Scikit-Learn Cheat Sheet: Python Machine Learning (Article)

Scikit-learn cheat sheet: #machinelearning with #Python -

  • Most of you who are learning data science with Python will have definitely heard already about , the open source Python library that implements a wide variety of machine learning, preprocessing, cross-validation and visualization algorithms with the help of a unified interface.
  • If you’re still quite new to the field, you should be aware that machine learning, and thus also this Python library, belong to the must-knows for every aspiring data scientist.
  • This  cheat sheet will introduce you to the basic steps that you need to go through to implement machine learning algorithms successfully: you’ll see how to load in your data, how to preprocess it, how to create your own model to which you can fit your data and predict target labels, how to validate your model and how to tune it further to improve its performance.
  • Scikit-learn is an open source Python library that implements a range of machine learning, preprocessing, cross-validation and visualization algorithms using a unified interface.
  • >>> from sklearn import neighbors, datasets, preprocessing >>> from sklearn.model_selection import train_test_split >>> from sklearn.metrics import accuracy_score >>> iris = datasets.load_iris() >>> X, y = iris.data[:, :2], iris.target >>> X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33) >>> scaler = >>> X_train = scaler.transform(X_train) >>> X_test = scaler.transform(X_test) >>> knn = >>> knn.fit(X_train, y_train) >>> y_pred = knn.predict(X_test) >>> accuracy_score(y_test, y_pred) Your data needs to be numeric and stored as NumPy arrays or SciPy sparse matrices.

A handy scikit-learn cheat sheet to machine learning with Python, including code examples.
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IBM Bets The Company On Cloud, AI, And Blockchain

IBM Bets The Company On #Cloud, #AI, And #Blockchain by @theebizwizard   #IBMInterConnect

  • IBM has dubbed its AI offering Watson – a powerful cognitive computing engine that pervades everything IBM is bringing to customers.
  • David Kenny, Senior Vice President of IBM Watson and Cloud Platform, laid out IBM’s strategic ONE Architecture consisting of four layers: Cloud, Data AI, and Applications, as the image below shows.
  • The ONE Architecture is remarkable in two ways: first, AI (in the form of Watson) represents an entire layer, indicating the breadth of IBM’s bet on the technology.
  • What, then, does Watson truly bring to customers of the IBM Cloud?
  • This enormous commitment of person-hours from highly qualified professionals as well as vast quantities of data makes Watson more of a consultant’s tool, best suited for selling the time of IBM consultants, more so than a modular, LEGO-block style plug-in for customers to incorporate directly into their own applications.

It’s clear that Rometty has taken the lesson of the Innovator’s Dilemma to heart, and is willing to jeopardize existing revenue streams in order to place large bets on innovation. The big picture here, of course, is business transformation – most notably for IBM, but also for its customers.
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Artificial Intelligence Will Not Replace Lawyers With IQ And EQ

#ArtificialIntelligence Will Not Replace Lawyers With IQ And EQ

  • Many lawyers have elevated IQ’s, though relatively few seem to possess high EQ’s– commonly called ‘people skills’.
  • Artificial intelligence (AI), a recent entrant in the legal vertical, scores high on IQ, but the jury is still out on whether machines can develop comparable EQ.
  • How is human intelligence—IQ and EQ– applied to legal practice, and what functions require specialized training and social skills that cannot be performed by machines?
  • Put another way, what are the core functions that lawyers perform, and what attributes differentiate effective human lawyers from machine ones?
  • And while some of the rote chores that support the work they do—legal research, discovery production review, statistical analysis—can certainly be performed by machines—only human lawyers can synthesize it and communicate it to others in a way that evokes confidence (“I’m sure glad she’s my lawyer!)

Analytical prowess (IQ), people skills (EQ) and artificial intelligence (AI) are three kinds of intelligence in the legal marketplace. Who is best suited for what tasks is largely driven by which resource–human or machine has the necessary form of intelligence. This article examines the three.
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Google’s new machine learning API recognizes objects in videos

Google’s new machine learning API recognizes objects in videos

  • At its Cloud Next conference in San Francisco, Google today announced the launch of a new machine learning API for automatically recognizing objects in videos and making them searchable.
  • The new Video Intelligence API will allow developers to build applications that can automatically extract entities from a video.
  • Until now, most similar image recognition APIs available in the cloud only focused on doing this for still images, but with the help of this new API, developers will be able to build applications that let users search and discover information in videos.
  • Besides extracting metadata, the API allows you to tag scene changes in a video.

Google’s new machine learning API recognizes objects in videos
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Scikit-Learn Cheat Sheet: Python Machine Learning

Scikit-learn cheat sheet: #machinelearning with #Python -

  • Most of you who are learning data science with Python will have definitely heard already about , the open source Python library that implements a wide variety of machine learning, preprocessing, cross-validation and visualization algorithms with the help of a unified interface.
  • If you’re still quite new to the field, you should be aware that machine learning, and thus also this Python library, belong to the must-knows for every aspiring data scientist.
  • This  cheat sheet will introduce you to the basic steps that you need to go through to implement machine learning algorithms successfully: you’ll see how to load in your data, how to preprocess it, how to create your own model to which you can fit your data and predict target labels, how to validate your model and how to tune it further to improve its performance.
  • In short, this cheat sheet will kickstart your data science projects: with the help of code examples, you’ll have created, validated and tuned your machine learning models in no time.
  • In addition, you’ll make use of Python’s data visualization library  to visualize your results.

A handy scikit-learn cheat sheet to machine learning with Python, including code examples.
Continue reading “Scikit-Learn Cheat Sheet: Python Machine Learning”

Scikit-Learn Cheat Sheet: Python Machine Learning

Cheat sheet: #machinelearning in #Python with scikit-learn -

  • The cheat sheet will kickstart your data science projects: with the help of code examples, you’ll have created, validated and tuned your machine learning models in no time.
  • Begin with our scikit-learn tutorial for beginners , in which you’ll learn in an easy, step-by-step way how to explore handwritten digits data, how to create a model for it, how to fit your data to your model and how to predict target values.
  • If you still have no idea about how scikit-learn works, this machine learning cheat sheet might come in handy to get a quick first idea of the basics that you need to know to get started.
  • The scikit-learn cheat sheet will introduce you to the basic steps that you need to go through to implement machine learning algorithms successfully: you’ll see how to load in your data, how to preprocess it, how to create your own model to which you can fit your data and predict target labels, how to validate your model and how to tune it further to improve its performance.
  • If you’re still quite new to the field, you should be aware that machine learning, and also this Python library, belong to the must-knows for every aspiring data scientist.

A handy scikit-learn cheat sheet to machine learning with Python, including code examples.
Continue reading “Scikit-Learn Cheat Sheet: Python Machine Learning”

Snapchat Is Beginning to Use Machine Learning to Improve Ad Targeting

Snapchat is beginning to use machine learning to improve ad targeting:

  • Snap’s expected IPO coming sometime next year will likely keep a fire lit under Snap to further innovate.
  • While GBB campaigns are still charged on a CPM basis, Snap says the campaigns end up being more effective.
  • Snap has also been making a big push to improve measurement.
  • Snapchat is adding ways to optimize campaign performance with the help of machine learning.
  • According to CBInsights , Snap Inc. acquired four patents in 2016, and while it’s nowhere near the 16 it received in 2015 or the 18 it received in 2014, it’ll likely need to keep innovating next year.

Snapchat is adding ways to optimize campaign performance with the help of machine learning.  Earlier this month, Snap Inc. began rolling out what it calls Goal-Based Bidding (GBB). The option, available to marketers buying ads through Snapchat’s API, uses machine learning to know which users are most likely to swipe a certain type of ad.
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