AI News, hangtwenty/dive-into-machine-learning


(You can learn by screencast instead.) Now, follow along with this brief exercise (10 minutes): An introduction to machine learning with scikit-learn.

encourage you to look at the scikit-learn homepage and spend about 5 minutes looking over the names of the strategies (Classification, Regression, etc.), and their applications.

The whole paper is packed with value, but I want to call out two points: When you work on a real Machine Learning problem, you should focus your efforts on your domain knowledge and data before optimizing your choice of algorithms.

It's helpful if you decide on a pet project to play around with, as you go, so you have a way to apply your knowledge.

(Machine Learning, Data Science, and related topics.) Start with the support forums and chats related to the course(s) you're taking.

(Please submit a Pull Request to add other useful cheat sheets.) I'm not repeating the materials mentioned above, but here are some other Data Science resources: From the 'Bayesian Machine Learning' overview on Metacademy: ...

Bayesian ideas have had a big impact in machine learning in the past 20 years or so because of the flexibility they provide in building structured models of real world phenomena.

Algorithmic advances and increasing computational resources have made it possible to fit rich, highly structured models which were previously considered intractable.

Here is the abstract of Machine Learning: The High-Interest Credit Card of Technical Debt: Machine learning offers a fantastically powerful toolkit for building complex systems quickly.

This paper argues that it is dangerous to think of these quick wins as coming for free.

Using the framework of technical debt, we note that it is remarkably easy to incur massive ongoing maintenance costs at the system level when applying machine learning.

The goal of this paper is highlight several machine learning specific risk factors and design patterns to be avoided or refactored where possible.

These include boundary erosion, entanglement, hidden feedback loops, undeclared consumers, data dependencies, changes in the external world, and a variety of system-level anti-patterns.

(Also please don't be evil.) This guide can't tell you how you'll know you've 'made it' into Machine Learning competence ...

Re-phrasing this, it fits with the scientific method: formulate a question (or problem statement), create a hypothesis, gather data, analyze the data, and communicate results.

(You should watch this video about the scientific method in data science, and/or read this article.) How can you come up with interesting questions?

This advice, to do practice studies and learn from peer review, is based on a conversation with Dr. Randal S.

think the best advice is to tell people to always present their methods clearly and to avoid over-interpreting their results.

Part of being an expert is knowing that there's rarely a clear answer, especially when you're working with real data.

As you repeat this process, your practice studies will become more scientific, interesting, and focused.

When I read the feedback on my Pull Requests, first I repeat to myself, 'I will not get defensive, I will not get defensive, I will not get defensive.'

Whenever you apply Machine Learning to solve a problem, you are going to be working in some specific problem domain.

If you aren’t aligned with a human need, you’re just going to build a very powerful system to address a very small—or perhaps nonexistent—problem.

Then, if you you know a coworker or friend who works in UX, take them out for coffee or lunch and pick their brain.

Pull requests welcome for all parts of this guide, including this section!) By no means will that make you an expert in UX, but maybe it'll help you know if/when to reach out for help (hint: almost always — UX is really tricky and you should work with experts whenever you can!).

If you want to explore this space more deeply, there is a lot of reading material in the below links: In early editions of this guide, there was no specific 'Deep Learning' section.

I also know that if you become an expert in traditional Machine Learning, you'll be capable of moving onto advanced subjects like Deep Learning, whether or not I've put that in this guide.

Even if 'buy' instead of 'build,' you may want to buy from vendors who use known good stacks.

If you are working with data-intensive applications at all, I'll recommend this book: Lastly, here are some other useful links regarding Big Data and ML.

Ten Machine Learning Algorithms You Should Know to Become a Data Scientist

That said, no one can deny the fact that as practicing Data Scientists, we will have to know basics of some common machine learning algorithms, which would help us engage with a new-domain problem we come across.

Covariance Matrix of data points is analyzed here to understand what dimensions(mostly)/ data points (sometimes) are more important (ie have high variance amongst themselves, but low covariance with others).

As is obvious, use this algorithm to fit simple curves / regression Least Squares can get confused with outliers, spurious fields and noise in data.

As is obvious from the name, you can use this algorithm to create K clusters in dataset Logistic Regression is constrained Linear Regression with a nonlinearity (sigmoid function is used mostly or you can use tanh too) application after weights are applied, hence restricting the outputs close to +/- classes (which is 1 and 0 in case of sigmoid). SVMs are linear models like Linear/ Logistic Regression, the difference is that they have different margin-based loss function (The derivation of Support Vectors is one of the most beautiful mathematical results I have seen along with eigenvalue calculation).

FFNNs can be used to train a classifier or extract features as autoencoders Almost any state of the art Vision based Machine Learning result in the world today has been achieved using Convolutional Neural Networks. RNNs model sequences by applying the same set of weights recursively on the aggregator state at a time t and input at a time t (Given a sequence has inputs at times 0..t..T, and have a hidden state at each time t which is output from t-1 step of RNN).

Use RNNs for any sequence modelling task specially text classification, machine translation, language modelling (Many cool NLP research papers from Google are here) CRFs are probably the most frequently used models from the family of Probabilitic Graphical Models (PGMs).

Before Neural Machine Translation systems came in CRFs were the state of the art and in many sequence tagging tasks with small datasets, they will still learn better than RNNs which require a larger amount of data to generalize.

Earlier versions like CART trees were once used for simple data, but with bigger and larger dataset, the bias-variance tradeoff needs to solved with better algorithms.

The two common decision trees algorithms used nowadays are Random Forests (which build different classifiers on a random subset of attributes and combine them for output) and Boosting Trees (which train a cascade of trees one on top of others, correcting the mistakes of ones below them).

Decision Trees can be used to classify datapoints (and even regression) If you are still wondering how can any of the above methods solve tasks like defeating Go world champion like DeepMind did, they cannot.

To learn strategy to solve a multi-step problem like winning a game of chess or playing Atari console, we need to let an agent-free in the world and learn from the rewards/penalties it faces.

Python Machine Learning: Scikit-Learn Tutorial

If you’re more interested in an R tutorial, take a look at our Machine Learning with R for Beginners tutorial.

For now, you should warm up, not worry about finding any data by yourself and just load in the digits data set that comes with a Python library, called scikit-learn.

This scikit contains modules specifically for machine learning and data mining, which explains the second component of the library name.

Note that the datasets module contains other methods to load and fetch popular reference datasets, and you can also count on this module in case you need artificial data generators.

Note that if you download the data like this, the data is already split up in a training and a test set, indicated by the extensions .tra and .tes.

When it comes to scikit-learn, you don’t immediately have this information readily available, but in the case where you import data from another source, there's usually a data description present, which will already be a sufficient amount of information to gather some insights into your data.

The init indicates the method for initialization and even though it defaults to ‘k-means++’, you see it explicitly coming back in the code.

This number not only indicates the number of clusters or groups you want your data to form, but also the number of centroids to generate.

That is, that the initial set of cluster centers that you give up can have a big effect on the clusters that are eventually found?

Usually, you try to deal with this effect by trying several initial sets in multiple runs and by selecting the set of clusters with the minimum sum of the squared errors (SSE).

Note again that you don’t want to insert the test labels when you fit the model to your data: these will be used to see if your model is good at predicting the actual classes of your instances!

The next step is to predict the labels of the test set: In the code chunk above, you predict the values for the test set, which contains 450 samples.

Tip: run the code from above again, but use the PCA reduction method instead of the Isomap to study the effect of reduction methods yourself.

Machine Learning

Supervised learning algorithms are trained using labeled examples, such as an input where the desired output is known.

The learning algorithm receives a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors.

Through methods like classification, regression, prediction and gradient boosting, supervised learning uses patterns to predict the values of the label on additional unlabeled data.

Popular techniques include self-organizing maps, nearest-neighbor mapping, k-means clustering and singular value decomposition.

Apache Spark Tutorial: ML with PySpark

A block group is the smallest geographical unit for which the U.S. Census Bureau publishes sample data (a block group typically has a population of 600 to 3,000 people).

These spatial data contain 20,640 observations on housing prices with 9 economic variables: What’s more, you also learn that all the block groups have zero entries for the independent and dependent variables have been excluded from the data.

You already gathered a lot of information by just looking at the web page where you found the data set, but it’s always better to get hands-on and inspect your data with the help of Spark with Python, in this case.

You have to push Spark to work for you, so let’s use the collect() method to look at the header: The collect() method brings the entire RDD to a single machine, and you’ll get to see the following result: Tip: be careful when using collect()!

You learn that the order of the variables is the same as the one that you saw above in the presentation of the data set, and you also learn that all columns should have continuous values.

You’ll get the following result: Alternatively, you can also use the following functions to inspect your data: If you’re used to working with Pandas or data frames in R, you’ll have probably also expected to see a header, but there is none.

To recapitulate, you’ll switch to DataFrames now to use high-level expressions, to perform SQL queries to explore your data further and to gain columnar access.

To make this more visual, consider this first line: The lambda function says that you’re going to construct a row in a SchemaRDD and that the element at index 0 will have the name “longitude”, and so on.

Now that you have your DataFrame df, you can inspect it with the methods that you have also used before, namely first() and take(), but also with head() and show(): You’ll immediately see that this looks much different from the RDD that you were working with before: Tip: use df.columns to return the columns of your DataFrame.

Intuitively, you could go for a solution like the following, where you declare that each column of the DataFrame df should be cast to a FloatType(): But these repeated calls are quite obscure, error-proof and don’t really look nice.

Let’s start small and just select two columns from df of which you only want to see 10 rows: This query gives you the following result: You can also make your queries more complex, as you see in the following example: Which gives you the following result: Besides querying, you can also choose to describe your data and get some summary statistics.

Teach Yourself Data Science: the learning path I used to get an analytics job at

It’s easier to motivate yourself to learn Python and machine learning when you’re fascinated by the practical applications.

had about a month before the class began, so I took as many classes around data science and machine learning as possible.

it covers convolutional neural networks (what we use for image or facial recognition software) extensively, which I read would be incredibly helpful for the self-driving car Nanodegree.

If you’re interested at all in using machine learning with images or video, you won’t find much better than this course.

After diving intensely into machine learning for a few months, it was helpful to take a step back and reinforce my understanding of practical analytics and data science principles.

While touching upon machine learning, it completely covers principles in analytics, data science, and statistics, particularly around different data mining techniques and practical scenarios to deploy them.

Of course, if you’re interested in pursuing a career in analytics or data science, you should always be honing old skills or adding new skills into your toolkit.

Even if you don’t have access to high-quality data at your company, there are plenty of open source datasets that you can play around and practice with.

Many of these groups have free tutorial or study sessions, and you’ll meet plenty of insanely smart people who can provide tips and tricks to accelerate your learnings.

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