AI News, Data Science for Beginners video 1: The 5 questions data science answers

Data Science for Beginners video 1: The 5 questions data science answers

Get a quick introduction to data science from Data Science for Beginners in five short videos from a top data scientist.

These videos are basic but useful, whether you're interested in doing data science or you work with data scientists.

Data Science for Beginners is a quick introduction to data science taking about 25 minutes total.

Data Science uses numbers and names (also known as categories or labels) to predict answers to questions.

It might surprise you, but there are only five questions that data science answers: Each one of these questions is answered by a separate family of machine learning methods, called algorithms.

This is called multiclass classification and it's useful when you have several — or several thousand — possible answers.

Your credit card company analyzes your purchase patterns, so that they can alert you to possible fraud.

might be a purchase at a store where you don't normally shop or buying an unusually pricey item.

Common examples of clustering questions are: By understanding how data is organized, you can better understand - and predict - behaviors and events.

Typically, reinforcement learning is a good fit for automated systems that have to make lots of small decisions without human guidance.

Data Science for Beginners video 1: The 5 questions data science answers

Get a quick introduction to data science from Data Science for Beginners in five short videos from a top data scientist.

These videos are basic but useful, whether you're interested in doing data science or you work with data scientists.

Data Science for Beginners is a quick introduction to data science taking about 25 minutes total.

Data Science uses numbers and names (also known as categories or labels) to predict answers to questions.

It might surprise you, but there are only five questions that data science answers: Each one of these questions is answered by a separate family of machine learning methods, called algorithms.

This is called multiclass classification and it's useful when you have several — or several thousand — possible answers.

Your credit card company analyzes your purchase patterns, so that they can alert you to possible fraud.

might be a purchase at a store where you don't normally shop or buying an unusually pricey item.

Common examples of clustering questions are: By understanding how data is organized, you can better understand - and predict - behaviors and events.

Typically, reinforcement learning is a good fit for automated systems that have to make lots of small decisions without human guidance.

Five Questions Data Science Answers

Each ML method (also called an algorithm) takes in data, turns it over, and spits out an answer.

ML algorithms do the part of data science that is the trickiest to explain and the most fun to work with.

Like its name implies, it answers a question that has several (or even many) possible answers: which flavor, which person, which part, which company, which candidate.

When you are looking for a number instead of a class or category, the algorithm family to use is regression.

Out of a thousand units, how many of this model of bearings will survive 10,000 hours of use?

For some questions, especially questions beginning “How many…”, negative answers may have to be re-interpreted as zero and fractional values re-interpreted as the nearest whole number. 

Sometimes questions that look like multi-value classification questions are actually better suited to regression.

For instance, “Which news story is the most interesting to this reader?” appears to ask for a category—a single item from the list of news stories.

However, you can reformulate it to “How interesting is each story on this list to this reader?” and give each article a numerical score.

“Which van in my fleet needs servicing the most?” can be rephrased as “How badly does each van in my fleet need servicing?”

“Which 5% of my customers will leave my business for a competitor in the next year?” can be rephrased as “How likely is each of my customers to leave my business for a competitor in the next year?” 

(In fact, under the hood some algorithms reformulate every binary classification as regression.) This is especially helpful when an example can belong part A and part B, or have a chance of going either way.

As you may have gathered, the families of two-class classification, multi-class classification, anomaly detection, and regression are all closely related.

What they all share is that they are built using a set labeled examples (a process called training), after which they can assign a value or category to unlabeled examples (a process called scoring).

Entirely different sets of data science questions belong in the extended algorithm families of unsupervised and reinforcement learning. 

What makes clustering different from supervised learning is that there is no number or name that tells you what group each point belongs to, what the groups represent, or even how many groups there should be.

If supervised learning is picking out planets from among the stars in the night sky, then clustering is inventing constellations.

Clustering tries to separate out data into natural “clumps,” so that a human analyst can more easily interpret it and explain it to others.

The distance metric can be any measurable quantity, such as difference in IQ, number of shared genetic base pairs, or miles-as-the-crow-flies.

Dimensionality reduction is another way to simplify the data, to make it both easier to communicate, faster to compute with, and easier to store.

A college student’s academic strength is measured in dozens of classes by hundreds of exams and thousands of assignments.

Each assignment says something about how well that student understands the course material, but a full listing of them would be way too much for any recruiter to digest.

For instance, you wouldn’t know it if the student is stronger in math than English, or if she scored better on take-home programming assignments than on in-class quizzes.

If your goal is to summarize, simplify, condense, or distill a collection of data, dimensionality reduction and clustering are your tools of choice.

A regression algorithm might predict that the high temperature will be 98 degrees tomorrow, but it doesn’t decide what to do about it.

A RL algorithm goes the next step and chooses an action, such as pre-refrigerating the upper floors of the office building while the day is still cool.

RL algorithms were originally inspired by how the brains of rats and humans respond to punishment and rewards.

They choose actions, trying very hard to choose the action that will earn the greatest reward.

You have to provide them with a set of possible actions, and they need to get feedback after each action on whether it was good, neutral, or a huge mistake.

Typically RL algorithms are a good fit for automated systems that have to make a lot of small decisions without a human’s guidance.

RL was originally developed to control robots, so anything that moves on its own, from inspection drones to vacuum cleaners, is fair game.

RL usually requires more effort to get working than other algorithm types because it’s so tightly integrated with the rest of the system.

The next and final post will give lots of specific examples of sharp data science questions and the algorithm family best suited to each.

ShuaiW/data-science-question-answer

The purpose of this repo is two fold: The focus is on the knowledge breadth so this is more of a quick reference rather than an in-depth study material.

If you want to learn a specific topic in detail please refer to other content or reach out and I'd love to point you to materials I found useful.

Here are the categorizes: The only advice I can give about resume is to indicate your past data science / machine learning projects in a specific, quantifiable way.

Consider the following two statements: Trained a machine learning system and Designed and deployed a deep learning model to recognize objects using Keras, Tensorflow, and Node.js.

The model has 1/30 model size, 1/3 training time, 1/5 inference time, and 2x faster convergence compared with traditional neural networks (e.g, ResNet) The second is much better because it quantifies your contribution and also highlights specific technologies you used (and therefore have expertise in).

back to top Cross-validation is a technique to evaluate predictive models by partitioning the original sample into a training set to train the model, and a validation set to evaluate it.

For example, a k-fold cross validation divides the data into k folds (or partitions), trains on each k-1 fold, and evaluate on the remaining 1 fold.

So a potential way to address underfitting is to increase the model complexity (e.g., to add higher order coefficients for linear model, increase depth for tree-based methods, add more layers / number of neurons for neural networks etc.) back to top For neural networks back to top To address overfitting, we can use an ensemble method called bagging (bootstrap aggregating), which

Difference from random forest (RF) XGBoost (Extreme Gradient Boosting) XGBoost uses a more regularized model formalization to control overfitting, which gives it better performance back to top A

a set of learnable filters (such as 5 * 5 * 3, width * height * depth).

The math behind LSTM can be pretty complicated, but intuitively LSTM introduce LSTM resembles human memory: it forgets old stuff (old internal state * forget gate) and

back to top The software utility cron is a time-based job scheduler in Unix-like computer operating systems.

People who set up and maintain software environments use cron to schedule jobs (commands or shell scripts) to run periodically at fixed times, dates, or intervals.

It typically automates system maintenance or administration -- though its general-purpose nature makes it useful for things like downloading files from the Internet and downloading email at regular intervals.

41 Essential Machine Learning Interview Questions (with answers)

We’ve traditionally seen machine learning interview questions pop up in several categories.

The third has to do with your general interest in machine learning: you’ll be asked about what’s going on in the industry and how you keep up with the latest machine learning trends.

Finally, there are company or industry-specific questions that test your ability to take your general machine learning knowledge and turn it into actionable points to drive the bottom line forward.

We’ve divided this guide to machine learning interview questions into the categories we mentioned above so that you can more easily get to the information you need when it comes to machine learning interview questions.

This can lead to the model underfitting your data, making it hard for it to have high predictive accuracy and for you to generalize your knowledge from the training set to the test set.

The bias-variance decomposition essentially decomposes the learning error from any algorithm by adding the bias, the variance and a bit of irreducible error due to noise in the underlying dataset.

For example, in order to do classification (a supervised learning task), you’ll need to first label the data you’ll use to train the model to classify data into your labeled groups.

K-means clustering requires only a set of unlabeled points and a threshold: the algorithm will take unlabeled points and gradually learn how to cluster them into groups by computing the mean of the distance between different points.

It’s often used as a proxy for the trade-off between the sensitivity of the model (true positives) vs the fall-out or the probability it will trigger a false alarm (false positives).

More reading: Precision and recall (Wikipedia) Recall is also known as the true positive rate: the amount of positives your model claims compared to the actual number of positives there are throughout the data.

Precision is also known as the positive predictive value, and it is a measure of the amount of accurate positives your model claims compared to the number of positives it actually claims.

It can be easier to think of recall and precision in the context of a case where you’ve predicted that there were 10 apples and 5 oranges in a case of 10 apples.

Mathematically, it’s expressed as the true positive rate of a condition sample divided by the sum of the false positive rate of the population and the true positive rate of a condition.

Say you had a 60% chance of actually having the flu after a flu test, but out of people who had the flu, the test will be false 50% of the time, and the overall population only has a 5% chance of having the flu.

(Quora) Despite its practical applications, especially in text mining, Naive Bayes is considered “Naive” because it makes an assumption that is virtually impossible to see in real-life data: the conditional probability is calculated as the pure product of the individual probabilities of components.

clever way to think about this is to think of Type I error as telling a man he is pregnant, while Type II error means you tell a pregnant woman she isn’t carrying a baby.

More reading: Deep learning (Wikipedia) Deep learning is a subset of machine learning that is concerned with neural networks: how to use backpropagation and certain principles from neuroscience to more accurately model large sets of unlabelled or semi-structured data.

More reading: Using k-fold cross-validation for time-series model selection (CrossValidated) Instead of using standard k-folds cross-validation, you have to pay attention to the fact that a time series is not randomly distributed data —

More reading: Pruning (decision trees) Pruning is what happens in decision trees when branches that have weak predictive power are removed in order to reduce the complexity of the model and increase the predictive accuracy of a decision tree model.

For example, if you wanted to detect fraud in a massive dataset with a sample of millions, a more accurate model would most likely predict no fraud at all if only a vast minority of cases were fraud.

More reading: Regression vs Classification (Math StackExchange) Classification produces discrete values and dataset to strict categories, while regression gives you continuous results that allow you to better distinguish differences between individual points.

You would use classification over regression if you wanted your results to reflect the belongingness of data points in your dataset to certain explicit categories (ex: If you wanted to know whether a name was male or female rather than just how correlated they were with male and female names.) Q21- Name an example where ensemble techniques might be useful.

They typically reduce overfitting in models and make the model more robust (unlikely to be influenced by small changes in the training data).  You could list some examples of ensemble methods, from bagging to boosting to a “bucket of models” method and demonstrate how they could increase predictive power.

(Quora) This is a simple restatement of a fundamental problem in machine learning: the possibility of overfitting training data and carrying the noise of that data through to the test set, thereby providing inaccurate generalizations.

There are three main methods to avoid overfitting: 1- Keep the model simpler: reduce variance by taking into account fewer variables and parameters, thereby removing some of the noise in the training data.

More reading: How to Evaluate Machine Learning Algorithms (Machine Learning Mastery) You would first split the dataset into training and test sets, or perhaps use cross-validation techniques to further segment the dataset into composite sets of training and test sets within the data.

More reading: Kernel method (Wikipedia) The Kernel trick involves kernel functions that can enable in higher-dimension spaces without explicitly calculating the coordinates of points within that dimension: instead, kernel functions compute the inner products between the images of all pairs of data in a feature space.

This allows them the very useful attribute of calculating the coordinates of higher dimensions while being computationally cheaper than the explicit calculation of said coordinates. Many algorithms can be expressed in terms of inner products.

More reading: Writing pseudocode for parallel programming (Stack Overflow) This kind of question demonstrates your ability to think in parallelism and how you could handle concurrency in programming implementations dealing with big data.

For example, if you were interviewing for music-streaming startup Spotify, you could remark that your skills at developing a better recommendation model would increase user retention, which would then increase revenue in the long run.

The startup metrics Slideshare linked above will help you understand exactly what performance indicators are important for startups and tech companies as they think about revenue and growth.

Your interviewer is trying to gauge if you’d be a valuable member of their team and whether you grasp the nuances of why certain things are set the way they are in the company’s data process based on company- or industry-specific conditions.

This overview of deep learning in Nature by the scions of deep learning themselves (from Hinton to Bengio to LeCun) can be a good reference paper and an overview of what’s happening in deep learning —

More reading: Mastering the game of Go with deep neural networks and tree search (Nature) AlphaGo beating Lee Sidol, the best human player at Go, in a best-of-five series was a truly seminal event in the history of machine learning and deep learning.

The Nature paper above describes how this was accomplished with “Monte-Carlo tree search with deep neural networks that have been trained by supervised learning, from human expert games, and by reinforcement learning from games of self-play.” Want more?  Brush up your skills with our free machine learning course.

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