Reddit mentions of Python Machine Learning - Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow

Took " Intro to Data Science with Python" and didn't finish the course.

I remembered it being dry and slow and just not structured in a way that made me care for the material. Maybe it works best for someone who has no prior exposure.

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I ended up using the book Python Machine Learning

Good decision!

There’s no one “right” path. A lot depends on your time and financial resources. I started my career as an actuary, but spent a lot of my downtime learning programming, and now I’m a software engineer with some experience in machine learning. I learned most of what I know through work experience and online resources. I’ll share what has helped me:

There are several free or cheap courses online to help you get started.

Udacity offers a free introductory video-based course. It recommends a basic understanding of linear algebra and statistics as prerequisites.

If you prefer a textbook, Python Machine Learning was very helpful to me. Getting started, IMO, I don’t think it’s necessarily important to understand all of the math that’s included in the book, but it certainly doesn’t hurt.

Kaggle is a great resource. You can practice your skills on real data sets that you find interesting. There are some tutorials included, and you can see how more experienced engineers approach problems.

Try to develop an understanding of fundamental statistical principles. Understanding basic linear regression is a good starting point. I have a degree in statistics, so I had a bit of a head start here. Udacity also has intro stats courses that are free.

If you have the time, money, and desire, look into a university education in math, statistics, or machine learning.

It’s great that you enjoy programming, because you will definitely spend a lot of your time doing just that. I highly recommend learning Python — it’s extremely popular for machine learning/data science.

If you do decide to learn Python, check out sklearn, pandas, and numpy. These libraries are extremely useful for introductory machine learning. Sklearn has built-in support for lots of algorithms. Pandas and numpy are extremely useful for data wrangling/cleaning (which is a critical, if mundane, component of machine learning).

Deep learning (neural networks) is a subset of machine learning and is quite the rage nowadays. Karas is a simple-ish, high level Python wrapper around TensorFlow, which is a lower-level API for deep learning. This is definitely useful, but I would recommend learning more basic machine learning techniques and technologies before you get started with deep learning.

Spark is also a very useful technology for Big Data, but that may be beyond the scope of getting started in machine learning.

I’d be happy to think of some more resources if you’re interested.

My understanding is that this is more of a traditional statistics problem than a data science problem (though the two overlap substantially). It sounds like you will be dealing with (at most) a few hundred “squads” and maybe 10-20 pertaining variables (e.g., workload, time taken to complete work load, size of team, age of team members, educational qualifications of team members). There is no strict boarder segregating data science and traditional statistics, but generally speaking traditional statistics aim to analyse 100s-1000s of data points with 10s of variables, whereas data science or machine learning procedures aim to assess millions of data points with 1000s of variables available. Again, this is not a strict definition and you can almost always apply a data science procedure to a traditional statistics problem (and visa-versa).
This being said, this sub is an okay place to seek resources. I would highly recommend checking out Stack Exchange and the machine learning sub. You may want to purchase a textbook to facilitate your learning. My favourites include Applied Linear Statistical Models by Kutner and friends, and Python Machine Learning by Sebastian Raschika. The former is a traditional stats textbook and the latter is a data science/machine learning textbook. You may be able to find a good portion of these books on Google Books for free. This might help you decide which one to buy and what direction to go in. Additionally, I have made some videos on particular data analysis procedures with the aim of facilitating application oriented understanding rather than complete mathematical understanding, you may find some of these videos useful.

I would say that your proposed project is potentially a good one, but we’d need more information to gauge its feasibility. As a start, see what variables you have available to you and explore your data a bit (maybe look at all their bivariate relationships). Data analysis itself requires a lot of “looking at” and “interpreting what you see”. Just doing this basic task will give you a much better idea of what you are dealing with, what variables are possibly related to your outcome of interest, and how feasible it is to gain insight into the problem of interest. Explore and get to know your data, and if you are stuck after that then definitely come back and ask more questions. In the end, you may not be able to accurately “predict” anything, but you can definitely calculate probabilities of successfully completing a sprint based on workload + conditions.

Best of luck, sounds like a fun project :)

Hi!

I am also studying ML/AI ( who isn't these days).

I have already taken some math courses ( I am a math teacher) , but this was like 10 years ago.

If not I think I would have started with more math.

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I have based my own curriculum around these three:

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• MK: (more course oriented)
  
  • https://github.com/maykulkarni/Machine-Learning-Curriculum
    
• KR: (more book oriented)
  
  • http://karlrosaen.com/ml/
    
• OSSU: (Computer Science)
  
  • https://github.com/ossu/computer-science/blob/dev/README.md#advanced-cs
    
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    Courses/books that I have done or started:
    
    
• Andrew Ng - Machine Learning (done).
  
  • https://www.coursera.org/learn/machine-learning/home/welcome
    
• Course 2 Machine Learning A-Z (done)
  
  • https://www.udemy.com/machinelearning/
    
• Sebastian Raschka:Python Machine Learning: (haven't finished it yet)
  
  • https://www.amazon.com/Python-Machine-Learning-scikit-learn-TensorFlow/dp/1787125939/ref=dp_ob_title_bk
    
• Microsoft Artificial Intelligence Professional Program ( I have done course 5,6,7 and 8)
  
  • https://www.edx.org/microsoft-professional-program-artificial-intelligence
    
• MITx's Statistics and Data Science MicroMasters® Program ( Im half way through Probability)
  
  • https://www.edx.org/micromasters/mitx-statistics-and-data-science?utm_medium=partner-marketing&utm_source=mitnews&utm_campaign=mitx&utm_content=mm-ds-fl18-mit-news
    
    ​
    
    Doing right now:
    
    Continue with MITx's Statistics and Data Science MicroMasters. I don't think I will take the Data Analysis course in it though.
    
    Im starting a deep learning course today. Probably Deep Learning A-Z™: Hands-On Artificial Neural Networks. I feel that I need to do at least 1 course in deep learning and Reinforcement learning before finishing the Microsoft AI program.
    
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    Can recommend all of the courses/books above. They complement each other quite well. I have also done the Getting started on kaggle.com, which was fun.
    
    ​
    
    If you have any suggestions for further reading/study please let me know!Besides following my on curriculum Im also studying a three year CS-program (starting my second year soon).

>Suggested Book
>
>Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition

Did you seriously recommend that Packt book? I actually have a printed version right in front of me and I will be honest - this is probably the worst position on machine learning I have tried reading in a while. I mean - very first chapter and this book already tries to talk about clustering, dimensionality reduction, regression vs classification and moves telling you that you install Python packages with pip. Nothing like overloading a reader with shitloads of information upfront.

Then the chapter 2 arrives and you INSTANTLY get thrown not into linear regression (what most courses do)... but straight into neural networks with Heaviside step function as an activation (and that's page 1 of chapter 2, 5 pages later you will be working with an ADALINE and jump right into gradients).

I am sorry but I don't understand at ALL who this book is oriented for. I can guarantee, it's not for people that are new to machine learning (nothing like your first programming assignment being a perceptron). It's also not for people that already understand machine learning.

We have a lot of good resources regarding machine learning nowadays. In particular a free https://www.coursera.org/learn/machine-learning which not only is taught by a Stanford professor (and is very similar to what they actually teach at that university) but it also comes with proper pacing and properly introduces concepts in a right order.

So I am really wondering - have you actually read that book you are recommending? Especially since you also talk about "Tensor Flow" over and over (it's actually named TensorFlow, no spacebar) and the resource you are recommending doesn't even mention it (focusing on Theano instead).

Also:

>Their is also the Tensor Flow framework which exposes the ability to do more accurate based Machine Learning tasks using advanced Convolutional Neural Networks and parallel computing provided by the many computing processing units of a typical gaming graphics card

This sentence stinks. I can tell English is not your native language but this is a nightmare to read through. It's also not even correct (if you want you can write CNNs in Microsoft Excel and it will be JUST as accurate, Tensorflow just makes it simpler).