AI News, Machine Learning is dead – Long live machine learning!

Machine Learning is dead – Long live machine learning!

The open sourcing of entire ML frameworks marks the end of a phase of rapid development of tools, and thus marks the death of ML as we have known it so far.

What all this essentially means is that it is perfectly alright if one doesn’t know about the Sequential Minimal Optimization algorithm developed by John Platt in 1998 to solve the quadratic programming problem that occurs when you train a support vector machine (SVM).

Knowing that (1) SVM is just a supervised learning model that fits a linear hyperplane to classify high dimensional data (2) SVM is capable of transforming nonlinear data to a linear classifiable form using kernels, and (3) SVM allows for errors using the soft margin technique allows one to implement SVM from scratch.

However, we definitely need people who can come up with creative applications that will eventually enable ML and AI to become more mainstream in our lives, almost to a point where ML now becomes a part of human culture itself.

It just means that the go-to-market timelines for implementations of new tools and techniques from the time they get published in the academic community to the time the practicing ML community lays hands on it, have shortened tremendously.

How to choose algorithms for Azure Machine Learning Studio

The answer to the question "What machine learning algorithm should I use?"

It depends on how the math of the algorithm was translated into instructions for the computer you are using.

Even the most experienced data scientists can't tell which algorithm will perform best before trying them.

The Microsoft Azure Machine Learning Algorithm Cheat Sheet helps you choose the right machine learning algorithm for your predictive analytics solutions from the Azure Machine Learning Studio library of algorithms. This

This cheat sheet has a very specific audience in mind: a beginning data scientist with undergraduate-level machine learning, trying to choose an algorithm to start with in Azure Machine Learning Studio.

That means that it makes some generalizations and oversimplifications, but it points you in a safe direction.

As Azure Machine Learning grows to encompass a more complete set of available methods, we'll add them.

These recommendations are compiled feedback and tips from many data scientists and machine learning experts.

We didn't agree on everything, but I've tried to harmonize our opinions into a rough consensus.

data scientists I talked with said that the only sure way to find

Supervised learning algorithms make predictions based on a set of examples.

For instance, historical stock prices can be used to hazard guesses

company's financial data, the type of industry, the presence of disruptive

it uses that pattern to make predictions for unlabeled testing data—tomorrow's

Supervised learning is a popular and useful type of machine learning.

In unsupervised learning, data points have no labels associated with them.

grouping it into clusters or finding different ways of looking at complex

In reinforcement learning, the algorithm gets to choose an action in response

signal a short time later, indicating how good the decision was. Based

where the set of sensor readings at one point in time is a data

The number of minutes or hours necessary to train a model varies a great deal

time is limited it can drive the choice of algorithm, especially when

regression algorithms assume that data trends follow a straight line.

These assumptions aren't bad for some problems, but on others they bring

Non-linear class boundary - relying on a linear classification algorithm

Data with a nonlinear trend - using a linear regression method would generate

much larger errors than necessary Despite their dangers, linear algorithms are very popular as a first line

Parameters are the knobs a data scientist gets to turn when setting up an

as error tolerance or number of iterations, or options between variants

to make sure you've spanned the parameter space, the time required to

train a model increases exponentially with the number of parameters.

For certain types of data, the number of features can be very large compared

algorithms, making training time unfeasibly long.

Some learning algorithms make particular assumptions about the structure of

- shows excellent accuracy, fast training times, and the use of linearity ○

- shows good accuracy and moderate training times As mentioned previously, linear regression fits

curve instead of a straight line makes it a natural fit for dividing

logistic regression to two-class data with just one feature - the class

boundary is the point at which the logistic curve is just as close to both classes Decision forests (regression, two-class, and multiclass), decision

all based on decision trees, a foundational machine learning concept.

decision tree subdivides a feature space into regions of roughly uniform

values Because a feature space can be subdivided into arbitrarily small regions,

it's easy to imagine dividing it finely enough to have one data point

a large set of trees are constructed with special mathematical care

memory at the expense of a slightly longer training time.

Boosted decision trees avoid overfitting by limiting how many times they can

a variation of decision trees for the special case where you want to know

that input features are passed forward (never backward) through a sequence

a long time to train, particularly for large data sets with lots of features.

typical support vector machine class boundary maximizes the margin separating

Any new data points that fall far outside that boundary

PCA-based anomaly detection - the vast majority of the data falls into

data set is grouped into five clusters using K-means There is also an ensemble one-v-all multiclass classifier, which

19 Data Science and Machine Learning Tools for people who Don’t Know Programming

This article was originally published on 5 May, 2016 and updated with the latest tools on May 16, 2018.

Among other things, it is acknowledged that a person who understands programming logic, loops and functions has a higher chance of becoming a successful data scientist.

There are tools that typically obviate the programming aspect and provide user-friendly GUI (Graphical User Interface) so that anyone with minimal knowledge of algorithms can simply use them to build high quality machine learning models.

The tool is open-source for old version (below v6) but the latest versions come in a 14-day trial period and licensed after that.

RM covers the entire life-cycle of prediction modeling, starting from data preparation to model building and finally validation and deployment.

You just have to connect them in the right manner and a large variety of algorithms can be run without a single line of code.

There current product offerings include the following: RM is currently being used in various industries including automotive, banking, insurance, life Sciences, manufacturing, oil and gas, retail, telecommunication and utilities.

BigML provides a good GUI which takes the user through 6 steps as following: These processes will obviously iterate in different orders. The BigML platform provides nice visualizations of results and has algorithms for solving classification, regression, clustering, anomaly detection and association discovery problems.

Cloud AutoML is part of Google’s Machine Learning suite offerings that enables people with limited ML expertise to build high quality models. The first product, as part of the Cloud AutoML portfolio, is Cloud AutoML Vision.

This service makes it simpler to train image recognition models. It has a drag-and-drop interface that let’s the user upload images, train the model, and then deploy those models directly on Google Cloud.

It also provides visual guidance making it easy to bring together data, find and fix dirty or missing data, and share and re-use data projects across teams.

Also, for each column it automatically recommends some transformations which can be selected using a single click. Various transformations can be performed on the data using some pre-defined functions which can be called easily in the interface.

Trifacta platform uses the following steps of data preparation: Trifacta is primarily used in the financial, life sciences and telecommunication industries.

The core idea behind this is to provide an easy solution for applying machine learning to large scale problems.

All you have to do is using simple dropdowns select the files for train, test and mention the metric using which you want to track model performance.

Sit back and watch as the platform with an intuitive interface trains on your dataset to give excellent results at par with a good solution an experienced data scientist can come up with.

It also comes with built-in integration with the Amazon Web Services (AWS) platform. Amazon Lex is a fully managed service so as your user engagement increases, you don’t need to worry about provisioning hardware and managing infrastructure to improve your bot experience.

You can interactively discover, clean and transform your data, use familiar open source tools with Jupyter notebooks and RStudio, access the most popular libraries, train deep neural networks, among a a vast array of other things.

It can take in various kinds of data and uses natural language processing at it’s core to generate a detailed report.

But these are excellent tools to assist organizations that are looking to start out with machine learning or are looking for alternate options to add to their existing catalogue.

Machine Learning is dead – Long live machine learning!

The open sourcing of entire ML frameworks marks the end of a phase of rapid development of tools, and thus marks the death of ML as we have known it so far.

What all this essentially means is that it is perfectly alright if one doesn’t know about the Sequential Minimal Optimization algorithm developed by John Platt in 1998 to solve the quadratic programming problem that occurs when you train a support vector machine (SVM).

Knowing that (1) SVM is just a supervised learning model that fits a linear hyperplane to classify high dimensional data (2) SVM is capable of transforming nonlinear data to a linear classifiable form using kernels, and (3) SVM allows for errors using the soft margin technique allows one to implement SVM from scratch.

However, we definitely need people who can come up with creative applications that will eventually enable ML and AI to become more mainstream in our lives, almost to a point where ML now becomes a part of human culture itself.

It just means that the go-to-market timelines for implementations of new tools and techniques from the time they get published in the academic community to the time the practicing ML community lays hands on it, have shortened tremendously.

Your First Machine Learning Project in R Step-By-Step (tutorial and template for future projects)

Do you want to do machine learning using R, but you’re having trouble getting started?

In this step-by-step tutorial you will: If you are a machine learning beginner and looking to finally get started using R, this tutorial was designed for you.

The process of a machine learning project may not be linear, but there are a number of well-known steps: For more information on the steps in a machine learning project see this checklist and more on the process.

The best way to really come to terms with a new platform or tool is to work through a machine learning project end-to-end and cover the key steps.

Open your command line, change (or create) to your project directory and start R by typing: You should see something like the screenshot below either in a new window or in your terminal.

UPDATE: We may need other packages, but caret should ask us if we want to load them. If you are having problems with packages, you can install the caret packages and all packages that you might need by typing: Now, let’s load the package that we are going to use in this tutorial, the caret package.

The caret package provides a consistent interface into hundreds of machine learning algorithms and provides useful convenience methods for data visualization, data resampling, model tuning and model comparison, among other features.

is, we are going to hold back some data that the algorithms will not get to see and we will use this data to get a second and independent idea of how accurate the best model might actually be.

We will split the loaded dataset into two, 80% of which we will use to train our models and 20% that we will hold back as a validation dataset.

In this step we are going to take a look at the data a few different ways: Don’t worry, each look at the data is one command. These are useful commands that you can use again and again on future projects.

Knowing the types is important as it will give you an idea of how to better summarize the data you have and the types of transforms you might need to use to prepare the data before you model it.

This includes the mean, the min and max values as well as some percentiles (25th, 50th or media and 75th e.g.

This gives us a much clearer idea of the distribution of the input attributes: We can also create a barplot of the Species class variable to get a graphical representation of the class distribution (generally uninteresting in this case because they’re even).

This confirms what we learned in the last section, that the instances are evenly distributed across the three class: Now we can look at the interactions between the variables.

We can see some clear relationships between the input attributes (trends) and between attributes and the class values (ellipses): We can also look at box and whisker plots of each input variable again, but this time broken down into separate plots for each class.

This will split our dataset into 10 parts, train in 9 and test on 1 and release for all combinations of train-test splits.

We will also repeat the process 3 times for each algorithm with different splits of the data into 10 groups, in an effort to get a more accurate estimate.

This is a ratio of the number of correctly predicted instances in divided by the total number of instances in the dataset multiplied by 100 to give a percentage (e.g.

We reset the random number seed before reach run to ensure that the evaluation of each algorithm is performed using exactly the same data splits.

Let’s build our five models: Caret does support the configuration and tuning of the configuration of each model, but we are not going to cover that in this tutorial.

We can see the accuracy of each classifier and also other metrics like Kappa: We can also create a plot of the model evaluation results and compare the spread and the mean accuracy of each model.

There is a population of accuracy measures for each algorithm because each algorithm was evaluated 10 times (10 fold cross validation).

This gives a nice summary of what was used to train the model and the mean and standard deviation (SD) accuracy achieved, specifically 97.5% accuracy +/- 4%

It is valuable to keep a validation set just in case you made a slip during such as overfitting to the training set or a data leak.

It was a small validation dataset (20%), but this result is within our expected margin of 97% +/-4% suggesting we may have an accurate and a reliably accurate model.

You can learn about the benefits and limitations of various algorithms later, and there are plenty of posts that you can read later to brush up on the steps of a machine learning project and the importance of evaluating accuracy using cross validation.

You discovered that completing a small end-to-end project from loading the data to making predictions is the best way to get familiar with a new platform.

Understanding Support Vector Machine algorithm from examples (along with code)

Note: This article was originally published on Oct 6th, 2015 and updated on Sept 13th, 2017 Mastering machine learning algorithms isn’t a myth at all.

Think of machine learning algorithms as an armory packed with axes, sword, blades, bow, dagger etc. You have various tools, but you ought to learn to use them at the right time.

In this article, I shall guide you through the basics to advanced knowledge of a crucial machine learning algorithm, support vector machines.

However,  it is mostly used in classification problems. In this algorithm, we plot each data item as a point in n-dimensional space (where n is number of features you have) with the value of each feature being the value of a particular coordinate.

In Python, scikit-learn is a widely used library for implementing machine learning algorithms, SVM is also available in scikit-learn library and follow the same structure (Import library, object creation, fitting model and prediction).

The creation of a support vector machine in R and Python follow similar approaches, let’s take a look now at the following code:

Let’s look at the example, where we’ve used linear kernel on two feature of iris data set to classify their class.

Example: Have linear kernel Example: Have rbf kernel Change the kernel type to rbf in below line and look at the impact.

would suggest you to go for linear kernel if you have large number of features (>1000) because it is more likely that the data is linearly separable in high dimensional space.

 I discussed its concept of working, process of implementation in python, the tricks to make the model efficient by tuning its parameters, Pros and Cons, and finally a problem to solve.

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