# Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

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Hi! My top 2 recommendations are:

Book: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291 (one of the best books on ML, you'll leave having a strong grasp on most fundamental ML concepts)

Course: https://course18.fast.ai/ml (the lesser known cousin of the famous Fast.ai deep learning course)

Good luck!

If you want to work on personal projects asap Hands on Machine Learning has to be the best. If you want to learn the math as well, then Pattern Recognition and Machine learning by Bishop will be much better. If you possibly want a career in ML I'd recommend the latter. If you want to do some personal projects go with the hands on book.

My previous video on AI did go into a lot more depth on neural networks but still wasn't enough to build your own. (There's only 2 vids on my channel so far so it's just the other one). I will defiantly consider making a video for my fellow devs on AI but for now you should check out https://www.youtube.com/watch?v=32wtJZ3yRfw&list=PLX2vGYjWbI0R08eWQkO7nQkGiicHAX7IX if you want to learn how to implent AI into a Unity3D project. If Unity3D isn't for you then you can read:

https://www.amazon.co.uk/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291/ref=sr_1_3?keywords=machine+learning+hands+on&qid=1563498470&s=gateway&sr=8-3

Also thanks for the support!

Data Analyst may or may not use sophisticated statistical methods. If they do a lot they are probably underemployed. Most Data Analysts use SQL, Python or R, and various business intelligence software (Tableau) to do data manipulation and summarization. Statisticians are typically designing studies, analyzing the data, summarizing and presenting results. They will have a much deeper understanding of theory.

SQL is important but R or Python is much more. Your data may be in files, relational databases, or even NoSQL databases so being able to pick those things up as needed is more important than one specific language.

Starting out in a business intelligence position or a data analyst position will be easiest and would give you good experience but could get boring quickly. With a masters you would technically qualify for a statistician or even data scientist position which is what you’re going to eventually want. As long as you feel confident about the job qualifications go for those instead.

4 and 5. This really depends on the industry or domain, but good free books are: ISLR , Forecasting: Principles and Practice . The first is more general. I would focus there first. An inexpensive excellent python book: Hands on machine learning with sci-kit learn and tensorflow

Depends on job, but I would say stats is more important as long as you can pick up programming as needed. I was given some very good advice that knowing how to analyze the data is more important than what technology you know.

Classification or Regression. Kaggle has lots of competitions and data.

There are jobs but lots of unqualified applicants due to hype. I would say there is lots of opportunities for qualified individuals.

First of all - machine learning is a huge field, and just doing a tutorial probably won't get you all that much. Best to try a project, or better yet find a job where you can start doing some light ML, so that you get practice with using it "in the wild".

That said, I've really found this book to be good, especially if you want to get into TensorFlow & deep learning.

"Hands On Machine Learning in Scikit-Learn and Tensorflow" : (https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291) has some examples of production pipelines among the code, and generally presents good form usage of common production APIs. Highly recommended book to work as it transitions nicely from core ML and data science into modern neural architectures and even explains utilizing multiple GPUs as well as Tensorflow's distribution package for multi-node jobs.

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That book (and books like it) as well as a general focus on scale (modern ML is data insatiable - check out horizontal vs. vertical scaling of ML as well as hardware-smart software solutions like trade-offs between 32 bit and 16 bit floating point operations in GPU-resident NNs) and finally reading up on current ML research from papers is a good route to be well equipped for production applications and field innovation. Diversifying and learning multiple learning frameworks in a working capacity (Pytorch, Tensorflow 1.0/2.0, Keras, etc) gives you a broader view of how machine learning is applied. A really great paper to get into scalable ML would be something like "Scale out Acceleration of Machine Learning" : (https://www.cc.gatech.edu/~hadi/doc/paper/2017-micro-cosmic.pdf) which utilizes hardware accelerators to beat many production ML streaming platforms commonly utilized for big data (i.e., Flink, Storm) or "FireCaffe" : (https://arxiv.org/abs/1511.00175) which is an extremely well done overview of the challenges involved with distributing machine learning from an implementation point of view. Perspective of this would be helpful in understanding the underlying mechanics involved in utilizing multiple GPUs/machines to process a single NN in an efficient manner.

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Disclaimer, I am a researcher myself and not into production, but I work with highly scalable streaming/ML applications. Though this isn't production work, it's generally in the direction of advancing the field of scalable production applications as a whole. Realtime streaming ML is the future.

The most used is https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291

https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-T...

I'm a beginner too, so I can't give you end-to-end solution. I'll try to describe my path.

I have no idea what I'll do next. Maybe, I'll took several courses and nanodegrees on Coursera. Maybe I'll find guidance and start getting hands on experience on a real project. It's not so hard to start learning - it's hard to find purpose and application of your knowledge.

I'm finding this one useful for the ML class:

[https://toptalkedbooks.com/amzn/1491962291)

This book is a pretty good reference on scikit-learn with very good walkthroughs and code samples:

https://toptalkedbooks.com/amzn/1491962291

[1]: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-T...

https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-T...

always"no".If no, what are the great resources for starters?The videos / slides / assignments from here:

https://www.amazon.com/Artificial-Intelligence-Modern-Approa...

This book:

https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-T...

This book:

https://www.amazon.com/Introduction-Machine-Learning-Python-...

These books:

https://www.amazon.com/Machine-Learning-Hackers-Studies-Algo...

This book:

https://www.amazon.com/Thoughtful-Machine-Learning-Test-Driv...

These subreddits:

http://artificial.reddit.com

http://machinelearning.reddit.com

http://semanticweb.reddit.com

These journals:

http://www.jmlr.org

http://www.jair.org

This site:

http://arxiv.org/corr/home/

Any tips before I get this journey going?Depending on your maths background, you may need to refresh some math skills, or learn some new ones. The basic maths you need includes calculus (including multi-variable calc / partial derivatives), probability / statistics, and linear algebra. For a much deeper discussion of this topic, see this recent HN thread:

https://news.ycombinator.com/item?id=15116379

Luckily there are tons of free resources available online for learning various maths topics. Khan Academy isn't a bad place to start if you need that. There are also tons of good videos on Youtube from Gilbert Strang, Professor Leonard, 3blue1brown, etc.

Also, check out Kaggle.com. Doing Kaggle contests can be a good way to get your feet wet.

And the various Wikipedia pages on AI/ML topics can be pretty useful as well.

# Elements of Programming

https://www.amazon.com/Elements-Programming-Alexander-Stepan...

This book proposes how to write C++-ish code in a mathematical way that makes all your code terse. In this talk, Sean Parent, at that time working on Adobe Photoshop, estimated that the PS codebase could be reduced from 3,000,000 LOC to 30,000 LOC (=100x!!) if they followed ideas from the book https://www.amazon.com/Grammar-Graphics-Statistics-Computing...

This book changed my perception of creativity, aesthetics and mathematics and their relationships. Fundamentally, the book provides all the diverse tools to give you confidence that your graphics are mathematically sound and visually pleasing. After reading this, Tufte just doesn't cut it anymore. It's such a weird book because it talks about topics as disparate Bayesian rule, OOP, color theory, SQL, chaotic models of time (lolwut), style-sheet language design and a bjillion other topics but always somehow all of these are very relevant. It's like if Bret Victor was a book, a tour de force of polymathical insanity.

The book is in full color and it has some of the nicest looking and most instructive graphics I've ever seen even for things that I understand, such as Central Limit Theorem. It makes sense the the best graphics would be in the book written by the guy who wrote a book on how to do visualizations mathematically. The book is also interesting if you are doing any sort of UI interfaces, because UI interfaces are definitely just a subset of graphical visualizations.

# Scala for Machine Learning

https://www.amazon.com/Scala-Machine-Learning-Patrick-Nicola...

This book almost never gets mentioned but it's a superb intro to machine learning if you dig types, scalable back-ends or JVM.

It’s the only ML book that I’ve seen that contains the word monad so if you sometimes get a hankering for some monading (esp. in the context of ML pipelines), look no further.

Discusses setup of actual large scale ML pipelines using modern concurrency primitives such as actors using the Akka framework.

# Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques for Building Intelligent Systems

https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-T...

Not released yet but I've been reading the drafts and it's a nice intro to machine learning using modern ML frameworks, TensorFlow and Scikit-Learn.

# Basic Category Theory for Computer Scientists

https://www.amazon.com/Markov-Logic-Interface-Artificial-Int...

Have you ever wondered what's the relationship between machine learning and logic? If so look no further.

# Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)

https://www.amazon.com/Designing-Scalability-Erlang-OTP-Faul...

Even though this is an Erlang book (I don't really know Erlang), 1/3 of the book is devoted to designing scalable and robust distributed systems in a general setting which I found the book worth it on it's own.

# Practical Foundations for Programming Languages

https://www.amazon.com/First-Course-Network-Theory/dp/019872...

Up until recently I didn't know the difference between graphs and networks. But look at me now, I still don't but at least I have a book on it.