al.'s "Learning from Data". https://www.amazon.com/Learning-Data-Yaser-S-Abu-Mostafa/dp/...

I adore PRML, but the scope and depth is overwhelming. LfD encapsulates a number of really core principles in a simple text. The companion course is outstanding and available on EdX.

The tradeoff is that LfD doesn't cover a lot of breath in terms of looking at specific algorithms, but your other texts will do a better job there.

My second recommendation is to read the documentation for Scikit.Learn. It's amazingly instructive and a practical guide to doing ML in practice.

I really enjoyed the books "Learning From Data"[1], and "Programming Collective Intelligence"[2].

Both are accessible to beginners.

Learning From Data gives a more theoretical introduction to machine learning. One of the central ideas from the book that I still think about often is that machine learning is merely function approximation. There exists a function which will drive a car perfectly, but we don't know what that function is, so we try to approximate that function with machine learning.

Programming Collective Intelligence is a more hands-on introduction to machine learning. The book has examples in Python, but I believe the Python code is low quality. Ignoring the example code (and I did ignore it), the book is a very enjoyable introduction to many different machine learning algorithms. If you don't know the difference between linear regression, nearest-neighbors clustering, support vector machines, and a neural networks, this book will explain how each of these work and give a good intuition about when to use each.

[1] http://www.amazon.com/gp/product/1600490069 [2] http://www.amazon.com/Programming-Collective-Intelligence-Bu...

Prof Yaser S. Abu-Mostafa's Caltech course "Learning from Data" (http://work.caltech.edu/telecourse) is probably the best introductory course for really understanding the physics of how machine learning works.

See Prof's Yaser's 1 min overview: http://www.youtube.com/watch?v=KlP0DpiM7Lw

The "Learning from Data Book" videos are online for free, and the book is on Amazon...

Videos: http://home.caltech.edu/lectures.html

Book: http://www.amazon.com/Learning-From-Data-Yaser-Abu-Mostafa/d...

The course is also availble on EdX: https://www.edx.org/course/caltechx/cs1156x/learning-data/11...

For a glimpse into machine learning, check out Professor Yaser Abu-Mostafa's "Learning From Data" course from Caltech. The videos are online for free (http://work.caltech.edu/telecourse.html, https://www.edx.org/course/caltechx/cs1156x/learning-data/11...), and its corresponding book is on Amazon (http://www.amazon.com/Learning-From-Data-Yaser-Abu-Mostafa/d...).

Also Professor Ng's course from Stanford (http://cs.stanford.edu/people/ang/?page_id=22).

Don't worry about needing to catch up. Stuff is moving so fast these days, you're always working with something new. Everyone is in a continual update mode so it's not like you have 10 years of catching up to do. Tech has turned over a 10 times since then. You could say 10 years and 2 years are functionally equivalent from a new tech point of view.

And don't worry about corps and recruiters. Focus on a problem you want to solve, and update your skills in the context of learning what you need to know to solve that problem. If you can leverage your industry experience in the problem domain, even better.

Data is driving everything so developing a data analysis/machine learning skillset will put you into any industry you want. Professor Yaser Abu-Mostafa's "Learning From Data" is a gem of a course that helps you see the physics underpinning the learning (metaphorically of course -- ML is mostly vectors, matrices, linear algebra and such). The course videos are online for free (http://work.caltech.edu/telecourse.html), and you can get the corresponding book on Amazon -- it's short (http://www.amazon.com/Learning-From-Data-Yaser-Abu-Mostafa/d...).

Python is a good general purpose language for getting back in the groove. It's used for everything, from server-side scripting to Web dev to machine learning, and everywhere in between. "Coding the Matrix" (https://www.coursera.org/course/matrix, http://codingthematrix.com/) is an online course by Prof Philip Klein that teaches you linear algebra in Python so it pairs well with "Learning from Data".

Clojure (http://clojure.org/) and Go (http://golang.org/) are two emerging languages. Both are elegantly designed with good concurrency models (concurrency is becoming increasingly important in the multicore world). Rich Hickey is the author Clojure -- watch his talks to understand the philosophy behind the design (http://www.infoq.com/author/Rich-Hickey). "Simple Made Easy" (http://www.infoq.com/presentations/Simple-Made-Easy) is one of those talks everyone should see. It will change the way you think.

Knowing your way around a cloud platform is essential these days. Amazon Web Services (AWS) has ruled the space for some time, but last year Google opened its gates (https://cloud.google.com/). Its high-performance cloud platform is based on Google search, and learning how to rev its engines will be a valuable thing. Relative few have had time to explore its depths so it's a platform you could jump from.

Hadoop MapReduce (https://hadoop.apache.org/, http://www.cloudera.com, http://hortonworks.com/) has been the dominant data processing framework the last few years, and Hadoop has become almost synonymous with the term "Big Data". Hadoop is like the Big Data operating system, and true to its name, Hadoop is big and bulky and slow. However, there is a new framework on the scene that's true to its name. Spark (http://spark.incubator.apache.org/) is small and nimble and fast. Spark is part of the Berkeley Data Analytics Stack (BDAS - https://amplab.cs.berkeley.edu/software/), and it will likely emerge as Hadoop's successor (see last week's thread -- https://news.ycombinator.com/item?id=6466222).

ElasticSearch (http://www.elasticsearch.org/) is a good to know. Paired with Kibana (http://www.elasticsearch.org/overview/kibana/) and LogStash (http://www.elasticsearch.org/overview/logstash/), it's morphed into a multipurpose analytics platform you can use in 100 different ways.

Databases abound. There's a bazillion new databases and new ones keep popping up for increasingly specialized use cases. Cassandra (https://cassandra.apache.org), Datomic (http://www.cognitect.com/), and Titan (http://thinkaurelius.github.io/titan/) to name a few (http://nosql-database.org/). Redis (http://redis.io/) is a Swiss Army knife you can apply anywhere, and it's simple to use -- you'll want it on your belt.

If you're doing Web work and front-end stuff, JavaScript is a must. AngularJS (http://angularjs.org/) and ClojureScript (https://github.com/clojure/clojurescript) are two of the most interersting developments.

Oh, and you'll need to know Git (http://git-scm.com, https://github.com). See Linus' talk at Google to get the gist (https://www.youtube.com/watch?v=4XpnKHJAok8 :-).

As you can see, the opportunities for learning emerging tech are overflowing, and what's cool is the ways you can apply it are boundless. Make something. Be creative. Follow your interests wherever they lead because you'll have no trouble catching the next wave from any path you choose.

To tag onto this, I found "Learning from Data" [0] by Abu Mastafa to be a great intro to the field. It's not heavy on the math, but it doesn't gloss over it either

[0]:http://www.amazon.com/Learning-From-Data-Yaser-Abu-Mostafa/d...