Get started with machine learning using Python

Get started with machine learning using #Python:

  • Also, getting started with Python and machine learning is easy as there are plenty of online resources and lots of Python machine learning libraries available.
  • With basic Python programming skills under your belt, you’re ready to pick up basic machine learning skills.
  • Other online training worth checking out include:

    After getting a good feel for Python and machine learning, consider learning the open source Python libraries.

  • A few Python libraries to check out include:

    With an understanding of basic Python, machine learning skills, and Python libraries, you are all set.

  • Machine learning with Python is a great addition to your technical skillset, and there are lots of free and low-cost online resources available to help.

Machine learning is an in-demand skill to add to your resume. We walk through steps for wading into machine learning with the help of Python.
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Artificial Intelligence must be for all

#AI must be for all, including developing countries. Our CEO @ollybuston's latest blog:

  • Around the world individual researchers and small teams are working on AI solutions to deliver services where currently they are absent or inadequate.
  • We’ve all read the stories about machines taking over our jobs, and the World Bank says that jobs in developing countries are even more at risk of automation.
  • As algorithms become cheaper than people, manufacturing and low-skilled service jobs that are carried out by workers in low and middle income countries may be ‘reshored’ and replaced by work done by machines in rich countries.
  • The answer to the question “Will AI be more of an opportunity or more of a risk for developing countries?”
  • It is time to start laying the foundations for a truly global approach to AI development that maximises the opportunities and minimises the risks for everyone.

The list of tech failures in development is long.  Whether it be drone
pilots getting in the way of emergency relief in Nepal, or computers
gathering dust in Indian schools because teachers don’t know how to use
them, the outcome is rarely positive when techies fall in love with their
preferred solutions rather than taking  time to understand real problems
faced by real people.
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The Present and Future of Quantum Computing for AI – Towards Data Science – Medium

The Present and Future of #QuantumComputing for #AI

  • For AI researchers optimization and sampling is particularly important, because it allows to train Machine Learning models much faster with higher accuracy.At the present time, Canadian D-Wave is the leading company in quantum computing.
  • Task description is encoded as the energy function in connections between qubits, and through annealing they are moving towards some optimal configuration.If the transition is carried out slowly enough the algorithm will find a ground state (i.e., an optimal solution) with high probability:During the annealing process, probability of qubits ending up in the minimum energy state increasesQuantum Coupling allows qubits to explore all potential solutions simultaneously, and at the same time Quantum Tunneling allows them to move through high energy barriers towards the “better” states.
  • This video by D-Wave explains QA in more details:IBM QAnother major player is IBM Q. Big Blue is working with Gate-model quantum computing and their machines are Universal Quantum Computers.
  • State-of-the-art processors from IBM have 16 and 17 qubits, and it’s really hard to scale further.More general architecture of IBM’s processors allows them to run any quantum algorithms.
  • Only theoretical research and simulations on toy problems.Overall, quantum computing looks like a promising direction for stochastic models in Machine Learning.

Quantum computing is still in it’s infancy, and no universal architecture for quantum computers exists right now. However, their prototypes are already here and showing promising results in…
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A #Java Toolbox for Scalable Probabilistic #MachineLearning

  • The AMIDST Toolbox allows you to model your problem using a flexible probabilistic language based on graphical models.
  • AMIDST Toolbox has been used to track concept drift and do risk prediction in credit operations, and as data is collected continuously and reported on a daily basis, this gives rise to a streaming data classification problem.
  • As an example, the following figure shows how the data processing capacity of our toolbox increases given the number of CPU cores when learning an a probabilistic model (including a class variable C, two latent variables (dashed nodes), multinomial (blue nodes) and Gaussian (green nodes) observable variables) using the AMIDST’s learning engine.
  • As can be seen, using our variational learning engine, AMIDST toolbox is able to process data in the order of gigabytes (GB) per hour depending on the number of available CPU cores with large and complex PGMs with latent variables.
  • If your data is really big and can not be stored in a single laptop, you can also learn your probabilistic model on it by using the AMIDST distributed learning engine based on a novel and state-of-the-art distributed message passing scheme implemented on top of Apache Flink.

toolbox – A Java Toolbox for Scalable Probabilistic Machine Learning
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Book: Machine Learning Algorithms From Scratch

Book: #MachineLearning Algorithms From Scratch

  • From First Principles With Pure Python and

    Use them on Real-World Datasets

    You must understand algorithms to get good at machine learning.

  • In this mega Ebook written in the friendly Machine Learning Mastery style that you’re used to, finally cut through the math and learn exactly how machine learning algorithms work.
  • I’ve written books on algorithms, won and ranked in the top 10% in machine learning competitions, consulted for startups and spent a long time working on systems for forecasting tropical cyclones.
  • (yes I have written tons of code that runs operationally)

    I get a lot of satisfaction helping developers get started and get really good at machine learning.

  • I teach an unconventional top-down and results-first approach to machine learning where we start by working through tutorials and problems, then later wade into theory as we need it.

Discover How to Code Machine Algorithms
From First Principles With Pure Python and
Use them on Real-World Datasets

$37 USD
You must understand algorithms t…
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Free Machine Learning eBooks

Free #MachineLearning eBooks - March 2017 #abdsc

  • Machine learning is one of the fastest growing areas of computer science, with far-reaching applications.
  • The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way.
  • The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms.
  • These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds.
  • Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.

Here are three eBooks available for free.
Edited by Abdelhamid Mellouk and Abdennacer Chebira
Machine Learning can be defined in various ways…
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Google’s plan to best Amazon rests on one particular piece of software

#Google's plan to best #Amazon rests on one piece of software  #aws #cloud #AI

  • Two years later the tool, which is used in building machine-­learning software, underpins many future ambitions of Google and its parent company, Alphabet.
  • But just months after TensorFlow was released to Google’s army of coders, the company also began offering it to the world for free, as an open source tactic.
  • S. Somasegar, a managing director at venture fund Madrona who was previously head of Microsoft’s developer division, says TensorFlow’s prominence poses a genuine challenge to Google’s cloud rivals.
  • The company has created specialized processors to make TensorFlow faster and reduce the power it consumes inside Google’s data centers.
  • Since Google released TensorFlow, its competitors in cloud computing, Microsoft and Amazon, have released, or started supporting, their own free software tools to help coders build machine-learning systems.

Google has pinned its cloud computing hopes on a bit of software that helps programmers build artificial intelligence apps called TensorFlow.
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